Microsegment.ru
  • Главная страница
  • О проекте
  • Портфолио
  • Блог

Регрессионный анализ на градиентном бустинге для прогнозирования стоимости автомобиля

Сервис по продаже автомобилей с пробегом «Не бит, не крашен» разрабатывает приложение для привлечения новых клиентов. В нём можно быстро узнать рыночную стоимость своего автомобиля. В вашем распоряжении исторические данные: технические характеристики, комплектации и цены автомобилей. Вам нужно построить модель для определения стоимости.

Заказчику важны:

  • качество предсказания;
  • скорость предсказания;
  • время обучения.

Инструкция по выполнению проекта¶

Чтобы усилить исследование, не ограничивайтесь градиентным бустингом. Попробуйте более простые модели — иногда они работают лучше. Эти редкие случаи легко пропустить, если всегда применять только бустинг.

Поэкспериментируйте и сравните характеристики моделей: время обучения, время предсказания, точность результата.

Основные шаги:

  1. Загрузите данные, путь к файлу: /datasets/autos.csv.
  2. Изучите данные. Заполните пропущенные значения и обработайте аномалии в столбцах. Если среди признаков имеются неинформативные, удалите их.
  3. Подготовьте выборки для обучения моделей.
  4. Обучите разные модели, одна из которых — LightGBM, как минимум одна — не бустинг. Для каждой модели попробуйте разные гиперпараметры.
  5. Проанализируйте время обучения, время предсказания и качество моделей.
  6. Опираясь на критерии заказчика, выберете лучшую модель, проверьте её качество на тестовой выборке.

Примечания:

  • Для оценки качества моделей применяйте метрику RMSE.
  • Значение метрики RMSE должно быть меньше 2500.
  • Самостоятельно освойте библиотеку LightGBM и её средствами постройте модели градиентного бустинга.
  • Время выполнения ячейки кода Jupyter Notebook можно получить специальной командой. Найдите её.
  • Модель градиентного бустинга может долго обучаться, поэтому измените у неё только два-три параметра.
  • Если перестанет работать Jupyter Notebook, удалите лишние переменные оператором del.

Описание данных¶

Данные находятся в файле /datasets/autos.csv.

Признаки

  1. DateCrawled — дата скачивания анкеты из базы
  2. VehicleType — тип автомобильного кузова
  3. RegistrationYear — год регистрации автомобиля
  4. Gearbox — тип коробки передач
  5. Power — мощность (л. с.)
  6. Model — модель автомобиля
  7. Kilometer — пробег (км)
  8. RegistrationMonth — месяц регистрации автомобиля
  9. FuelType — тип топлива
  10. Brand — марка автомобиля
  11. Repaired — была машина в ремонте или нет
  12. DateCreated — дата создания анкеты
  13. NumberOfPictures — количество фотографий автомобиля
  14. PostalCode — почтовый индекс владельца анкеты (пользователя)
  15. LastSeen — дата последней активности пользователя

Целевой признак

  1. Price — цена (евро)

Содержание

    • 0.1  Инструкция по выполнению проекта
    • 0.2  Описание данных
  • 1  Подготовка данных
    • 1.1  Настройка тетради
    • 1.2  Загрузка и изучение данных
    • 1.3  Предобработка данных
    • 1.4  Проверка результатов предобработкаи данных
  • 2  Обучение моделей
    • 2.1  Полезные функции подготовки данных и подбора моделей и их параметров
    • 2.2  Функции моделей
    • 2.3  Применение функций
    • 2.4  Выбор лучшей модели
  • 3  Анализ моделей
  • 4  Выводы проекта
  • 5  Чек-лист проверки

Подготовка данных¶

Настройка тетради¶

In [1]:
# Базовые библиотеки
import pandas as pd # Датафреймы
import numpy as np # Математика для массивов
from math import factorial # Факториалы
from scipy import stats as st # Статистика
import os # Библиотека для оптимизации чтения данных из файла
import time # Расчет времени выполнения функций

# Pipeline (пайплайн)
from sklearn.pipeline import(
    Pipeline, # Pipeline с ручным вводом названий шагов.
    make_pipeline # Pipeline с автоматическим названием шагов.
)
# Функция для поддержки экспериментальной функции HavingGridSearchSV
from sklearn.experimental import enable_halving_search_cv
# Ускоренная автоматизация поиска лучших моделей и их параметров
from sklearn.model_selection import HalvingGridSearchCV
# Ускоренная автоматизация рандомного поиска лучших моделей и их параметров
from sklearn.model_selection import HalvingRandomSearchCV

# Автоматизация раздельного декодирования признаков
from sklearn.compose import(
    make_column_selector, 
    make_column_transformer, 
    ColumnTransformer
)

# Обработка данных для машинного обучения
# Стандартизация данных
import re
#! pip install sklearn.preprocessing 
from sklearn.preprocessing import(
    OneHotEncoder, # Создание отдельных столбцов для каждого категориального значения, drop='first' (удаление первого столбца против dummy-ловушки), sparse=False (?)
    OrdinalEncoder, # Кодирование порядковых категориальных признаков
    #TargetEncoder, # Кодирование категорий на основе таргетов (ошибка, модуль не найден) 
    LabelEncoder, 
    StandardScaler, 
    MinMaxScaler
)
# Кодирование категорий на основе таргетов 
!pip install -U category_encoders
from category_encoders.target_encoder import TargetEncoder
# Другие функции предобработки данных
from sklearn.impute import KNNImputer # Заполнение пропусков в данных методом k-блжиайших соседей.
from sklearn.utils import shuffle # Перемешивание данных для уравновешивания их в разных выборках
from statsmodels.stats.outliers_influence import variance_inflation_factor # Коэффициент инфляции дисперсии (5 и более - признак коррелирует со всеми остальными, его можно удалить и выразить через другие признаки)
from sklearn.model_selection import(
    GridSearchCV, # Поиск гиперпараметров по сетке (GridSearch)
    train_test_split, # Разделение выборок с целевыми и нецелевыми признаками на обучающую и тестовую
    validation_curve, 
    StratifiedKFold, # Кроссвалидация с указанием количества фолдов (частей, на которые будет разбита обучающая выборка, одна из которых будет участвовать в валидации)
    KFold, # Кроссвалидация 
    cross_val_score # Оценка качества модели на кроссвалидации
)

# Различные модели машинного обучения (в данном проекте требуется регрессия)
# (есть разбор на https://russianblogs.com/article/83691573909/)
# Линейная модель
from sklearn.linear_model import(
    #LogisticRegression, # Линейная классификация
    LinearRegression, # Линейная регрессия
    Ridge , # Линейная регрессия. "Хребтовая" регрессия (метод наименьших квадратов)
    BayesianRidge , # Линейная регрессия. Байесовская "хребтовая" регрессия (максимизации предельного логарифмического правдоподобия)
    SGDRegressor # Линейная регрессия. SGD - Стохастический градиентный спуск (минимизирует регуляризованные эмпирические потери за счет стохастического градиентного спуска)
)
# Решающее дерево
from sklearn.tree import(
    #DecisionTreeClassifier, # Решающее дерево. Классификация
    DecisionTreeRegressor # Решающее дерево. Регрессия
)
# Случайный лес
from sklearn.ensemble import(
    #RandomForestClassifier, # Случайный лес. Классификация
    RandomForestRegressor # Случайный лес. Регрессия
)
# Машина опорных векторов
from sklearn.svm import(
    SVR # # Линейная модель. Регрессия с использованием опорных векторов
)
# Нейронная сеть
from sklearn.neural_network import(
    MLPRegressor # Нейронная сеть. Регрессия
)
# CatBoost (made in Yandex)
from catboost import(
    CatBoostRegressor # CatBoost (Яндекс). Регрессия
)
# LightGBM
from lightgbm import(
    LGBMRegressor # LightGBM. Регрессия
)

# Метрики (Показатели качества моделей)
from sklearn.metrics import(
    # Метрики для моделей регрессии
    mean_absolute_error, # MAE, Средняя абсолютная ошибка (не чувствительная к выбросам)
    mean_absolute_percentage_error, # MAPE, Средняя абсолютная ошибка в % (универсальная в %)
    mean_squared_error, # MSE, Средняя квадратичная ошибка (дисперсия, чувствительная к выбросам), RMSE (сигма) = mean_squared_error(test_y, preds, squared=False)
    r2_score, # R^2, Коэффициент детерминации (универсальная в %, чувствительная к выбросам, может быть отрицательной и возвращать NaN)
    
    # Другое
    make_scorer, # Функция для использования собственных функций в параметре scoring функции HalvingGridSearchCV
    ConfusionMatrixDisplay
)

# Визуализация графиков
import seaborn as sns
import matplotlib
%matplotlib inline
from matplotlib import pyplot as plt
from matplotlib import rcParams, rcParamsDefault
from pandas.plotting import scatter_matrix

# Для поиска совпадений 
# в названиях населённых пунктов
from fuzzywuzzy import fuzz
from fuzzywuzzy import process

# Улучшенная функция 
# определения корреляции
# (возвращает сообщение о том, 
# что модуль не найден) 
!pip3 install phik
import phik 

Collecting category_encoders
  Downloading category_encoders-2.6.2-py2.py3-none-any.whl (81 kB)
     |████████████████████████████████| 81 kB 1.1 MB/s eta 0:00:011
Requirement already satisfied: scikit-learn>=0.20.0 in /opt/conda/lib/python3.9/site-packages (from category_encoders) (0.24.1)
Requirement already satisfied: scipy>=1.0.0 in /opt/conda/lib/python3.9/site-packages (from category_encoders) (1.9.1)
Requirement already satisfied: patsy>=0.5.1 in /opt/conda/lib/python3.9/site-packages (from category_encoders) (0.5.2)
Requirement already satisfied: statsmodels>=0.9.0 in /opt/conda/lib/python3.9/site-packages (from category_encoders) (0.13.2)
Requirement already satisfied: pandas>=1.0.5 in /opt/conda/lib/python3.9/site-packages (from category_encoders) (1.2.4)
Requirement already satisfied: numpy>=1.14.0 in /opt/conda/lib/python3.9/site-packages (from category_encoders) (1.21.1)
Requirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.9/site-packages (from pandas>=1.0.5->category_encoders) (2.8.1)
Requirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.9/site-packages (from pandas>=1.0.5->category_encoders) (2021.1)
Requirement already satisfied: six in /opt/conda/lib/python3.9/site-packages (from patsy>=0.5.1->category_encoders) (1.16.0)
Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.9/site-packages (from scikit-learn>=0.20.0->category_encoders) (3.1.0)
Requirement already satisfied: joblib>=0.11 in /opt/conda/lib/python3.9/site-packages (from scikit-learn>=0.20.0->category_encoders) (1.1.0)
Requirement already satisfied: packaging>=21.3 in /opt/conda/lib/python3.9/site-packages (from statsmodels>=0.9.0->category_encoders) (21.3)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.9/site-packages (from packaging>=21.3->statsmodels>=0.9.0->category_encoders) (2.4.7)
Installing collected packages: category-encoders
Successfully installed category-encoders-2.6.2

/opt/conda/lib/python3.9/site-packages/fuzzywuzzy/fuzz.py:11: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning
  warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')

Collecting phik
  Downloading phik-0.12.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (679 kB)
     |████████████████████████████████| 679 kB 953 kB/s eta 0:00:01
Requirement already satisfied: joblib>=0.14.1 in /opt/conda/lib/python3.9/site-packages (from phik) (1.1.0)
Requirement already satisfied: numpy>=1.18.0 in /opt/conda/lib/python3.9/site-packages (from phik) (1.21.1)
Requirement already satisfied: pandas>=0.25.1 in /opt/conda/lib/python3.9/site-packages (from phik) (1.2.4)
Requirement already satisfied: matplotlib>=2.2.3 in /opt/conda/lib/python3.9/site-packages (from phik) (3.3.4)
Requirement already satisfied: scipy>=1.5.2 in /opt/conda/lib/python3.9/site-packages (from phik) (1.9.1)
Requirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.9/site-packages (from matplotlib>=2.2.3->phik) (1.4.4)
Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /opt/conda/lib/python3.9/site-packages (from matplotlib>=2.2.3->phik) (2.4.7)
Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.9/site-packages (from matplotlib>=2.2.3->phik) (0.11.0)
Requirement already satisfied: python-dateutil>=2.1 in /opt/conda/lib/python3.9/site-packages (from matplotlib>=2.2.3->phik) (2.8.1)
Requirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.9/site-packages (from matplotlib>=2.2.3->phik) (8.4.0)
Requirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.9/site-packages (from pandas>=0.25.1->phik) (2021.1)
Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.9/site-packages (from python-dateutil>=2.1->matplotlib>=2.2.3->phik) (1.16.0)
Installing collected packages: phik
Successfully installed phik-0.12.3
In [2]:
# Отображение всех столбцов таблицы
pd.set_option('display.max_columns', None)
# Обязательно для нормального отображения графиков plt
rcParams['figure.figsize'] = 10, 6
%config InlineBackend.figure_format = 'svg'
# Дополнительно и не обязательно для декорирования графиков
factor = .8
default_dpi = rcParamsDefault['figure.dpi']
rcParams['figure.dpi'] = default_dpi * factor

# Глобальная переменная 
# для функций со случайными значениями
STATE = 42

Загрузка и изучение данных¶

In [3]:
# Загрузка данных
def read_csv_file(path1, path2):
    if os.path.exists(path1):
        data = pd.read_csv(path1)
    elif os.path.exists(path2):
        data = pd.read_csv(path2)
    else:
        print('Файл не найден')
    return data

data = read_csv_file(
    '/datasets/autos.csv', 
    'datasets/autos.csv'
)
In [4]:
# Первичный анализ данных
print(data.info()) 
data.head(10) 

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 354369 entries, 0 to 354368
Data columns (total 16 columns):
 #   Column             Non-Null Count   Dtype 
---  ------             --------------   ----- 
 0   DateCrawled        354369 non-null  object
 1   Price              354369 non-null  int64 
 2   VehicleType        316879 non-null  object
 3   RegistrationYear   354369 non-null  int64 
 4   Gearbox            334536 non-null  object
 5   Power              354369 non-null  int64 
 6   Model              334664 non-null  object
 7   Kilometer          354369 non-null  int64 
 8   RegistrationMonth  354369 non-null  int64 
 9   FuelType           321474 non-null  object
 10  Brand              354369 non-null  object
 11  Repaired           283215 non-null  object
 12  DateCreated        354369 non-null  object
 13  NumberOfPictures   354369 non-null  int64 
 14  PostalCode         354369 non-null  int64 
 15  LastSeen           354369 non-null  object
dtypes: int64(7), object(9)
memory usage: 43.3+ MB
None
Out[4]:
DateCrawled Price VehicleType RegistrationYear Gearbox Power Model Kilometer RegistrationMonth FuelType Brand Repaired DateCreated NumberOfPictures PostalCode LastSeen
0 2016-03-24 11:52:17 480 NaN 1993 manual 0 golf 150000 0 petrol volkswagen NaN 2016-03-24 00:00:00 0 70435 2016-04-07 03:16:57
1 2016-03-24 10:58:45 18300 coupe 2011 manual 190 NaN 125000 5 gasoline audi yes 2016-03-24 00:00:00 0 66954 2016-04-07 01:46:50
2 2016-03-14 12:52:21 9800 suv 2004 auto 163 grand 125000 8 gasoline jeep NaN 2016-03-14 00:00:00 0 90480 2016-04-05 12:47:46
3 2016-03-17 16:54:04 1500 small 2001 manual 75 golf 150000 6 petrol volkswagen no 2016-03-17 00:00:00 0 91074 2016-03-17 17:40:17
4 2016-03-31 17:25:20 3600 small 2008 manual 69 fabia 90000 7 gasoline skoda no 2016-03-31 00:00:00 0 60437 2016-04-06 10:17:21
5 2016-04-04 17:36:23 650 sedan 1995 manual 102 3er 150000 10 petrol bmw yes 2016-04-04 00:00:00 0 33775 2016-04-06 19:17:07
6 2016-04-01 20:48:51 2200 convertible 2004 manual 109 2_reihe 150000 8 petrol peugeot no 2016-04-01 00:00:00 0 67112 2016-04-05 18:18:39
7 2016-03-21 18:54:38 0 sedan 1980 manual 50 other 40000 7 petrol volkswagen no 2016-03-21 00:00:00 0 19348 2016-03-25 16:47:58
8 2016-04-04 23:42:13 14500 bus 2014 manual 125 c_max 30000 8 petrol ford NaN 2016-04-04 00:00:00 0 94505 2016-04-04 23:42:13
9 2016-03-17 10:53:50 999 small 1998 manual 101 golf 150000 0 NaN volkswagen NaN 2016-03-17 00:00:00 0 27472 2016-03-31 17:17:06
In [5]:
# Анализ значений датафрейма
data.hist() 
plt.subplots_adjust(wspace=.4, hspace=.5)

data.describe()
Out[5]:
Price RegistrationYear Power Kilometer RegistrationMonth NumberOfPictures PostalCode
count 354369.000000 354369.000000 354369.000000 354369.000000 354369.000000 354369.0 354369.000000
mean 4416.656776 2004.234448 110.094337 128211.172535 5.714645 0.0 50508.689087
std 4514.158514 90.227958 189.850405 37905.341530 3.726421 0.0 25783.096248
min 0.000000 1000.000000 0.000000 5000.000000 0.000000 0.0 1067.000000
25% 1050.000000 1999.000000 69.000000 125000.000000 3.000000 0.0 30165.000000
50% 2700.000000 2003.000000 105.000000 150000.000000 6.000000 0.0 49413.000000
75% 6400.000000 2008.000000 143.000000 150000.000000 9.000000 0.0 71083.000000
max 20000.000000 9999.000000 20000.000000 150000.000000 12.000000 0.0 99998.000000
2023-08-24T22:28:13.126563 image/svg+xml Matplotlib v3.3.4, https://matplotlib.org/
In [6]:
# Подсчёт пропусков
data_shape = data.shape[0]
print('Всего объектов:', data_shape) 
print() 
print('Количество объектов с пропусками в признаках:') 
for i in data.columns:
    if data_shape - data[i].loc[data[i].notna()].shape[0] > 0:
        _a = data_shape - data[i].loc[data[i].notna()].shape[0]
        _b = int((1 - data[i].loc[data[i].notna()].shape[0] / data_shape) * 100) 
        _c = data[i].dtype
        print(f'{i} ({_c})\t= {_a} ({_b}%)') 

Всего объектов: 354369

Количество объектов с пропусками в признаках:
VehicleType (object)	= 37490 (10%)
Gearbox (object)	= 19833 (5%)
Model (object)	= 19705 (5%)
FuelType (object)	= 32895 (9%)
Repaired (object)	= 71154 (20%)
In [7]:
# Анализ значений атрибута "RegistrationYear" 
print('Уникальные значения атрибута "RegistrationYear":') 
print(np.sort(data['RegistrationYear'].unique())) 
print() 
print('Количество значений атрибута "RegistrationYear", которые меньше 1990 и больше 2023:',  
      data.loc[
          (data['RegistrationYear'] < 1900) |
          (data['RegistrationYear'] > 2023), 
          'RegistrationYear'
      ].count(), 
      'это', 
      (data.loc[
          (data['RegistrationYear'] < 1900) |
          (data['RegistrationYear'] > 2023), 
          'RegistrationYear'
      ].count() / data_shape) * 100, '%' 
     ) 

Уникальные значения атрибута "RegistrationYear":
[1000 1001 1039 1111 1200 1234 1253 1255 1300 1400 1500 1600 1602 1688
 1800 1910 1915 1919 1920 1923 1925 1927 1928 1929 1930 1931 1932 1933
 1934 1935 1936 1937 1938 1940 1941 1942 1943 1944 1945 1946 1947 1948
 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962
 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976
 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990
 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
 2019 2066 2200 2222 2290 2500 2800 2900 3000 3200 3500 3700 3800 4000
 4100 4500 4800 5000 5300 5555 5600 5900 5911 6000 6500 7000 7100 7500
 7800 8000 8200 8455 8500 8888 9000 9229 9450 9996 9999]

Количество значений атрибута "RegistrationYear", которые меньше 1990 и больше 2023: 171 это 0.048254785266205566 %
In [8]:
# Анализ минимальна значений атрибута "Power" 
string = f"{data.loc[data['Power'] < .75, 'Power'].count()} объектов имеют мощность двигателя меньше 0.75 л.с. "
string += f"Это {data.loc[data['Power'] < .75, 'Power'].count() / data_shape * 100}% от всего количества объектов. " 
print(string, '\n')

print('Примеры подобных объектов:') 
data.loc[data['Power'] < .75].head() 

print() 

40225 объектов имеют мощность двигателя меньше 0.75 л.с. Это 11.351162206626427% от всего количества объектов.  

Примеры подобных объектов:
In [9]:
# Анализ максимальная значений атрибута "Power" 
string = f"{data.loc[data['Power'] > 5000, 'Power'].count()} объектов имеют мощность двигателя больше 5000 л.с. "
string += f"Это {data.loc[data['Power'] > 5000, 'Power'].count() / data_shape * 100}% от всего количества объектов. "
print(string, '\n')

print('Примеры подобных объектов:') 
data.loc[data['Power'] > 5000].head() 

82 объектов имеют мощность двигателя больше 5000 л.с. Это 0.023139721589642434% от всего количества объектов.  

Примеры подобных объектов:
Out[9]:
DateCrawled Price VehicleType RegistrationYear Gearbox Power Model Kilometer RegistrationMonth FuelType Brand Repaired DateCreated NumberOfPictures PostalCode LastSeen
7661 2016-04-02 19:25:25 1499 small 1999 manual 7515 lupo 150000 4 petrol volkswagen NaN 2016-04-02 00:00:00 0 65830 2016-04-06 11:46:49
11039 2016-03-25 19:55:32 0 sedan 1998 manual 10317 other 150000 8 petrol fiat no 2016-03-25 00:00:00 0 57520 2016-04-01 19:16:33
25232 2016-03-28 19:57:39 10900 bus 2009 manual 10520 caddy 150000 6 gasoline volkswagen no 2016-03-28 00:00:00 0 36272 2016-04-07 02:47:02
33952 2016-03-09 11:37:03 3740 small 2006 manual 6920 aygo 90000 10 NaN toyota no 2016-03-09 00:00:00 0 94116 2016-03-17 05:16:32
44520 2016-03-10 22:37:21 2500 convertible 1998 manual 7512 golf 150000 6 NaN volkswagen NaN 2016-03-10 00:00:00 0 68239 2016-04-05 15:17:50
In [10]:
# Анализ уникальных значений 
# категориальных текстовых признаков
for i in data.select_dtypes(include='object').columns: 
    print(f'Уникальные значения признака "{i}":', data[i].unique()) 
    print(f'Всего унимальных значений признака "{i}":', len(data[i].unique())) 
    print() 

Уникальные значения признака "DateCrawled": ['2016-03-24 11:52:17' '2016-03-24 10:58:45' '2016-03-14 12:52:21' ...
 '2016-03-21 09:50:58' '2016-03-14 17:48:27' '2016-03-19 18:57:12']
Всего унимальных значений признака "DateCrawled": 271174

Уникальные значения признака "VehicleType": [nan 'coupe' 'suv' 'small' 'sedan' 'convertible' 'bus' 'wagon' 'other']
Всего унимальных значений признака "VehicleType": 9

Уникальные значения признака "Gearbox": ['manual' 'auto' nan]
Всего унимальных значений признака "Gearbox": 3

Уникальные значения признака "Model": ['golf' nan 'grand' 'fabia' '3er' '2_reihe' 'other' 'c_max' '3_reihe'
 'passat' 'navara' 'ka' 'polo' 'twingo' 'a_klasse' 'scirocco' '5er'
 'meriva' 'arosa' 'c4' 'civic' 'transporter' 'punto' 'e_klasse' 'clio'
 'kadett' 'kangoo' 'corsa' 'one' 'fortwo' '1er' 'b_klasse' 'signum'
 'astra' 'a8' 'jetta' 'fiesta' 'c_klasse' 'micra' 'vito' 'sprinter' '156'
 'escort' 'forester' 'xc_reihe' 'scenic' 'a4' 'a1' 'insignia' 'combo'
 'focus' 'tt' 'a6' 'jazz' 'omega' 'slk' '7er' '80' '147' '100' 'z_reihe'
 'sportage' 'sorento' 'v40' 'ibiza' 'mustang' 'eos' 'touran' 'getz' 'a3'
 'almera' 'megane' 'lupo' 'r19' 'zafira' 'caddy' 'mondeo' 'cordoba' 'colt'
 'impreza' 'vectra' 'berlingo' 'tiguan' 'i_reihe' 'espace' 'sharan'
 '6_reihe' 'panda' 'up' 'seicento' 'ceed' '5_reihe' 'yeti' 'octavia' 'mii'
 'rx_reihe' '6er' 'modus' 'fox' 'matiz' 'beetle' 'c1' 'rio' 'touareg'
 'logan' 'spider' 'cuore' 's_max' 'a2' 'galaxy' 'c3' 'viano' 's_klasse'
 '1_reihe' 'avensis' 'roomster' 'sl' 'kaefer' 'santa' 'cooper' 'leon'
 '4_reihe' 'a5' '500' 'laguna' 'ptcruiser' 'clk' 'primera' 'x_reihe'
 'exeo' '159' 'transit' 'juke' 'qashqai' 'carisma' 'accord' 'corolla'
 'lanos' 'phaeton' 'verso' 'swift' 'rav' 'picanto' 'boxster' 'kalos'
 'superb' 'stilo' 'alhambra' 'mx_reihe' 'roadster' 'ypsilon' 'cayenne'
 'galant' 'justy' '90' 'sirion' 'crossfire' 'agila' 'duster' 'cr_reihe'
 'v50' 'c_reihe' 'v_klasse' 'm_klasse' 'yaris' 'c5' 'aygo' 'cc' 'carnival'
 'fusion' '911' 'bora' 'forfour' 'm_reihe' 'cl' 'tigra' '300c' 'spark'
 'v70' 'kuga' 'x_type' 'ducato' 's_type' 'x_trail' 'toledo' 'altea'
 'voyager' 'calibra' 'bravo' 'antara' 'tucson' 'citigo' 'jimny' 'wrangler'
 'lybra' 'q7' 'lancer' 'captiva' 'c2' 'discovery' 'freelander' 'sandero'
 'note' '900' 'cherokee' 'clubman' 'samara' 'defender' '601' 'cx_reihe'
 'legacy' 'pajero' 'auris' 'niva' 's60' 'nubira' 'vivaro' 'g_klasse'
 'lodgy' '850' 'range_rover' 'q3' 'serie_2' 'glk' 'charade' 'croma'
 'outlander' 'doblo' 'musa' 'move' '9000' 'v60' '145' 'aveo' '200' 'b_max'
 'range_rover_sport' 'terios' 'rangerover' 'q5' 'range_rover_evoque'
 'materia' 'delta' 'gl' 'kalina' 'amarok' 'elefantino' 'i3' 'kappa'
 'serie_3' 'serie_1']
Всего унимальных значений признака "Model": 251

Уникальные значения признака "FuelType": ['petrol' 'gasoline' nan 'lpg' 'other' 'hybrid' 'cng' 'electric']
Всего унимальных значений признака "FuelType": 8

Уникальные значения признака "Brand": ['volkswagen' 'audi' 'jeep' 'skoda' 'bmw' 'peugeot' 'ford' 'mazda'
 'nissan' 'renault' 'mercedes_benz' 'opel' 'seat' 'citroen' 'honda' 'fiat'
 'mini' 'smart' 'hyundai' 'sonstige_autos' 'alfa_romeo' 'subaru' 'volvo'
 'mitsubishi' 'kia' 'suzuki' 'lancia' 'toyota' 'chevrolet' 'dacia'
 'daihatsu' 'trabant' 'saab' 'chrysler' 'jaguar' 'daewoo' 'porsche'
 'rover' 'land_rover' 'lada']
Всего унимальных значений признака "Brand": 40

Уникальные значения признака "Repaired": [nan 'yes' 'no']
Всего унимальных значений признака "Repaired": 3

Уникальные значения признака "DateCreated": ['2016-03-24 00:00:00' '2016-03-14 00:00:00' '2016-03-17 00:00:00'
 '2016-03-31 00:00:00' '2016-04-04 00:00:00' '2016-04-01 00:00:00'
 '2016-03-21 00:00:00' '2016-03-26 00:00:00' '2016-04-07 00:00:00'
 '2016-03-15 00:00:00' '2016-03-11 00:00:00' '2016-03-20 00:00:00'
 '2016-03-23 00:00:00' '2016-03-27 00:00:00' '2016-03-12 00:00:00'
 '2016-03-13 00:00:00' '2016-03-18 00:00:00' '2016-03-10 00:00:00'
 '2016-03-07 00:00:00' '2016-03-09 00:00:00' '2016-03-08 00:00:00'
 '2016-04-03 00:00:00' '2016-03-29 00:00:00' '2016-03-25 00:00:00'
 '2016-03-28 00:00:00' '2016-03-30 00:00:00' '2016-03-22 00:00:00'
 '2016-02-09 00:00:00' '2016-03-05 00:00:00' '2016-04-02 00:00:00'
 '2016-03-16 00:00:00' '2016-03-19 00:00:00' '2016-04-05 00:00:00'
 '2016-03-06 00:00:00' '2016-02-12 00:00:00' '2016-03-03 00:00:00'
 '2016-03-01 00:00:00' '2016-03-04 00:00:00' '2016-04-06 00:00:00'
 '2016-02-15 00:00:00' '2016-02-24 00:00:00' '2016-02-27 00:00:00'
 '2015-03-20 00:00:00' '2016-02-28 00:00:00' '2016-02-17 00:00:00'
 '2016-01-27 00:00:00' '2016-02-20 00:00:00' '2016-02-29 00:00:00'
 '2016-02-10 00:00:00' '2016-02-23 00:00:00' '2016-02-21 00:00:00'
 '2015-11-02 00:00:00' '2016-02-19 00:00:00' '2016-02-26 00:00:00'
 '2016-02-11 00:00:00' '2016-01-10 00:00:00' '2016-02-06 00:00:00'
 '2016-02-18 00:00:00' '2016-01-29 00:00:00' '2016-03-02 00:00:00'
 '2015-12-06 00:00:00' '2016-01-24 00:00:00' '2016-01-30 00:00:00'
 '2016-02-02 00:00:00' '2016-02-16 00:00:00' '2016-02-13 00:00:00'
 '2016-02-05 00:00:00' '2016-02-22 00:00:00' '2015-11-17 00:00:00'
 '2014-03-10 00:00:00' '2016-02-07 00:00:00' '2016-01-23 00:00:00'
 '2016-02-25 00:00:00' '2016-02-14 00:00:00' '2016-01-02 00:00:00'
 '2015-09-04 00:00:00' '2015-11-12 00:00:00' '2015-12-27 00:00:00'
 '2015-11-24 00:00:00' '2016-01-20 00:00:00' '2016-02-03 00:00:00'
 '2015-12-05 00:00:00' '2015-08-07 00:00:00' '2016-01-28 00:00:00'
 '2016-01-31 00:00:00' '2016-02-08 00:00:00' '2016-01-07 00:00:00'
 '2016-01-22 00:00:00' '2016-01-18 00:00:00' '2016-01-08 00:00:00'
 '2015-11-23 00:00:00' '2016-01-13 00:00:00' '2016-01-17 00:00:00'
 '2016-01-15 00:00:00' '2015-11-08 00:00:00' '2016-01-26 00:00:00'
 '2016-02-04 00:00:00' '2016-01-25 00:00:00' '2016-01-16 00:00:00'
 '2015-08-10 00:00:00' '2016-01-03 00:00:00' '2016-01-19 00:00:00'
 '2015-12-30 00:00:00' '2016-02-01 00:00:00' '2015-12-17 00:00:00'
 '2015-11-10 00:00:00' '2016-01-06 00:00:00' '2015-09-09 00:00:00'
 '2015-06-18 00:00:00']
Всего унимальных значений признака "DateCreated": 109

Уникальные значения признака "LastSeen": ['2016-04-07 03:16:57' '2016-04-07 01:46:50' '2016-04-05 12:47:46' ...
 '2016-03-19 20:44:43' '2016-03-29 10:17:23' '2016-03-21 10:42:49']
Всего унимальных значений признака "LastSeen": 179150
In [11]:
# Анализ нефвных совпадений 
# признака "Model"
for i in data['Model'].fillna('no_value').unique():
    print(i, '~', process.extract(i, data['Model'].fillna('no_value').unique(), limit=3)) 

golf ~ [('golf', 100), ('gl', 67), ('twingo', 60)]
no_value ~ [('no_value', 100), ('altea', 60), ('lupo', 51)]
grand ~ [('grand', 100), ('panda', 60), ('logan', 60)]
fabia ~ [('fabia', 100), ('ibiza', 60), ('agila', 60)]
3er ~ [('3er', 100), ('5er', 67), ('1er', 67)]
2_reihe ~ [('2_reihe', 100), ('3_reihe', 86), ('z_reihe', 86)]
other ~ [('other', 100), ('transporter', 72), ('boxster', 67)]
c_max ~ [('c_max', 100), ('s_max', 80), ('b_max', 80)]
3_reihe ~ [('3_reihe', 100), ('2_reihe', 86), ('z_reihe', 86)]
passat ~ [('passat', 100), ('tt', 60), ('arosa', 55)]
navara ~ [('navara', 100), ('rav', 72), ('niva', 68)]
ka ~ [('ka', 100), ('kadett', 90), ('kangoo', 90)]
polo ~ [('polo', 100), ('doblo', 67), ('toledo', 60)]
twingo ~ [('twingo', 100), ('elefantino', 72), ('citigo', 67)]
a_klasse ~ [('a_klasse', 100), ('e_klasse', 88), ('b_klasse', 88)]
scirocco ~ [('scirocco', 100), ('cc', 90), ('clio', 68)]
5er ~ [('5er', 100), ('3er', 67), ('1er', 67)]
meriva ~ [('meriva', 100), ('materia', 77), ('niva', 68)]
arosa ~ [('arosa', 100), ('carisma', 67), ('corsa', 60)]
c4 ~ [('c4', 100), ('300c', 60), ('a4', 50)]
civic ~ [('civic', 100), ('mii', 60), ('cc', 57)]
transporter ~ [('transporter', 100), ('other', 72), ('note', 68)]
punto ~ [('punto', 100), ('picanto', 67), ('ducato', 55)]
e_klasse ~ [('e_klasse', 100), ('a_klasse', 88), ('b_klasse', 88)]
clio ~ [('clio', 100), ('cl', 90), ('scirocco', 68)]
kadett ~ [('kadett', 100), ('ka', 90), ('tt', 90)]
kangoo ~ [('kangoo', 100), ('ka', 90), ('aygo', 68)]
corsa ~ [('corsa', 100), ('cordoba', 67), ('carisma', 67)]
one ~ [('one', 100), ('phaeton', 72), ('ypsilon', 72)]
fortwo ~ [('fortwo', 100), ('sorento', 62), ('forfour', 62)]
1er ~ [('1er', 100), ('3er', 67), ('5er', 67)]
b_klasse ~ [('b_klasse', 100), ('a_klasse', 88), ('e_klasse', 88)]
signum ~ [('signum', 100), ('insignia', 57), ('tiguan', 50)]
astra ~ [('astra', 100), ('antara', 73), ('rav', 72)]
a8 ~ [('a8', 100), ('meriva', 60), ('corsa', 60)]
jetta ~ [('jetta', 100), ('tt', 90), ('a8', 60)]
fiesta ~ [('fiesta', 100), ('a8', 60), ('a4', 60)]
c_klasse ~ [('c_klasse', 100), ('a_klasse', 88), ('e_klasse', 88)]
micra ~ [('micra', 100), ('rav', 72), ('corsa', 60)]
vito ~ [('vito', 100), ('viano', 67), ('citigo', 60)]
sprinter ~ [('sprinter', 100), ('spider', 71), ('note', 68)]
156 ~ [('156', 100), ('159', 67), ('145', 67)]
escort ~ [('escort', 100), ('colt', 68), ('sorento', 62)]
forester ~ [('forester', 100), ('boxster', 67), ('other', 62)]
xc_reihe ~ [('xc_reihe', 100), ('x_reihe', 93), ('c_reihe', 93)]
scenic ~ [('scenic', 100), ('scirocco', 57), ('seicento', 57)]
a4 ~ [('a4', 100), ('meriva', 60), ('corsa', 60)]
a1 ~ [('a1', 100), ('meriva', 60), ('corsa', 60)]
insignia ~ [('insignia', 100), ('niva', 77), ('a8', 60)]
combo ~ [('combo', 100), ('croma', 60), ('doblo', 60)]
focus ~ [('focus', 100), ('modus', 60), ('fox', 60)]
tt ~ [('tt', 100), ('kadett', 90), ('jetta', 90)]
a6 ~ [('a6', 100), ('meriva', 60), ('corsa', 60)]
jazz ~ [('jazz', 100), ('ka', 45), ('a8', 45)]
omega ~ [('omega', 100), ('megane', 73), ('one', 60)]
slk ~ [('slk', 100), ('sl', 90), ('clk', 67)]
7er ~ [('7er', 100), ('3er', 67), ('5er', 67)]
80 ~ [('80', 100), ('850', 80), ('a8', 50)]
147 ~ [('147', 100), ('145', 67), ('c4', 45)]
100 ~ [('100', 100), ('500', 67), ('900', 67)]
z_reihe ~ [('z_reihe', 100), ('2_reihe', 86), ('3_reihe', 86)]
sportage ~ [('sportage', 100), ('range_rover_sport', 69), ('bora', 68)]
sorento ~ [('sorento', 100), ('seicento', 67), ('fortwo', 62)]
v40 ~ [('v40', 100), ('v50', 67), ('v70', 67)]
ibiza ~ [('ibiza', 100), ('fabia', 60), ('a8', 60)]
mustang ~ [('mustang', 100), ('musa', 73), ('gl', 60)]
eos ~ [('eos', 100), ('mondeo', 72), ('terios', 67)]
touran ~ [('touran', 100), ('bora', 68), ('tiguan', 67)]
getz ~ [('getz', 100), ('sportage', 60), ('voyager', 51)]
a3 ~ [('a3', 100), ('meriva', 60), ('corsa', 60)]
almera ~ [('almera', 100), ('altea', 73), ('rav', 72)]
megane ~ [('megane', 100), ('omega', 73), ('one', 60)]
lupo ~ [('lupo', 100), ('up', 90), ('no_value', 51)]
r19 ~ [('r19', 100), ('159', 67), ('90', 60)]
zafira ~ [('zafira', 100), ('rav', 72), ('calibra', 62)]
caddy ~ [('caddy', 100), ('cayenne', 50), ('charade', 50)]
mondeo ~ [('mondeo', 100), ('eos', 72), ('one', 67)]
cordoba ~ [('cordoba', 100), ('corolla', 71), ('corsa', 67)]
colt ~ [('colt', 100), ('escort', 68), ('cl', 67)]
impreza ~ [('impreza', 100), ('a8', 60), ('a4', 60)]
vectra ~ [('vectra', 100), ('rav', 72), ('a8', 60)]
berlingo ~ [('berlingo', 100), ('golf', 60), ('3er', 60)]
tiguan ~ [('tiguan', 100), ('tigra', 73), ('touran', 67)]
i_reihe ~ [('i_reihe', 100), ('2_reihe', 86), ('3_reihe', 86)]
espace ~ [('espace', 100), ('eos', 60), ('ceed', 60)]
sharan ~ [('sharan', 100), ('samara', 67), ('charade', 62)]
6_reihe ~ [('6_reihe', 100), ('2_reihe', 86), ('3_reihe', 86)]
panda ~ [('panda', 100), ('grand', 60), ('santa', 60)]
up ~ [('up', 100), ('lupo', 90), ('superb', 90)]
seicento ~ [('seicento', 100), ('sorento', 67), ('picanto', 67)]
ceed ~ [('ceed', 100), ('espace', 60), ('cayenne', 55)]
5_reihe ~ [('5_reihe', 100), ('2_reihe', 86), ('3_reihe', 86)]
yeti ~ [('yeti', 100), ('i3', 60), ('x_type', 51)]
octavia ~ [('octavia', 100), ('rav', 60), ('carisma', 57)]
mii ~ [('mii', 100), ('civic', 60), ('micra', 60)]
rx_reihe ~ [('rx_reihe', 100), ('x_reihe', 93), ('xc_reihe', 88)]
6er ~ [('6er', 100), ('3er', 67), ('5er', 67)]
modus ~ [('modus', 100), ('musa', 67), ('focus', 60)]
fox ~ [('fox', 100), ('fortwo', 60), ('forester', 60)]
matiz ~ [('matiz', 100), ('materia', 67), ('elefantino', 54)]
beetle ~ [('beetle', 100), ('leon', 60), ('toledo', 50)]
c1 ~ [('c1', 100), ('300c', 60), ('c4', 50)]
rio ~ [('rio', 100), ('sirion', 90), ('terios', 90)]
touareg ~ [('touareg', 100), ('touran', 62), ('gl', 60)]
logan ~ [('logan', 100), ('leon', 67), ('grand', 60)]
spider ~ [('spider', 100), ('sprinter', 71), ('superb', 67)]
cuore ~ [('cuore', 100), ('corsa', 60), ('one', 60)]
s_max ~ [('s_max', 100), ('c_max', 80), ('b_max', 80)]
a2 ~ [('a2', 100), ('meriva', 60), ('corsa', 60)]
galaxy ~ [('galaxy', 100), ('galant', 67), ('glk', 60)]
c3 ~ [('c3', 100), ('300c', 60), ('c4', 50)]
viano ~ [('viano', 100), ('vivaro', 73), ('vito', 67)]
s_klasse ~ [('s_klasse', 100), ('a_klasse', 88), ('e_klasse', 88)]
1_reihe ~ [('1_reihe', 100), ('2_reihe', 86), ('3_reihe', 86)]
avensis ~ [('avensis', 100), ('aveo', 68), ('eos', 60)]
roomster ~ [('roomster', 100), ('roadster', 75), ('boxster', 67)]
sl ~ [('sl', 100), ('slk', 90), ('focus', 60)]
kaefer ~ [('kaefer', 100), ('ka', 90), ('3er', 60)]
santa ~ [('santa', 100), ('antara', 73), ('panda', 60)]
cooper ~ [('cooper', 100), ('3er', 60), ('5er', 60)]
leon ~ [('leon', 100), ('ypsilon', 77), ('phaeton', 68)]
4_reihe ~ [('4_reihe', 100), ('2_reihe', 86), ('3_reihe', 86)]
a5 ~ [('a5', 100), ('meriva', 60), ('corsa', 60)]
500 ~ [('500', 100), ('100', 67), ('v50', 67)]
laguna ~ [('laguna', 100), ('niva', 60), ('panda', 55)]
ptcruiser ~ [('ptcruiser', 100), ('duster', 66), ('primera', 62)]
clk ~ [('clk', 100), ('cl', 90), ('slk', 67)]
primera ~ [('primera', 100), ('rav', 72), ('sprinter', 67)]
x_reihe ~ [('x_reihe', 100), ('xc_reihe', 93), ('rx_reihe', 93)]
exeo ~ [('exeo', 100), ('eos', 57), ('toledo', 51)]
159 ~ [('159', 100), ('156', 67), ('r19', 67)]
transit ~ [('transit', 100), ('transporter', 67), ('touran', 62)]
juke ~ [('juke', 100), ('no_value', 51), ('range_rover_evoque', 51)]
qashqai ~ [('qashqai', 100), ('i3', 60), ('arosa', 50)]
carisma ~ [('carisma', 100), ('arosa', 67), ('yaris', 67)]
accord ~ [('accord', 100), ('cc', 90), ('cordoba', 62)]
corolla ~ [('corolla', 100), ('cordoba', 71), ('corsa', 67)]
lanos ~ [('lanos', 100), ('arosa', 60), ('eos', 60)]
phaeton ~ [('phaeton', 100), ('one', 72), ('leon', 68)]
verso ~ [('verso', 100), ('range_rover_sport', 72), ('range_rover', 68)]
swift ~ [('swift', 100), ('tt', 60), ('transit', 50)]
rav ~ [('rav', 100), ('bravo', 90), ('navara', 72)]
picanto ~ [('picanto', 100), ('punto', 67), ('seicento', 67)]
boxster ~ [('boxster', 100), ('other', 67), ('forester', 67)]
kalos ~ [('kalos', 100), ('ka', 90), ('arosa', 60)]
superb ~ [('superb', 100), ('up', 90), ('spider', 67)]
stilo ~ [('stilo', 100), ('ypsilon', 67), ('rio', 60)]
alhambra ~ [('alhambra', 100), ('bora', 77), ('rav', 72)]
mx_reihe ~ [('mx_reihe', 100), ('x_reihe', 93), ('m_reihe', 93)]
roadster ~ [('roadster', 100), ('roomster', 75), ('duster', 71)]
ypsilon ~ [('ypsilon', 100), ('leon', 77), ('one', 72)]
cayenne ~ [('cayenne', 100), ('one', 60), ('ceed', 55)]
galant ~ [('galant', 100), ('galaxy', 67), ('tt', 60)]
justy ~ [('justy', 100), ('duster', 55), ('s_type', 55)]
90 ~ [('90', 100), ('900', 90), ('9000', 90)]
sirion ~ [('sirion', 100), ('rio', 90), ('one', 72)]
crossfire ~ [('crossfire', 100), ('eos', 60), ('rio', 60)]
agila ~ [('agila', 100), ('fabia', 60), ('altea', 60)]
duster ~ [('duster', 100), ('roadster', 71), ('ptcruiser', 66)]
cr_reihe ~ [('cr_reihe', 100), ('c_reihe', 93), ('xc_reihe', 88)]
v50 ~ [('v50', 100), ('v40', 67), ('500', 67)]
c_reihe ~ [('c_reihe', 100), ('xc_reihe', 93), ('cr_reihe', 93)]
v_klasse ~ [('v_klasse', 100), ('a_klasse', 88), ('e_klasse', 88)]
m_klasse ~ [('m_klasse', 100), ('a_klasse', 88), ('e_klasse', 88)]
yaris ~ [('yaris', 100), ('auris', 80), ('carisma', 67)]
c5 ~ [('c5', 100), ('300c', 60), ('c4', 50)]
aygo ~ [('aygo', 100), ('kangoo', 68), ('sportage', 51)]
cc ~ [('cc', 100), ('scirocco', 90), ('accord', 90)]
carnival ~ [('carnival', 100), ('niva', 90), ('carisma', 67)]
fusion ~ [('fusion', 100), ('one', 72), ('sirion', 67)]
911 ~ [('911', 100), ('a1', 45), ('c1', 45)]
bora ~ [('bora', 100), ('alhambra', 77), ('calibra', 77)]
forfour ~ [('forfour', 100), ('fortwo', 62), ('fox', 60)]
m_reihe ~ [('m_reihe', 100), ('mx_reihe', 93), ('2_reihe', 86)]
cl ~ [('cl', 100), ('clio', 90), ('clk', 90)]
tigra ~ [('tigra', 100), ('tiguan', 73), ('rav', 72)]
300c ~ [('300c', 100), ('c4', 60), ('c1', 60)]
spark ~ [('spark', 100), ('ka', 60), ('espace', 55)]
v70 ~ [('v70', 100), ('v40', 67), ('v50', 67)]
kuga ~ [('kuga', 100), ('ka', 67), ('a8', 60)]
x_type ~ [('x_type', 100), ('s_type', 83), ('yeti', 51)]
ducato ~ [('ducato', 100), ('picanto', 62), ('punto', 55)]
s_type ~ [('s_type', 100), ('x_type', 83), ('justy', 55)]
x_trail ~ [('x_trail', 100), ('rio', 60), ('rav', 60)]
toledo ~ [('toledo', 100), ('leon', 68), ('polo', 60)]
altea ~ [('altea', 100), ('almera', 73), ('materia', 67)]
voyager ~ [('voyager', 100), ('3er', 60), ('5er', 60)]
calibra ~ [('calibra', 100), ('bora', 77), ('rav', 72)]
bravo ~ [('bravo', 100), ('rav', 90), ('alhambra', 68)]
antara ~ [('antara', 100), ('astra', 73), ('santa', 73)]
tucson ~ [('tucson', 100), ('one', 72), ('fusion', 67)]
citigo ~ [('citigo', 100), ('golf', 60), ('clio', 60)]
jimny ~ [('jimny', 100), ('i3', 45), ('viano', 40)]
wrangler ~ [('wrangler', 100), ('gl', 90), ('range_rover_sport', 68)]
lybra ~ [('lybra', 100), ('rav', 72), ('bora', 67)]
q7 ~ [('q7', 100), ('q3', 50), ('q5', 50)]
lancer ~ [('lancer', 100), ('freelander', 75), ('outlander', 75)]
captiva ~ [('captiva', 100), ('niva', 68), ('carnival', 67)]
c2 ~ [('c2', 100), ('300c', 60), ('c4', 50)]
discovery ~ [('discovery', 100), ('move', 68), ('3er', 60)]
freelander ~ [('freelander', 100), ('lancer', 75), ('defender', 67)]
sandero ~ [('sandero', 100), ('rio', 72), ('mondeo', 62)]
note ~ [('note', 100), ('transporter', 68), ('sprinter', 68)]
900 ~ [('900', 100), ('90', 90), ('9000', 86)]
cherokee ~ [('cherokee', 100), ('3er', 60), ('5er', 60)]
clubman ~ [('clubman', 100), ('cl', 90), ('clk', 60)]
samara ~ [('samara', 100), ('rav', 72), ('navara', 67)]
defender ~ [('defender', 100), ('freelander', 67), ('3er', 60)]
601 ~ [('601', 100), ('s60', 67), ('v60', 67)]
cx_reihe ~ [('cx_reihe', 100), ('x_reihe', 93), ('c_reihe', 93)]
legacy ~ [('legacy', 100), ('omega', 55), ('logan', 55)]
pajero ~ [('pajero', 100), ('rio', 72), ('phaeton', 62)]
auris ~ [('auris', 100), ('yaris', 80), ('carisma', 67)]
niva ~ [('niva', 100), ('carnival', 90), ('insignia', 77)]
s60 ~ [('s60', 100), ('601', 67), ('v60', 67)]
nubira ~ [('nubira', 100), ('rav', 72), ('bora', 68)]
vivaro ~ [('vivaro', 100), ('viano', 73), ('rio', 72)]
g_klasse ~ [('g_klasse', 100), ('a_klasse', 88), ('e_klasse', 88)]
lodgy ~ [('lodgy', 100), ('logan', 60), ('legacy', 55)]
850 ~ [('850', 100), ('80', 80), ('500', 67)]
range_rover ~ [('range_rover', 100), ('rangerover', 95), ('range_rover_sport', 90)]
q3 ~ [('q3', 100), ('a3', 50), ('c3', 50)]
serie_2 ~ [('serie_2', 100), ('serie_3', 86), ('serie_1', 86)]
glk ~ [('glk', 100), ('gl', 90), ('slk', 67)]
charade ~ [('charade', 100), ('sharan', 62), ('rav', 60)]
croma ~ [('croma', 100), ('cordoba', 67), ('carisma', 67)]
outlander ~ [('outlander', 100), ('lancer', 75), ('freelander', 63)]
doblo ~ [('doblo', 100), ('polo', 67), ('combo', 60)]
musa ~ [('musa', 100), ('mustang', 73), ('modus', 67)]
move ~ [('move', 100), ('discovery', 68), ('range_rover', 68)]
9000 ~ [('9000', 100), ('90', 90), ('900', 86)]
v60 ~ [('v60', 100), ('v40', 67), ('v50', 67)]
145 ~ [('145', 100), ('156', 67), ('147', 67)]
aveo ~ [('aveo', 100), ('avensis', 68), ('phaeton', 68)]
200 ~ [('200', 100), ('100', 67), ('500', 67)]
b_max ~ [('b_max', 100), ('c_max', 80), ('s_max', 80)]
range_rover_sport ~ [('range_rover_sport', 100), ('range_rover', 90), ('rangerover', 81)]
terios ~ [('terios', 100), ('rio', 90), ('eos', 67)]
rangerover ~ [('rangerover', 100), ('range_rover', 95), ('range_rover_sport', 81)]
q5 ~ [('q5', 100), ('a5', 50), ('c5', 50)]
range_rover_evoque ~ [('range_rover_evoque', 100), ('range_rover', 90), ('rangerover', 81)]
materia ~ [('materia', 100), ('meriva', 77), ('astra', 67)]
delta ~ [('delta', 100), ('a8', 60), ('jetta', 60)]
gl ~ [('gl', 100), ('wrangler', 90), ('glk', 90)]
kalina ~ [('kalina', 100), ('ka', 90), ('calibra', 62)]
amarok ~ [('amarok', 100), ('samara', 67), ('ka', 60)]
elefantino ~ [('elefantino', 100), ('twingo', 72), ('rio', 60)]
i3 ~ [('i3', 100), ('yeti', 60), ('qashqai', 60)]
kappa ~ [('kappa', 100), ('ka', 90), ('kalina', 55)]
serie_3 ~ [('serie_3', 100), ('serie_2', 86), ('serie_1', 86)]
serie_1 ~ [('serie_1', 100), ('serie_2', 86), ('serie_3', 86)]
In [12]:
# Анализ нефвных совпадений 
# признака "Brand"
for i in data['Brand'].unique():
    print(i, '~', process.extract(i, data['Brand'].unique(), limit=3)) 

volkswagen ~ [('volkswagen', 100), ('volvo', 54), ('opel', 45)]
audi ~ [('audi', 100), ('hyundai', 60), ('subaru', 51)]
jeep ~ [('jeep', 100), ('peugeot', 36), ('chevrolet', 31)]
skoda ~ [('skoda', 100), ('honda', 60), ('kia', 50)]
bmw ~ [('bmw', 100), ('volkswagen', 30), ('mazda', 30)]
peugeot ~ [('peugeot', 100), ('opel', 45), ('seat', 45)]
ford ~ [('ford', 100), ('mercedes_benz', 45), ('alfa_romeo', 45)]
mazda ~ [('mazda', 100), ('lada', 67), ('audi', 44)]
nissan ~ [('nissan', 100), ('saab', 51), ('seat', 45)]
renault ~ [('renault', 100), ('seat', 55), ('sonstige_autos', 51)]
mercedes_benz ~ [('mercedes_benz', 100), ('ford', 45), ('seat', 45)]
opel ~ [('opel', 100), ('citroen', 51), ('volkswagen', 45)]
seat ~ [('seat', 100), ('smart', 67), ('renault', 55)]
citroen ~ [('citroen', 100), ('opel', 51), ('chevrolet', 50)]
honda ~ [('honda', 100), ('hyundai', 67), ('skoda', 60)]
fiat ~ [('fiat', 100), ('daihatsu', 68), ('kia', 57)]
mini ~ [('mini', 100), ('nissan', 45), ('mitsubishi', 45)]
smart ~ [('smart', 100), ('seat', 67), ('subaru', 55)]
hyundai ~ [('hyundai', 100), ('honda', 67), ('audi', 60)]
sonstige_autos ~ [('sonstige_autos', 100), ('renault', 51), ('audi', 45)]
alfa_romeo ~ [('alfa_romeo', 100), ('rover', 54), ('land_rover', 50)]
subaru ~ [('subaru', 100), ('smart', 55), ('audi', 51)]
volvo ~ [('volvo', 100), ('volkswagen', 54), ('chevrolet', 54)]
mitsubishi ~ [('mitsubishi', 100), ('audi', 45), ('fiat', 45)]
kia ~ [('kia', 100), ('suzuki', 72), ('lancia', 60)]
suzuki ~ [('suzuki', 100), ('kia', 72), ('subaru', 50)]
lancia ~ [('lancia', 100), ('dacia', 73), ('kia', 60)]
toyota ~ [('toyota', 100), ('sonstige_autos', 40), ('audi', 36)]
chevrolet ~ [('chevrolet', 100), ('chrysler', 59), ('volvo', 54)]
dacia ~ [('dacia', 100), ('lancia', 73), ('daihatsu', 72)]
daihatsu ~ [('daihatsu', 100), ('dacia', 72), ('fiat', 68)]
trabant ~ [('trabant', 100), ('seat', 45), ('fiat', 45)]
saab ~ [('saab', 100), ('nissan', 51), ('seat', 50)]
chrysler ~ [('chrysler', 100), ('chevrolet', 59), ('rover', 46)]
jaguar ~ [('jaguar', 100), ('subaru', 50), ('audi', 45)]
daewoo ~ [('daewoo', 100), ('lada', 45), ('alfa_romeo', 38)]
porsche ~ [('porsche', 100), ('ford', 45), ('seat', 45)]
rover ~ [('rover', 100), ('land_rover', 90), ('alfa_romeo', 54)]
land_rover ~ [('land_rover', 100), ('rover', 90), ('lada', 68)]
lada ~ [('lada', 100), ('land_rover', 68), ('mazda', 67)]
In [13]:
# Проверка коррелируемости
# признаков датафрейма
data.corr()
Out[13]:
Price RegistrationYear Power Kilometer RegistrationMonth NumberOfPictures PostalCode
Price 1.000000 0.026916 0.158872 -0.333199 0.110581 NaN 0.076055
RegistrationYear 0.026916 1.000000 -0.000828 -0.053447 -0.011619 NaN -0.003459
Power 0.158872 -0.000828 1.000000 0.024002 0.043380 NaN 0.021665
Kilometer -0.333199 -0.053447 0.024002 1.000000 0.009571 NaN -0.007698
RegistrationMonth 0.110581 -0.011619 0.043380 0.009571 1.000000 NaN 0.013995
NumberOfPictures NaN NaN NaN NaN NaN NaN NaN
PostalCode 0.076055 -0.003459 0.021665 -0.007698 0.013995 NaN 1.000000

Выводы из анализа данных

  1. Датафрейм содержит 354369 объектов и 16 признаков, 7 из которых являются 64-битными целочисленными, а 9 типа object. Целевым является целочисленный признак Price.
  2. В данных есть пропуски. Их количество в разных признаках более 1% от всего количества. Поэтому, их нельзя удалить, а требуется заменить на значение no_value.
  3. Категориальные признаки VehicleType, FuelType и Model имеют значения other, которые можно изменить на no_value также, как и пропуски.
  4. По мнению автора данной работы, следующие признаки могут быть неинформативными для моделей машинного обучения:
    1. DateCrawled — дата скачивания анкеты из базы (может влиять на цену относительно даты размещения объявления, но незначительно)
    2. RegistrationMonth — месяц регистрации автомобиля (большее значение имеет год регистрации)
    3. DateCreated — дата создания анкеты (в целях предсказания цен автомобилей в будущих анкетах эта информация не акутальна)
    4. NumberOfPictures — количество фотографий автомобиля (может влиять на цену, т.к. фотографии продоваемого объекта вызывают доверие, но признак содержит только нули)
    5. PostalCode — почтовый индекс владельца анкеты (может влиять, если местоположение продавца и покупателя имеет значение, но это не точно)
    6. LastSeen — дата последней активности пользователя (может указывать на продолжительность наличия объявления в сети, но этот признак в меньшей степени может влиять на цену, чем другие признаки, описывающие характеристики продаваемого авто)
  5. Числовые данные не распределены нормально и имеют выбросы.
  6. Числовые признаки имеют разный диапазон. Для использования в машинном обучении их требуется стандартизировать.
  7. Корреляция аттрибутов между собой и с целевым признаком слабая. Наибольшей обратной корреляцией с целевым признаком обладают признаки RegistrationYear.
  8. Анализ текстовых категриальных признаков Model и Brand выявил неявное совпадение значений range_rover и rangerover в признаке Model. Эти значения следует объеденить в range_rover.
  9. Названия признаков не в «змеином» стиле. Можно привести их в соответствие со «змеиный» стилем.

Данные с пропусками:

  1. VehicleType — тип автомобильного кузова
  2. Gearbox — тип коробки передач
  3. Model — модель автомобиля
  4. FuelType — тип топлива
  5. Repaired — была машина в ремонте или нет

Все признаки с пропусками категориального типа object. Для них будет использована категория no_value, указывающая на отсутствие значений.

Ненормальности в данных:

  1. Атрибут RegistrationYear содержит 171 значение с годами производства автомобилей меньше 1900 года и больше 2023 года. Объектами с этими значениями можно принебречь, т.к. их мне 1% от всего количества объектов. Их требуется удалить.
  2. Атрибут Power содержит более 11% значений с мощностью двигателя выходящую за пределы известных значейни. Например, меньше, чем у самого маломощного автомобиля «Benz Patent Motorwagen», у которого мощность двигателя равна 0.75 л.с. (https://1gai-ru.turbopages.org/turbo/1gai.ru/s/blog/cars/513900-desyat-samyh-malomoschnyh-avtomobiley.html, 2023). Также атрибут Powerсодержит значения мощности двигателя более 5000 л.с.. Это превышает мощность самого мощного автомобиля Devel Sixteen (https://www.driver-helper.ru/text/sovetiy/top-10-samyx-moshhnyx-serijnyx-avto-v-mire). Учитывая большую долю подобных объектов и тот факт, что подобные объекты могут появиться в эксплуатационном данных, вместо того, чтобы от них избавляться, в них следует заменить ненормальные значения Power на медианные для каждой группы связки Brand Model.

Предобработка данных¶

In [14]:
# Замена пропусков 
# на значение "no_value" 
for i in data.columns:
    if data_shape - data[i].loc[data[i].notna()].shape[0] > 0:
        data.loc[data[i].isna(), i] = 'no_value'
In [15]:
# Замена значения "other" на "no_value" 
# для унификации отсутствующей информации 
# в признаках "VehicleType" и "FuellType"
data.loc[
    (data['VehicleType'] == 'other') | 
    (data['FuelType'] == 'other') |
    (data['Model'] == 'other'), 
    ['VehicleType', 'FuelType', 'Model'] 
] = 'no_value'
In [16]:
# Определение максимальной даты просмотра объявления 
# для установки в качестве предельного срока 
# выпуска автомобиля
data['DateCrawled'] = pd.to_datetime(
    data['DateCrawled'], format='%Y-%m-%dT%H:%M:%S'
)
date_crawled_max = data['DateCrawled'].max()
date_crawled_max
Out[16]:
Timestamp('2016-04-07 14:36:58')
In [17]:
# Удаление неинформативных признаков
data = data.drop([
    'DateCrawled', 
    'RegistrationMonth', 
    'DateCreated', 
    'NumberOfPictures', 
    'PostalCode', 
    'LastSeen'
], axis=1)
In [18]:
# Удаление ненормальностей в данных
# признака "RegistrationYear"
data = data.loc[
    (data['RegistrationYear'] > 1900) & 
    (data['RegistrationYear'] < date_crawled_max.year)
]
In [19]:
# Замена ненормальностей в данных 
# признака "Power" в более 10% объектов

#power_group_median = data.groupby(['Brand', 'Model'])['Power'].median()
power_group_median = data.pivot_table(values='Power', index=['Brand', 'Model'], aggfunc='median')

for i in power_group_median.index:
    data['Power'] = np.where(
        ((data['Power'] < .75) | (data['Power'] > 5000)) & 
        ((data['Power'] == i[0]) & (data['Model'] == i[1])), 
        power_group_median.loc[i], 
        data['Power']
    )

data['Power']
Out[19]:
0           0.0
1         190.0
2         163.0
3          75.0
4          69.0
          ...  
354364      0.0
354365      0.0
354366    101.0
354367    102.0
354368    100.0
Name: Power, Length: 330174, dtype: float64
In [20]:
# Объединение неявно совпадающих значений признака "Model" 
# "range_rover" и "rangerover" в "range_rover"
data.loc[data['Model'] == 'rangerover', 'Model'] = 'range_rover'
In [21]:
# Приведение названий признаков датафрейма
# к "змеиному" стилю
data.columns = [re.sub(r'(?<!^)(?=[A-Z])', '_', i).lower() for i in data.columns]

Проверка результатов предобработкаи данных¶

In [22]:
# Проверка изменений
data.info() 
data.head(10)

<class 'pandas.core.frame.DataFrame'>
Int64Index: 330174 entries, 0 to 354368
Data columns (total 10 columns):
 #   Column             Non-Null Count   Dtype  
---  ------             --------------   -----  
 0   price              330174 non-null  int64  
 1   vehicle_type       330174 non-null  object 
 2   registration_year  330174 non-null  int64  
 3   gearbox            330174 non-null  object 
 4   power              330174 non-null  float64
 5   model              330174 non-null  object 
 6   kilometer          330174 non-null  int64  
 7   fuel_type          330174 non-null  object 
 8   brand              330174 non-null  object 
 9   repaired           330174 non-null  object 
dtypes: float64(1), int64(3), object(6)
memory usage: 27.7+ MB
Out[22]:
price vehicle_type registration_year gearbox power model kilometer fuel_type brand repaired
0 480 no_value 1993 manual 0.0 golf 150000 petrol volkswagen no_value
1 18300 coupe 2011 manual 190.0 no_value 125000 gasoline audi yes
2 9800 suv 2004 auto 163.0 grand 125000 gasoline jeep no_value
3 1500 small 2001 manual 75.0 golf 150000 petrol volkswagen no
4 3600 small 2008 manual 69.0 fabia 90000 gasoline skoda no
5 650 sedan 1995 manual 102.0 3er 150000 petrol bmw yes
6 2200 convertible 2004 manual 109.0 2_reihe 150000 petrol peugeot no
7 0 no_value 1980 manual 50.0 no_value 40000 no_value volkswagen no
8 14500 bus 2014 manual 125.0 c_max 30000 petrol ford no_value
9 999 small 1998 manual 101.0 golf 150000 no_value volkswagen no_value
In [23]:
# Проверка изменений
data.hist() 
plt.subplots_adjust(wspace=.4, hspace=.5)

data.describe()
Out[23]:
price registration_year power kilometer
count 330174.000000 330174.000000 330174.000000 330174.000000
mean 4540.116554 2002.089226 111.900141 127920.581269
std 4564.387345 6.802931 182.410180 37913.642129
min 0.000000 1910.000000 0.000000 5000.000000
25% 1149.000000 1999.000000 70.000000 125000.000000
50% 2850.000000 2002.000000 105.000000 150000.000000
75% 6500.000000 2007.000000 143.000000 150000.000000
max 20000.000000 2015.000000 20000.000000 150000.000000
2023-08-24T22:28:42.803552 image/svg+xml Matplotlib v3.3.4, https://matplotlib.org/
In [24]:
# Анализ уникальных значений 
# категориальных текстовых признаков
for i in data.select_dtypes(include='object').columns: 
    print(f'Уникальные значения признака "{i}":', data[i].unique()) 
    print(f'Всего унимальных значений признака "{i}":', len(data[i].unique())) 
    print() 

Уникальные значения признака "vehicle_type": ['no_value' 'coupe' 'suv' 'small' 'sedan' 'convertible' 'bus' 'wagon']
Всего унимальных значений признака "vehicle_type": 8

Уникальные значения признака "gearbox": ['manual' 'auto' 'no_value']
Всего унимальных значений признака "gearbox": 3

Уникальные значения признака "model": ['golf' 'no_value' 'grand' 'fabia' '3er' '2_reihe' 'c_max' '3_reihe'
 'passat' 'navara' 'ka' 'twingo' 'a_klasse' 'scirocco' '5er' 'arosa'
 'civic' 'transporter' 'punto' 'e_klasse' 'corsa' 'one' 'fortwo' 'clio'
 '1er' 'b_klasse' 'signum' 'astra' 'a8' 'jetta' 'polo' 'fiesta' 'c_klasse'
 'micra' 'sprinter' '156' 'escort' 'forester' 'xc_reihe' 'scenic' 'a4'
 'a1' 'insignia' 'combo' 'focus' 'tt' 'a6' 'jazz' 'omega' 'slk' '7er' '80'
 '147' '100' 'meriva' 'z_reihe' 'sorento' 'v40' 'ibiza' 'mustang' 'eos'
 'vito' 'touran' 'getz' 'a3' 'megane' 'lupo' 'r19' 'caddy' 'mondeo'
 'cordoba' 'colt' 'impreza' 'vectra' 'berlingo' 'tiguan' 'sharan'
 '6_reihe' 'c4' 'panda' 'up' 'i_reihe' 'ceed' 'kangoo' '5_reihe' 'yeti'
 'octavia' 'zafira' 'mii' 'rx_reihe' '6er' 'fox' 'matiz' 'beetle' 'rio'
 'touareg' 'logan' 'spider' 'cuore' 's_max' 'modus' 'a2' 'galaxy' 'c3'
 'viano' 's_klasse' '1_reihe' 'avensis' 'roomster' 'sl' 'kaefer' 'santa'
 'cooper' 'leon' '4_reihe' 'a5' 'sportage' 'laguna' 'ptcruiser' 'clk'
 'primera' 'espace' 'x_reihe' 'exeo' '159' 'transit' 'juke' 'qashqai'
 'carisma' 'accord' 'corolla' 'lanos' 'phaeton' 'verso' 'swift' 'rav'
 'picanto' 'boxster' 'kalos' 'superb' 'stilo' 'alhambra' 'mx_reihe'
 'roadster' 'ypsilon' 'cayenne' 'galant' 'justy' '90' 'sirion' 'crossfire'
 'agila' 'duster' 'v50' '500' 'c_reihe' 'v_klasse' 'm_klasse' 'yaris' 'c5'
 'aygo' 'almera' 'seicento' 'cc' 'fusion' '911' 'bora' 'forfour' 'm_reihe'
 'cl' 'tigra' '300c' 'cr_reihe' 'spark' 'v70' 'kuga' 'x_type' 'ducato'
 's_type' 'x_trail' 'toledo' 'altea' 'voyager' 'calibra' 'carnival'
 'bravo' 'antara' 'tucson' 'c1' 'kadett' 'citigo' 'jimny' 'wrangler'
 'lybra' 'q7' 'lancer' 'captiva' 'discovery' 'freelander' 'sandero' 'note'
 '900' 'cherokee' 'clubman' 'samara' 'defender' 'cx_reihe' 'legacy' '601'
 'pajero' 'c2' 'niva' 's60' 'nubira' 'vivaro' 'g_klasse' 'auris' 'lodgy'
 '850' 'range_rover' 'q3' 'glk' 'charade' 'croma' 'outlander' 'doblo'
 'musa' 'move' '9000' 'v60' '145' '200' 'b_max' 'range_rover_sport' 'aveo'
 'terios' 'q5' 'range_rover_evoque' 'materia' 'delta' 'gl' 'serie_2'
 'kalina' 'elefantino' 'i3' 'amarok' 'kappa' 'serie_3' 'serie_1']
Всего унимальных значений признака "model": 249

Уникальные значения признака "fuel_type": ['petrol' 'gasoline' 'no_value' 'lpg' 'cng' 'electric' 'hybrid']
Всего унимальных значений признака "fuel_type": 7

Уникальные значения признака "brand": ['volkswagen' 'audi' 'jeep' 'skoda' 'bmw' 'peugeot' 'ford' 'mazda'
 'nissan' 'renault' 'mercedes_benz' 'seat' 'honda' 'fiat' 'opel' 'mini'
 'smart' 'sonstige_autos' 'alfa_romeo' 'subaru' 'volvo' 'mitsubishi' 'kia'
 'hyundai' 'suzuki' 'lancia' 'citroen' 'toyota' 'chevrolet' 'dacia'
 'daihatsu' 'trabant' 'saab' 'chrysler' 'jaguar' 'daewoo' 'porsche'
 'rover' 'land_rover' 'lada']
Всего унимальных значений признака "brand": 40

Уникальные значения признака "repaired": ['no_value' 'yes' 'no']
Всего унимальных значений признака "repaired": 3
In [25]:
# Анализ количества 
# удалённых объектов
print('Всего удалённо объектов:', data_shape - data.shape[0]) 
print(f'Доля удалённых объектов: {(1 - data.shape[0] / data_shape)*100}%') 

Всего удалённо объектов: 24195
Доля удалённых объектов: 6.827628827578036%
In [26]:
# Анализ коррляции признаков 
# после предобработки данных
data.corr()
Out[26]:
price registration_year power kilometer
price 1.000000 0.490673 0.164822 -0.336981
registration_year 0.490673 1.000000 0.067158 -0.220855
power 0.164822 0.067158 1.000000 0.027308
kilometer -0.336981 -0.220855 0.027308 1.000000
In [27]:
# Анализ коррляции признаков 
# после предобработки данных
data.phik_matrix()

interval columns not set, guessing: ['price', 'registration_year', 'power', 'kilometer']
Out[27]:
price vehicle_type registration_year gearbox power model kilometer fuel_type brand repaired
price 1.000000 0.276073 0.609296 0.305974 0.005928 0.567335 0.311190 0.263280 0.356071 0.366852
vehicle_type 0.276073 1.000000 0.209775 0.336422 0.005379 0.942273 0.164271 0.573069 0.644883 0.207384
registration_year 0.609296 0.209775 1.000000 0.146034 0.000000 0.574288 0.307834 0.274391 0.356596 0.235890
gearbox 0.305974 0.336422 0.146034 1.000000 0.008489 0.626205 0.070258 0.282519 0.523394 0.482828
power 0.005928 0.005379 0.000000 0.008489 1.000000 0.000000 0.000000 0.000000 0.002184 0.013631
model 0.567335 0.942273 0.574288 0.626205 0.000000 1.000000 0.437192 0.711109 0.997654 0.281206
kilometer 0.311190 0.164271 0.307834 0.070258 0.000000 0.437192 1.000000 0.137378 0.276410 0.226748
fuel_type 0.263280 0.573069 0.274391 0.282519 0.000000 0.711109 0.137378 1.000000 0.355297 0.194107
brand 0.356071 0.644883 0.356596 0.523394 0.002184 0.997654 0.276410 0.355297 1.000000 0.164810
repaired 0.366852 0.207384 0.235890 0.482828 0.013631 0.281206 0.226748 0.194107 0.164810 1.000000

Выводы из преварительной добработки данных

Датафрейм был успешно оптимизирован и подготовлен к использованию в машинном обучении:

  1. Все неопределенные значения во всех признаках заменены на no_value.
  2. Удалены неинформативные признаки DateCrawled, RegistrationMonth, DateCreated, NumberOfPictures, PostalCode, LastSeen.
  3. Удален 171 объект с датой регистрации автомобиля в признаке RegistrationYear меньше 1900 года и старше 2023 года.
  4. Заменены значения признака Power с мощностью двигателя менее 0.75 л.с. и более 5000 на медианные значения групп Brand + Model.
  5. Изменены неявно совпадающие значения range_rover и rangerover признака Model на значение range_rover.
  6. Названия признаков приведены к «змеиному» стилю.

После предвартиельной обработки данных все числовые признаки имеют среднюю и слабую корреляцию между собой и с целевым признаком. Наибольшей прямой корреляцией с целевым признаком обладает признак registration_year, а обратной корреляцией kilometer. После предварительной обработки данных стало очевидным то, что требуется все категориальные признаки кодировать с помощью технологии One Hot Encoding.

Обучение моделей¶

Полезные функции подготовки данных и подбора моделей и их параметров¶

In [28]:
# Функция для кодирования категориальных текстовых признаков
# с помощью технологии One Hot Encoding (pd.get_dummies())
def features_get_dummies(features, column_name):
    features = features.join(
        pd.get_dummies(
            data[column_name], 
            prefix=column_name, 
            prefix_sep='_', 
            drop_first=True
        )
    )
    features = features.drop(column_name, axis=1)
    return features
In [29]:
# Функция подготовки данных перед подбором моделей и их параметров
def data_preprocessing(data, target_name):
    
    # Перемешивание объектов 
    # для их лучшего распределения в выборках
    data = shuffle(data, random_state=STATE)
    
    # Разделение датафреймов на целевую и нецелевую выборку
    features = data.drop([target_name], axis=1)
    target = data[target_name]

    # Разделение целевой и нецелевой выборки 
    # на обучающие и тестовые выборки
    features_train,  features_test, target_train, target_test = train_test_split(
        features, 
        target, 
        test_size=.25, 
        random_state=STATE
    )
    
    return features_train,  features_test, target_train, target_test
In [30]:
# Функция создания структуры пайплайна
def params_and_model_selection(
    features_train, 
    features_test, 
    target_train, 
    target_test, 
    model_params
):
    
    start_time = time.time()
    #funtion_time = %timeit
    
    # Стандартизация числовых значений
    numeric_transformer = make_pipeline(
        StandardScaler()
    )
    
    # Шаг препроцессинга в Пайплайне
    preprocessor = make_column_transformer(
        (numeric_transformer, features_train.columns)
    )
    
    # Pipeline
    pipe = Pipeline([
        ('preprocessor', preprocessor), 
        ('regressor', model_params[0]['regressor'][0])
    ])
    pipe.fit(features_train, target_train)
    #predict = pipe.predict(features_test) # спорно, т.к. это должно быть в результате, а здесь должна использоваться валидационная выборка
    
    # Передача функции ошибки через make_scorer в HalvingGridSearchCV
    smape_score = make_scorer(
        mean_squared_error, 
        squared=False # Для RMSE
    )
    
    # HalvingGridSearchCV
    # (о подборе оптимальных параметров:
    # https://scikit-learn.ru/3-2-tuning-the-hyper-parameters-of-an-estimator/)
    #grid = HalvingRandomSearchCV(
    grid = HalvingGridSearchCV(
        pipe, 
        model_params, 
        cv=4, # параметр KFold для кроссвалидации (обучющая и валидационная выборки 75:25)
        n_jobs=-1, # количество параллельно выполняемых заданий (-1 - задействованы все процессоры)
        scoring=smape_score, # Передача функции ошибки через make_scorer в HalvingGridSearchCV
        error_score='raise', #0 , # 
        random_state=STATE
    )
    grid.fit(features_train, target_train)
    
    finish_time = time.time()
    funtion_time = finish_time - start_time
    
    return grid, funtion_time
In [31]:
# Вывод на печать результатов модели
def print_model_result(grids, data_times, model_name):
    print('Модель   :', model_name)
    print('RMSE     :', grids[-1].best_score_)
    print(f'Время    : {data_times[-1]} секунд')
    print('Параметры:\n', grids[-1].best_estimator_)
    print()
    print('-'*20)
    print()

Функции моделей¶

In [32]:
# LinearRegression
def grids_LinearRegression(
    features_train, 
    features_test, 
    target_train, 
    target_test, 
    grids, 
    data_times
):
    
    grid, time_best = params_and_model_selection(
        features_train, 
        features_test, 
        target_train, 
        target_test, 
        [{
            'regressor': [LinearRegression()] # score: R^2
        }]
    )
    
    grids.append(grid)
    data_times.append(time_best)
    
    return grids, data_times
In [33]:
# DecisionTreeRegressor
def grids_DecisionTreeRegressor(
    features_train, 
    features_test, 
    target_train, 
    target_test, 
    grids, 
    data_times
):
    
    grids_this = 0
    grids_best = 0
    funtion_time = 0
    time_best = 0
    
    # Поиск "regressor__max_depth"
    
    range_min = 1
    range_max = 201
    range_step = 20
    
    for i in range(1, 5, 1):
        # Поиск лучших параметров
        grids_this, funtion_time = params_and_model_selection(
            features_train, 
            features_test, 
            target_train, 
            target_test, 
            [{
                'regressor': [DecisionTreeRegressor(random_state=STATE)], # score: R^2
                'regressor__max_depth': range(
                    range_min, 
                    range_max, 
                    range_step
                )
            }]
        )
        # Выбор лучшей модели
        if grids_best == 0:
            grids_best = grids_this
            time_best = funtion_time
        elif grids_this.best_score_ > grids_best.best_score_:
            grids_best = grids_this
            time_best = funtion_time
        if range_step == 1: break
        # Выбор параметров поиска
        regressor__max_depth = grids_this.best_params_['regressor__max_depth']
        if int(regressor__max_depth - range_step / 2) > 0:
            range_min = int(regressor__max_depth - range_step / 2)
        else:
            range_min = regressor__max_depth
        range_max = int(regressor__max_depth + range_step / 2) + 1
        range_step = int(range_step / 2)
    
    grids.append(grids_best)
    data_times.append(time_best)
    
    return grids, data_times
In [34]:
# RandomForestRegressor
def grids_RandomForestRegressor(
    features_train, 
    features_test, 
    target_train, 
    target_test, 
    grids, 
    data_times
):
    
    grids_this = 0
    grids_best = 0
    funtion_time = 0
    time_best = 0
    
    # Поиск "regressor__max_depth"
    
    range_min = 20
    range_max = 61
    range_step = 20
    
    for i in range(1, 5, 1):
        
        # Поиск лучших параметров
        grids_this, funtion_time = params_and_model_selection(
            features_train, 
            features_test, 
            target_train, 
            target_test, 
            [{
                'regressor': [RandomForestRegressor(random_state=STATE)], # score: R^2
                'regressor__max_depth': range(
                    range_min, 
                    range_max, 
                    range_step
                ), 
                'regressor__n_estimators': [1]
            }]
        )
        # Выбор лучшей модели
        if grids_best == 0:
            grids_best = grids_this
            time_best = funtion_time
        elif grids_this.best_score_ > grids_best.best_score_: 
            grids_best = grids_this
            time_best = funtion_time
        if range_step == 1: break
        # Выбор параметров поиска
        regressor__max_depth = grids_this.best_params_['regressor__max_depth']
        if int(regressor__max_depth - range_step / 2) > 0:
            range_min = int(regressor__max_depth - range_step / 2)
        else:
            range_min = regressor__max_depth
        range_max = int(regressor__max_depth + range_step / 2) + 1
        range_step = int(range_step / 2)
        if range_step == 0: range_step = 1
    
    # Поиск "regressor__n_estimators"
    
    range_min = 10
    range_max = 31
    range_step = 10
    
    for i in range(1, 5, 1):
        
        # Поиск лучших параметров
        grids_this, funtion_time = params_and_model_selection(
            features_train, 
            features_test, 
            target_train, 
            target_test, 
            [{
                'regressor': [RandomForestRegressor(random_state=STATE)], # score: R^2
                'regressor__max_depth': [regressor__max_depth], 
                'regressor__n_estimators': range(
                    range_min, 
                    range_max, 
                    range_step
                )
            }]
        )
        # Выбор лучшей модели
        if grids_best == 0: 
            grids_best = grids_this
            time_best = funtion_time
        elif grids_this.best_score_ > grids_best.best_score_: 
            grids_best = grids_this
            time_best = funtion_time
        if range_step == 1: break
        # Выбор параметров поиска
        regressor__n_estimators = grids_this.best_params_['regressor__n_estimators']
        if int(regressor__n_estimators - range_step / 2) > 0:
            range_min = int(regressor__n_estimators - range_step / 2)
        else:
            range_min = regressor__n_estimators
        range_max = int(regressor__n_estimators + range_step / 2) + 1
        range_step = int(range_step / 10)
        if range_step == 0: range_step = 1
    
    grids.append(grids_best)
    data_times.append(time_best)
    
    return grids, data_times
In [35]:
# SGDRegressor
def grids_SGDRegressor(
    features_train, 
    features_test, 
    target_train, 
    target_test, 
    grids, 
    data_times
):
    
    # Поиск лучших параметров
    
    grids_best, time_best = params_and_model_selection(
        features_train, 
        features_test, 
        target_train, 
        target_test, 
        [{
            'regressor': [SGDRegressor()]
        }]
    )
    
    grids.append(grids_best)
    data_times.append(time_best)
    
    return grids, data_times
In [36]:
# MLPRegressor
def grids_MLPRegressor(
    features_train, 
    features_test, 
    target_train, 
    target_test, 
    grids, 
    data_times
):
    
    # Поиск лучших параметров
    
    grids_best, time_best = params_and_model_selection(
        features_train, 
        features_test, 
        target_train, 
        target_test, 
        [{
            'regressor': [MLPRegressor()]
        }]
    )
    
    grids.append(grids_best)
    data_times.append(time_best)
    
    return grids, data_times
In [37]:
# CatBoostRegressor
def grids_CatBoostRegressor(
    features_train, 
    features_test, 
    target_train, 
    target_test, 
    grids, 
    data_times
):
    
    
    grids_best, time_best = params_and_model_selection(
        features_train, 
        features_test, 
        target_train, 
        target_test, 
        [{
            'regressor': [CatBoostRegressor()]
        }]
    )
    
    
    grids.append(grids_best)
    data_times.append(time_best)
    
    return grids, data_times
In [38]:
# LGBMRegressor
def grids_LGBMRegressor(
    features_train, 
    features_test, 
    target_train, 
    target_test, 
    grids, 
    data_times
):
    
    # Поиск лучших параметров
    
    grids_this = 0
    grids_best = 0
    funtion_time = 0
    time_best = 0
    
    # Поиск "regressor__max_depth"
    
    range_min = 1
    range_max = 201
    range_step = 20
    
    for i in range(1, 5, 1):
        
        #print('regressor__max_depth =', range(range_min, range_max, range_step))
        
        # Поиск лучших параметров
        grids_this, funtion_time = params_and_model_selection(
            features_train, 
            features_test, 
            target_train, 
            target_test, 
            [{
                'regressor': [LGBMRegressor(random_state=STATE)], # score: R^2
                'regressor__max_depth': range(
                    range_min, 
                    range_max, 
                    range_step
                ), 
                'regressor__n_estimators': [1]
            }]
        )
        # Выбор лучшей модели
        if grids_best == 0: 
            grids_best = grids_this
            time_best = funtion_time
        elif grids_this.best_score_ > grids_best.best_score_: 
            grids_best = grids_this
            time_best = funtion_time
        if range_step == 1: break
        # Выбор параметров поиска
        regressor__max_depth = grids_this.best_params_['regressor__max_depth']
        if int(regressor__max_depth - range_step / 2) > 0:
            range_min = int(regressor__max_depth - range_step / 2)
        else:
            range_min = regressor__max_depth
        range_max = int(regressor__max_depth + range_step / 2) + 1
        range_step = int(range_step / 2)
        if range_step == 0: range_step = 1
    
    # Поиск "regressor__n_estimators"
    
    range_min = 1
    range_max = 51
    range_step = 10
    
    for i in range(1, 5, 1):
        
        #print('regressor__n_estimators =', range(range_min, range_max, range_step))
        
        # Поиск лучших параметров
        grids_this, funtion_time = params_and_model_selection(
            features_train, 
            features_test, 
            target_train, 
            target_test, 
            [{
                'regressor': [LGBMRegressor(random_state=STATE)], # score: R^2
                'regressor__max_depth': [regressor__max_depth], 
                'regressor__n_estimators': range(
                    range_min, 
                    range_max, 
                    range_step
                )
            }]
        )
        # Выбор лучшей модели
        if grids_best == 0: 
            grids_best = grids_this
            time_best = funtion_time
        elif grids_this.best_score_ > grids_best.best_score_: 
            grids_best = grids_this
            time_best = funtion_time
        if range_step == 1: break
        # Выбор параметров поиска
        regressor__n_estimators = grids_this.best_params_['regressor__n_estimators']
        if int(regressor__n_estimators - range_step / 2) > 0:
            range_min = int(regressor__n_estimators - range_step / 2)
        else:
            range_min = regressor__n_estimators
        range_max = int(regressor__n_estimators + range_step / 2) + 1
        range_step = int(range_step / 10)
        if range_step == 0: range_step = 1
    
    grids.append(grids_best)
    data_times.append(time_best)
    
    return grids, data_times

Применение функций¶

In [39]:
# Подготовка выборок из датафрейма

# Разделение обучающего датафрейма на целевую и нецелевую выборку
features_train,  features_test, target_train, target_test = data_preprocessing(data, 'price')

# Кодирование категориальных текстовых признаков
# с помощью технологии TargetEncoder 
features_encoding = ['vehicle_type', 'gearbox', 'model', 'fuel_type', 'brand', 'repaired']
te_fit = TargetEncoder().fit(features_train[features_encoding], target_train)
features_train[features_encoding] = te_fit.transform(features_train[features_encoding])
features_test[features_encoding] = te_fit.transform(features_test[features_encoding])

print(features_train.info())
features_train.head()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 247630 entries, 223244 to 257691
Data columns (total 9 columns):
 #   Column             Non-Null Count   Dtype  
---  ------             --------------   -----  
 0   vehicle_type       247630 non-null  float64
 1   registration_year  247630 non-null  int64  
 2   gearbox            247630 non-null  float64
 3   power              247630 non-null  float64
 4   model              247630 non-null  float64
 5   kilometer          247630 non-null  int64  
 6   fuel_type          247630 non-null  float64
 7   brand              247630 non-null  float64
 8   repaired           247630 non-null  float64
dtypes: float64(7), int64(2)
memory usage: 18.9 MB
None
Out[39]:
vehicle_type registration_year gearbox power model kilometer fuel_type brand repaired
223244 4759.405926 2005 6967.682167 190.0 5767.135775 150000 6756.042179 6010.171569 5385.242357
98910 3540.618365 2000 2270.272120 0.0 3793.928198 150000 3394.617204 3261.835992 2672.054560
62348 6827.486197 2010 4081.965033 120.0 3223.449143 80000 3807.829533 3147.120726 5385.242357
318127 4759.405926 2009 4081.965033 143.0 5890.414408 125000 6756.042179 6384.934049 5385.242357
290228 4759.405926 1993 4081.965033 45.0 2604.409468 150000 3394.617204 4530.331913 5385.242357
In [40]:
# Поиск лучших моделей и их параметров
data_grids = []
data_times = []
In [41]:
# LinearRegression (dummy-model)
data_grids, data_times = grids_LinearRegression(
    features_train, 
    features_test, 
    target_train, 
    target_test, 
    data_grids, 
    data_times
)
print_model_result(data_grids, data_times, 'LinearRegression')

Модель   : LinearRegression
RMSE     : 3069.23279496429
Время    : 2.045431137084961 секунд
Параметры:
 Pipeline(steps=[('preprocessor',
                 ColumnTransformer(transformers=[('pipeline',
                                                  Pipeline(steps=[('standardscaler',
                                                                   StandardScaler())]),
                                                  Index(['vehicle_type', 'registration_year', 'gearbox', 'power', 'model',
       'kilometer', 'fuel_type', 'brand', 'repaired'],
      dtype='object'))])),
                ('regressor', LinearRegression())])

--------------------
In [42]:
# DecisionTreeRegressor
data_grids, data_times = grids_DecisionTreeRegressor(
    features_train, 
    features_test, 
    target_train, 
    target_test, 
    data_grids, 
    data_times
)
print_model_result(data_grids, data_times, 'DecisionTreeRegressor')

Модель   : DecisionTreeRegressor
RMSE     : 3520.5951256556477
Время    : 14.969941139221191 секунд
Параметры:
 Pipeline(steps=[('preprocessor',
                 ColumnTransformer(transformers=[('pipeline',
                                                  Pipeline(steps=[('standardscaler',
                                                                   StandardScaler())]),
                                                  Index(['vehicle_type', 'registration_year', 'gearbox', 'power', 'model',
       'kilometer', 'fuel_type', 'brand', 'repaired'],
      dtype='object'))])),
                ('regressor',
                 DecisionTreeRegressor(max_depth=1, random_state=42))])

--------------------
In [44]:
# SGDRegressor
data_grids, data_times = grids_SGDRegressor(
    features_train, 
    features_test, 
    target_train, 
    target_test, 
    data_grids, 
    data_times
)
print_model_result(data_grids, data_times, 'SGDRegressor')

Модель   : SGDRegressor
RMSE     : 3089.779746424787
Время    : 5.435046195983887 секунд
Параметры:
 Pipeline(steps=[('preprocessor',
                 ColumnTransformer(transformers=[('pipeline',
                                                  Pipeline(steps=[('standardscaler',
                                                                   StandardScaler())]),
                                                  Index(['vehicle_type', 'registration_year', 'gearbox', 'power', 'model',
       'kilometer', 'fuel_type', 'brand', 'repaired'],
      dtype='object'))])),
                ('regressor', SGDRegressor())])

--------------------
In [46]:
# CatBoostRegressor
data_grids, data_times = grids_CatBoostRegressor(
    features_train, 
    features_test, 
    target_train, 
    target_test, 
    data_grids, 
    data_times
)
print_model_result(data_grids, data_times, 'CatBoostRegressor')

Learning rate set to 0.097814
0:	learn: 4268.9324355	total: 101ms	remaining: 1m 41s
1:	learn: 4010.6410460	total: 138ms	remaining: 1m 8s
2:	learn: 3784.1669481	total: 181ms	remaining: 1m
3:	learn: 3579.5232253	total: 218ms	remaining: 54.4s
4:	learn: 3400.3400769	total: 255ms	remaining: 50.7s
5:	learn: 3242.2229009	total: 299ms	remaining: 49.5s
6:	learn: 3101.0951468	total: 336ms	remaining: 47.6s
7:	learn: 2976.0448761	total: 382ms	remaining: 47.3s
8:	learn: 2865.0184049	total: 418ms	remaining: 46s
9:	learn: 2769.5971830	total: 455ms	remaining: 45s
10:	learn: 2688.7300008	total: 496ms	remaining: 44.6s
11:	learn: 2611.4192577	total: 533ms	remaining: 43.8s
12:	learn: 2547.1626659	total: 581ms	remaining: 44.1s
13:	learn: 2486.4814447	total: 618ms	remaining: 43.5s
14:	learn: 2436.7976506	total: 659ms	remaining: 43.3s
15:	learn: 2388.2476102	total: 698ms	remaining: 42.9s
16:	learn: 2349.1750890	total: 734ms	remaining: 42.4s
17:	learn: 2311.5397711	total: 777ms	remaining: 42.4s
18:	learn: 2279.0709995	total: 812ms	remaining: 41.9s
19:	learn: 2249.5200641	total: 853ms	remaining: 41.8s
20:	learn: 2225.0638599	total: 896ms	remaining: 41.8s
21:	learn: 2202.5869378	total: 939ms	remaining: 41.7s
22:	learn: 2181.3138603	total: 989ms	remaining: 42s
23:	learn: 2162.7987170	total: 1.03s	remaining: 41.9s
24:	learn: 2146.4960625	total: 1.08s	remaining: 42s
25:	learn: 2130.0284629	total: 1.11s	remaining: 41.8s
26:	learn: 2114.8175590	total: 1.16s	remaining: 41.9s
27:	learn: 2103.0645814	total: 1.21s	remaining: 41.9s
28:	learn: 2092.1972325	total: 1.25s	remaining: 42s
29:	learn: 2082.0707635	total: 1.29s	remaining: 41.8s
30:	learn: 2072.5199001	total: 1.33s	remaining: 41.7s
31:	learn: 2061.9256277	total: 1.38s	remaining: 41.6s
32:	learn: 2053.0818270	total: 1.41s	remaining: 41.3s
33:	learn: 2045.8192642	total: 1.45s	remaining: 41.2s
34:	learn: 2038.9822441	total: 1.49s	remaining: 41s
35:	learn: 2033.6254723	total: 1.52s	remaining: 40.7s
36:	learn: 2028.4506164	total: 1.57s	remaining: 40.8s
37:	learn: 2020.3179117	total: 1.6s	remaining: 40.6s
38:	learn: 2015.5523488	total: 1.64s	remaining: 40.4s
39:	learn: 2010.3228599	total: 1.68s	remaining: 40.2s
40:	learn: 2004.3267459	total: 1.72s	remaining: 40.2s
41:	learn: 1998.5867855	total: 1.76s	remaining: 40.3s
42:	learn: 1994.6349425	total: 1.8s	remaining: 40.1s
43:	learn: 1991.2172038	total: 1.84s	remaining: 40s
44:	learn: 1988.5022244	total: 1.88s	remaining: 39.9s
45:	learn: 1984.3815351	total: 1.92s	remaining: 39.8s
46:	learn: 1978.7809852	total: 1.96s	remaining: 39.8s
47:	learn: 1976.1214547	total: 2s	remaining: 39.6s
48:	learn: 1972.7190666	total: 2.04s	remaining: 39.5s
49:	learn: 1968.6779676	total: 2.07s	remaining: 39.4s
50:	learn: 1963.1907207	total: 2.11s	remaining: 39.2s
51:	learn: 1960.2988615	total: 2.15s	remaining: 39.2s
52:	learn: 1957.8772666	total: 2.18s	remaining: 39s
53:	learn: 1955.8311334	total: 2.22s	remaining: 39s
54:	learn: 1953.0846871	total: 2.26s	remaining: 38.8s
55:	learn: 1950.5234577	total: 2.29s	remaining: 38.6s
56:	learn: 1946.2558032	total: 2.34s	remaining: 38.7s
57:	learn: 1943.7185580	total: 2.37s	remaining: 38.5s
58:	learn: 1941.8747937	total: 2.41s	remaining: 38.4s
59:	learn: 1938.4969708	total: 2.45s	remaining: 38.4s
60:	learn: 1935.7775907	total: 2.48s	remaining: 38.2s
61:	learn: 1934.4810527	total: 2.52s	remaining: 38.1s
62:	learn: 1931.2776221	total: 2.55s	remaining: 38s
63:	learn: 1929.5589586	total: 2.59s	remaining: 37.8s
64:	learn: 1926.3388558	total: 2.63s	remaining: 37.9s
65:	learn: 1923.6750746	total: 2.66s	remaining: 37.7s
66:	learn: 1922.5330800	total: 2.69s	remaining: 37.5s
67:	learn: 1920.8281168	total: 2.74s	remaining: 37.6s
68:	learn: 1918.3175340	total: 2.77s	remaining: 37.4s
69:	learn: 1915.7574885	total: 2.82s	remaining: 37.4s
70:	learn: 1913.6291187	total: 2.85s	remaining: 37.3s
71:	learn: 1911.3063450	total: 2.88s	remaining: 37.2s
72:	learn: 1910.0146675	total: 2.92s	remaining: 37.1s
73:	learn: 1908.5637534	total: 2.96s	remaining: 37s
74:	learn: 1906.4780092	total: 2.99s	remaining: 36.9s
75:	learn: 1905.1691680	total: 3.03s	remaining: 36.9s
76:	learn: 1903.1997125	total: 3.06s	remaining: 36.7s
77:	learn: 1901.8821513	total: 3.1s	remaining: 36.7s
78:	learn: 1900.1337731	total: 3.14s	remaining: 36.6s
79:	learn: 1899.1992384	total: 3.17s	remaining: 36.5s
80:	learn: 1897.3895928	total: 3.21s	remaining: 36.4s
81:	learn: 1895.8139714	total: 3.24s	remaining: 36.3s
82:	learn: 1893.6768162	total: 3.28s	remaining: 36.2s
83:	learn: 1891.4986383	total: 3.32s	remaining: 36.2s
84:	learn: 1889.8489640	total: 3.36s	remaining: 36.2s
85:	learn: 1888.8394635	total: 3.4s	remaining: 36.1s
86:	learn: 1887.7631764	total: 3.43s	remaining: 36s
87:	learn: 1886.0409055	total: 3.47s	remaining: 35.9s
88:	learn: 1884.4447070	total: 3.51s	remaining: 35.9s
89:	learn: 1882.9561620	total: 3.54s	remaining: 35.8s
90:	learn: 1881.6691320	total: 3.58s	remaining: 35.8s
91:	learn: 1879.9898022	total: 3.63s	remaining: 35.8s
92:	learn: 1879.0661497	total: 3.66s	remaining: 35.7s
93:	learn: 1877.3728090	total: 3.71s	remaining: 35.7s
94:	learn: 1876.1311891	total: 3.74s	remaining: 35.7s
95:	learn: 1874.7015338	total: 3.78s	remaining: 35.6s
96:	learn: 1873.5465573	total: 3.82s	remaining: 35.6s
97:	learn: 1872.7624631	total: 3.85s	remaining: 35.5s
98:	learn: 1870.7938629	total: 3.9s	remaining: 35.5s
99:	learn: 1869.2673785	total: 3.93s	remaining: 35.4s
100:	learn: 1867.9182654	total: 3.97s	remaining: 35.4s
101:	learn: 1866.6275050	total: 4.02s	remaining: 35.4s
102:	learn: 1864.8356438	total: 4.05s	remaining: 35.3s
103:	learn: 1863.8575595	total: 4.09s	remaining: 35.3s
104:	learn: 1862.9924644	total: 4.13s	remaining: 35.2s
105:	learn: 1862.4445378	total: 4.16s	remaining: 35.1s
106:	learn: 1861.5129977	total: 4.2s	remaining: 35.1s
107:	learn: 1859.9204173	total: 4.24s	remaining: 35s
108:	learn: 1858.7382351	total: 4.28s	remaining: 35s
109:	learn: 1857.3892643	total: 4.31s	remaining: 34.9s
110:	learn: 1856.6283950	total: 4.35s	remaining: 34.8s
111:	learn: 1855.4247124	total: 4.39s	remaining: 34.8s
112:	learn: 1854.3838611	total: 4.43s	remaining: 34.8s
113:	learn: 1853.5954063	total: 4.46s	remaining: 34.7s
114:	learn: 1852.6219831	total: 4.51s	remaining: 34.7s
115:	learn: 1851.9004090	total: 4.55s	remaining: 34.7s
116:	learn: 1851.2159787	total: 4.59s	remaining: 34.6s
117:	learn: 1850.5915299	total: 4.62s	remaining: 34.5s
118:	learn: 1849.8976949	total: 4.65s	remaining: 34.5s
119:	learn: 1848.8652075	total: 4.7s	remaining: 34.4s
120:	learn: 1847.8232751	total: 4.73s	remaining: 34.4s
121:	learn: 1847.2230104	total: 4.77s	remaining: 34.3s
122:	learn: 1846.5371880	total: 4.8s	remaining: 34.3s
123:	learn: 1845.8262919	total: 4.84s	remaining: 34.2s
124:	learn: 1845.3827261	total: 4.88s	remaining: 34.1s
125:	learn: 1844.6014292	total: 4.91s	remaining: 34.1s
126:	learn: 1843.7578276	total: 4.95s	remaining: 34s
127:	learn: 1843.0869271	total: 4.99s	remaining: 34s
128:	learn: 1841.9619538	total: 5.02s	remaining: 33.9s
129:	learn: 1841.2991078	total: 5.06s	remaining: 33.9s
130:	learn: 1840.4296282	total: 5.1s	remaining: 33.8s
131:	learn: 1839.1957353	total: 5.13s	remaining: 33.7s
132:	learn: 1838.1950594	total: 5.17s	remaining: 33.7s
133:	learn: 1837.3731522	total: 5.21s	remaining: 33.6s
134:	learn: 1836.7928530	total: 5.24s	remaining: 33.6s
135:	learn: 1835.7954051	total: 5.28s	remaining: 33.6s
136:	learn: 1834.9445079	total: 5.31s	remaining: 33.5s
137:	learn: 1834.0497983	total: 5.36s	remaining: 33.5s
138:	learn: 1833.0138969	total: 5.39s	remaining: 33.4s
139:	learn: 1832.2018222	total: 5.42s	remaining: 33.3s
140:	learn: 1831.6937920	total: 5.46s	remaining: 33.3s
141:	learn: 1831.1211248	total: 5.49s	remaining: 33.2s
142:	learn: 1830.3238911	total: 5.53s	remaining: 33.1s
143:	learn: 1829.7812267	total: 5.57s	remaining: 33.1s
144:	learn: 1829.0570199	total: 5.6s	remaining: 33s
145:	learn: 1828.4505519	total: 5.64s	remaining: 33s
146:	learn: 1828.0967705	total: 5.68s	remaining: 33s
147:	learn: 1827.0128422	total: 5.72s	remaining: 32.9s
148:	learn: 1826.1013723	total: 5.76s	remaining: 32.9s
149:	learn: 1825.4182648	total: 5.8s	remaining: 32.9s
150:	learn: 1824.6176075	total: 5.84s	remaining: 32.8s
151:	learn: 1823.8060667	total: 5.88s	remaining: 32.8s
152:	learn: 1823.1678913	total: 5.92s	remaining: 32.7s
153:	learn: 1822.7543456	total: 5.95s	remaining: 32.7s
154:	learn: 1822.1850611	total: 5.98s	remaining: 32.6s
155:	learn: 1821.5692808	total: 6.03s	remaining: 32.6s
156:	learn: 1820.9095442	total: 6.06s	remaining: 32.6s
157:	learn: 1820.4462638	total: 6.09s	remaining: 32.5s
158:	learn: 1819.8860522	total: 6.14s	remaining: 32.5s
159:	learn: 1819.2441115	total: 6.17s	remaining: 32.4s
160:	learn: 1818.4616216	total: 6.22s	remaining: 32.4s
161:	learn: 1817.9427457	total: 6.26s	remaining: 32.4s
162:	learn: 1817.4403200	total: 6.29s	remaining: 32.3s
163:	learn: 1816.9666062	total: 6.33s	remaining: 32.3s
164:	learn: 1816.2439721	total: 6.37s	remaining: 32.2s
165:	learn: 1815.8090863	total: 6.41s	remaining: 32.2s
166:	learn: 1815.3273199	total: 6.44s	remaining: 32.1s
167:	learn: 1814.5158132	total: 6.48s	remaining: 32.1s
168:	learn: 1814.1986002	total: 6.52s	remaining: 32.1s
169:	learn: 1813.6626373	total: 6.56s	remaining: 32s
170:	learn: 1812.9822789	total: 6.61s	remaining: 32s
171:	learn: 1812.5582427	total: 6.64s	remaining: 32s
172:	learn: 1811.8354672	total: 6.67s	remaining: 31.9s
173:	learn: 1811.3725020	total: 6.71s	remaining: 31.9s
174:	learn: 1810.8608786	total: 6.75s	remaining: 31.8s
175:	learn: 1810.2449498	total: 6.79s	remaining: 31.8s
176:	learn: 1809.6423035	total: 6.83s	remaining: 31.7s
177:	learn: 1809.2480727	total: 6.86s	remaining: 31.7s
178:	learn: 1808.6675072	total: 6.91s	remaining: 31.7s
179:	learn: 1808.0039389	total: 6.94s	remaining: 31.6s
180:	learn: 1807.6046747	total: 6.98s	remaining: 31.6s
181:	learn: 1807.3519822	total: 7.01s	remaining: 31.5s
182:	learn: 1806.8990466	total: 7.05s	remaining: 31.5s
183:	learn: 1806.2886441	total: 7.1s	remaining: 31.5s
184:	learn: 1805.5854407	total: 7.14s	remaining: 31.5s
185:	learn: 1805.0783816	total: 7.18s	remaining: 31.4s
186:	learn: 1804.5242344	total: 7.22s	remaining: 31.4s
187:	learn: 1804.3149081	total: 7.25s	remaining: 31.3s
188:	learn: 1803.7452142	total: 7.29s	remaining: 31.3s
189:	learn: 1803.4434408	total: 7.33s	remaining: 31.2s
190:	learn: 1803.1064225	total: 7.37s	remaining: 31.2s
191:	learn: 1802.6029852	total: 7.4s	remaining: 31.2s
192:	learn: 1802.1195912	total: 7.44s	remaining: 31.1s
193:	learn: 1801.6937097	total: 7.48s	remaining: 31.1s
194:	learn: 1801.2427150	total: 7.51s	remaining: 31s
195:	learn: 1800.9240433	total: 7.55s	remaining: 31s
196:	learn: 1800.5739351	total: 7.59s	remaining: 31s
197:	learn: 1800.1988974	total: 7.63s	remaining: 30.9s
198:	learn: 1799.8508234	total: 7.67s	remaining: 30.9s
199:	learn: 1799.1884678	total: 7.71s	remaining: 30.8s
200:	learn: 1798.5902332	total: 7.74s	remaining: 30.8s
201:	learn: 1798.0212189	total: 7.78s	remaining: 30.7s
202:	learn: 1797.7243252	total: 7.82s	remaining: 30.7s
203:	learn: 1797.0336469	total: 7.86s	remaining: 30.7s
204:	learn: 1796.3191776	total: 7.9s	remaining: 30.6s
205:	learn: 1795.9519571	total: 7.93s	remaining: 30.6s
206:	learn: 1795.5950900	total: 7.97s	remaining: 30.5s
207:	learn: 1795.2389059	total: 8.01s	remaining: 30.5s
208:	learn: 1794.7079839	total: 8.04s	remaining: 30.4s
209:	learn: 1794.1596165	total: 8.08s	remaining: 30.4s
210:	learn: 1793.7831297	total: 8.12s	remaining: 30.3s
211:	learn: 1793.4907585	total: 8.15s	remaining: 30.3s
212:	learn: 1792.9769478	total: 8.19s	remaining: 30.2s
213:	learn: 1792.5382337	total: 8.22s	remaining: 30.2s
214:	learn: 1791.9538646	total: 8.26s	remaining: 30.2s
215:	learn: 1791.2988125	total: 8.3s	remaining: 30.1s
216:	learn: 1790.8879168	total: 8.33s	remaining: 30.1s
217:	learn: 1790.5466705	total: 8.37s	remaining: 30s
218:	learn: 1790.1821375	total: 8.41s	remaining: 30s
219:	learn: 1789.8022776	total: 8.45s	remaining: 29.9s
220:	learn: 1789.3897690	total: 8.48s	remaining: 29.9s
221:	learn: 1789.2006412	total: 8.51s	remaining: 29.8s
222:	learn: 1788.6142665	total: 8.56s	remaining: 29.8s
223:	learn: 1788.1002454	total: 8.6s	remaining: 29.8s
224:	learn: 1787.6531289	total: 8.64s	remaining: 29.8s
225:	learn: 1787.2842224	total: 8.68s	remaining: 29.7s
226:	learn: 1786.9077368	total: 8.71s	remaining: 29.6s
227:	learn: 1786.4275520	total: 8.75s	remaining: 29.6s
228:	learn: 1786.1205733	total: 8.78s	remaining: 29.6s
229:	learn: 1785.6904303	total: 8.82s	remaining: 29.5s
230:	learn: 1785.3968018	total: 8.86s	remaining: 29.5s
231:	learn: 1784.9782433	total: 8.89s	remaining: 29.4s
232:	learn: 1784.6855910	total: 8.94s	remaining: 29.4s
233:	learn: 1784.2238292	total: 8.97s	remaining: 29.4s
234:	learn: 1783.9608709	total: 9s	remaining: 29.3s
235:	learn: 1783.6271962	total: 9.04s	remaining: 29.3s
236:	learn: 1783.2335930	total: 9.07s	remaining: 29.2s
237:	learn: 1782.7868410	total: 9.11s	remaining: 29.2s
238:	learn: 1782.5721117	total: 9.15s	remaining: 29.1s
239:	learn: 1781.9219510	total: 9.18s	remaining: 29.1s
240:	learn: 1781.7145898	total: 9.22s	remaining: 29s
241:	learn: 1781.3611394	total: 9.26s	remaining: 29s
242:	learn: 1781.1198496	total: 9.29s	remaining: 28.9s
243:	learn: 1780.6603243	total: 9.33s	remaining: 28.9s
244:	learn: 1780.2112602	total: 9.36s	remaining: 28.9s
245:	learn: 1779.9453147	total: 9.4s	remaining: 28.8s
246:	learn: 1779.5035305	total: 9.44s	remaining: 28.8s
247:	learn: 1779.1024113	total: 9.47s	remaining: 28.7s
248:	learn: 1778.6124969	total: 9.51s	remaining: 28.7s
249:	learn: 1778.2540646	total: 9.56s	remaining: 28.7s
250:	learn: 1777.8708548	total: 9.59s	remaining: 28.6s
251:	learn: 1777.6396625	total: 9.63s	remaining: 28.6s
252:	learn: 1777.3333206	total: 9.67s	remaining: 28.5s
253:	learn: 1776.9195690	total: 9.71s	remaining: 28.5s
254:	learn: 1776.6025572	total: 9.74s	remaining: 28.5s
255:	learn: 1776.1948877	total: 9.78s	remaining: 28.4s
256:	learn: 1775.9161063	total: 9.82s	remaining: 28.4s
257:	learn: 1775.6270258	total: 9.85s	remaining: 28.3s
258:	learn: 1775.3998128	total: 9.89s	remaining: 28.3s
259:	learn: 1775.0897997	total: 9.93s	remaining: 28.3s
260:	learn: 1774.8271029	total: 9.96s	remaining: 28.2s
261:	learn: 1774.3388322	total: 10s	remaining: 28.2s
262:	learn: 1774.0200502	total: 10s	remaining: 28.1s
263:	learn: 1773.6887452	total: 10.1s	remaining: 28.1s
264:	learn: 1773.4059839	total: 10.1s	remaining: 28.1s
265:	learn: 1773.1201130	total: 10.2s	remaining: 28s
266:	learn: 1772.7696711	total: 10.2s	remaining: 28s
267:	learn: 1772.4872386	total: 10.2s	remaining: 27.9s
268:	learn: 1772.2062464	total: 10.3s	remaining: 27.9s
269:	learn: 1771.8423772	total: 10.3s	remaining: 27.9s
270:	learn: 1771.5846551	total: 10.3s	remaining: 27.8s
271:	learn: 1771.4041686	total: 10.4s	remaining: 27.8s
272:	learn: 1770.8989639	total: 10.4s	remaining: 27.7s
273:	learn: 1770.1904435	total: 10.5s	remaining: 27.7s
274:	learn: 1769.7568932	total: 10.5s	remaining: 27.6s
275:	learn: 1769.3664708	total: 10.5s	remaining: 27.6s
276:	learn: 1769.0642151	total: 10.6s	remaining: 27.6s
277:	learn: 1768.6658957	total: 10.6s	remaining: 27.5s
278:	learn: 1768.1364497	total: 10.6s	remaining: 27.5s
279:	learn: 1767.9284926	total: 10.7s	remaining: 27.5s
280:	learn: 1767.6985789	total: 10.7s	remaining: 27.4s
281:	learn: 1767.1823263	total: 10.8s	remaining: 27.4s
282:	learn: 1766.8906104	total: 10.8s	remaining: 27.4s
283:	learn: 1766.6383109	total: 10.8s	remaining: 27.3s
284:	learn: 1766.1665040	total: 10.9s	remaining: 27.3s
285:	learn: 1765.8448374	total: 10.9s	remaining: 27.3s
286:	learn: 1765.5680723	total: 11s	remaining: 27.3s
287:	learn: 1765.3666445	total: 11s	remaining: 27.2s
288:	learn: 1765.1500794	total: 11.1s	remaining: 27.2s
289:	learn: 1764.8564873	total: 11.1s	remaining: 27.2s
290:	learn: 1764.5185652	total: 11.1s	remaining: 27.1s
291:	learn: 1764.1867872	total: 11.2s	remaining: 27.1s
292:	learn: 1763.8993393	total: 11.2s	remaining: 27s
293:	learn: 1763.6660679	total: 11.2s	remaining: 27s
294:	learn: 1763.3575871	total: 11.3s	remaining: 26.9s
295:	learn: 1763.1142639	total: 11.3s	remaining: 26.9s
296:	learn: 1762.6812398	total: 11.3s	remaining: 26.8s
297:	learn: 1762.2830127	total: 11.4s	remaining: 26.8s
298:	learn: 1762.0901304	total: 11.4s	remaining: 26.7s
299:	learn: 1761.7681685	total: 11.5s	remaining: 26.7s
300:	learn: 1761.4818991	total: 11.5s	remaining: 26.7s
301:	learn: 1761.1103422	total: 11.5s	remaining: 26.7s
302:	learn: 1760.8641990	total: 11.6s	remaining: 26.6s
303:	learn: 1760.6317205	total: 11.6s	remaining: 26.6s
304:	learn: 1760.3780051	total: 11.6s	remaining: 26.5s
305:	learn: 1760.0414518	total: 11.7s	remaining: 26.5s
306:	learn: 1759.8263695	total: 11.7s	remaining: 26.4s
307:	learn: 1759.5421926	total: 11.8s	remaining: 26.4s
308:	learn: 1759.2803304	total: 11.8s	remaining: 26.4s
309:	learn: 1758.7037405	total: 11.8s	remaining: 26.3s
310:	learn: 1758.4650643	total: 11.9s	remaining: 26.3s
311:	learn: 1758.0051509	total: 11.9s	remaining: 26.2s
312:	learn: 1757.8479266	total: 11.9s	remaining: 26.2s
313:	learn: 1757.5739874	total: 12s	remaining: 26.1s
314:	learn: 1757.3063596	total: 12s	remaining: 26.1s
315:	learn: 1757.0848107	total: 12s	remaining: 26.1s
316:	learn: 1756.7994567	total: 12.1s	remaining: 26s
317:	learn: 1756.5600904	total: 12.1s	remaining: 26s
318:	learn: 1756.2659534	total: 12.1s	remaining: 25.9s
319:	learn: 1756.0907102	total: 12.2s	remaining: 25.9s
320:	learn: 1755.8491010	total: 12.2s	remaining: 25.8s
321:	learn: 1755.5478877	total: 12.2s	remaining: 25.8s
322:	learn: 1755.1168360	total: 12.3s	remaining: 25.7s
323:	learn: 1754.8449535	total: 12.3s	remaining: 25.7s
324:	learn: 1754.5057935	total: 12.4s	remaining: 25.7s
325:	learn: 1754.0880522	total: 12.4s	remaining: 25.6s
326:	learn: 1753.8344479	total: 12.4s	remaining: 25.6s
327:	learn: 1753.6281704	total: 12.5s	remaining: 25.5s
328:	learn: 1753.2965795	total: 12.5s	remaining: 25.5s
329:	learn: 1752.9300382	total: 12.5s	remaining: 25.4s
330:	learn: 1752.5147734	total: 12.6s	remaining: 25.4s
331:	learn: 1752.1240970	total: 12.6s	remaining: 25.4s
332:	learn: 1751.9118084	total: 12.6s	remaining: 25.3s
333:	learn: 1751.5902142	total: 12.7s	remaining: 25.3s
334:	learn: 1751.3225292	total: 12.7s	remaining: 25.2s
335:	learn: 1750.9929799	total: 12.7s	remaining: 25.2s
336:	learn: 1750.7245450	total: 12.8s	remaining: 25.1s
337:	learn: 1750.3474505	total: 12.8s	remaining: 25.1s
338:	learn: 1750.1473267	total: 12.9s	remaining: 25.1s
339:	learn: 1749.9925988	total: 12.9s	remaining: 25s
340:	learn: 1749.7293843	total: 12.9s	remaining: 25s
341:	learn: 1749.5443800	total: 13s	remaining: 24.9s
342:	learn: 1749.2786608	total: 13s	remaining: 24.9s
343:	learn: 1749.0282543	total: 13s	remaining: 24.9s
344:	learn: 1748.8016758	total: 13.1s	remaining: 24.8s
345:	learn: 1748.5816151	total: 13.1s	remaining: 24.8s
346:	learn: 1748.2391353	total: 13.1s	remaining: 24.7s
347:	learn: 1747.9193570	total: 13.2s	remaining: 24.7s
348:	learn: 1747.5006882	total: 13.2s	remaining: 24.6s
349:	learn: 1747.2459727	total: 13.2s	remaining: 24.6s
350:	learn: 1746.9780406	total: 13.3s	remaining: 24.6s
351:	learn: 1746.6494659	total: 13.3s	remaining: 24.5s
352:	learn: 1746.4461148	total: 13.3s	remaining: 24.5s
353:	learn: 1746.2071127	total: 13.4s	remaining: 24.4s
354:	learn: 1745.9392687	total: 13.4s	remaining: 24.4s
355:	learn: 1745.5625000	total: 13.5s	remaining: 24.4s
356:	learn: 1745.2700794	total: 13.5s	remaining: 24.3s
357:	learn: 1744.8984304	total: 13.5s	remaining: 24.3s
358:	learn: 1744.6932283	total: 13.6s	remaining: 24.3s
359:	learn: 1744.5093506	total: 13.6s	remaining: 24.2s
360:	learn: 1744.2132385	total: 13.7s	remaining: 24.2s
361:	learn: 1743.7651854	total: 13.7s	remaining: 24.1s
362:	learn: 1743.2861394	total: 13.7s	remaining: 24.1s
363:	learn: 1743.0874491	total: 13.8s	remaining: 24.1s
364:	learn: 1742.8353213	total: 13.8s	remaining: 24s
365:	learn: 1742.6024486	total: 13.8s	remaining: 24s
366:	learn: 1742.3204285	total: 13.9s	remaining: 23.9s
367:	learn: 1742.1034864	total: 13.9s	remaining: 23.9s
368:	learn: 1741.6239581	total: 13.9s	remaining: 23.8s
369:	learn: 1741.3617940	total: 14s	remaining: 23.8s
370:	learn: 1741.0757835	total: 14s	remaining: 23.8s
371:	learn: 1740.7511262	total: 14.1s	remaining: 23.7s
372:	learn: 1740.4421277	total: 14.1s	remaining: 23.7s
373:	learn: 1740.1921007	total: 14.1s	remaining: 23.7s
374:	learn: 1739.7759459	total: 14.2s	remaining: 23.6s
375:	learn: 1739.5004514	total: 14.2s	remaining: 23.6s
376:	learn: 1739.2280361	total: 14.2s	remaining: 23.5s
377:	learn: 1739.0545965	total: 14.3s	remaining: 23.5s
378:	learn: 1738.8086327	total: 14.3s	remaining: 23.4s
379:	learn: 1738.6508553	total: 14.3s	remaining: 23.4s
380:	learn: 1738.3791908	total: 14.4s	remaining: 23.4s
381:	learn: 1738.1401524	total: 14.4s	remaining: 23.3s
382:	learn: 1737.8842934	total: 14.5s	remaining: 23.3s
383:	learn: 1737.6455247	total: 14.5s	remaining: 23.3s
384:	learn: 1737.4289805	total: 14.5s	remaining: 23.2s
385:	learn: 1737.1759950	total: 14.6s	remaining: 23.2s
386:	learn: 1736.9287996	total: 14.6s	remaining: 23.1s
387:	learn: 1736.6644316	total: 14.7s	remaining: 23.1s
388:	learn: 1736.4509275	total: 14.7s	remaining: 23.1s
389:	learn: 1736.1780686	total: 14.7s	remaining: 23s
390:	learn: 1735.8027451	total: 14.8s	remaining: 23s
391:	learn: 1735.5312872	total: 14.8s	remaining: 23s
392:	learn: 1735.2365895	total: 14.8s	remaining: 22.9s
393:	learn: 1735.0621982	total: 14.9s	remaining: 22.9s
394:	learn: 1734.7786646	total: 14.9s	remaining: 22.8s
395:	learn: 1734.5900719	total: 14.9s	remaining: 22.8s
396:	learn: 1734.4205195	total: 15s	remaining: 22.8s
397:	learn: 1734.1769668	total: 15s	remaining: 22.7s
398:	learn: 1734.0092385	total: 15s	remaining: 22.7s
399:	learn: 1733.6076731	total: 15.1s	remaining: 22.6s
400:	learn: 1733.4073943	total: 15.1s	remaining: 22.6s
401:	learn: 1733.1870464	total: 15.2s	remaining: 22.5s
402:	learn: 1732.8418426	total: 15.2s	remaining: 22.5s
403:	learn: 1732.5884280	total: 15.2s	remaining: 22.5s
404:	learn: 1732.4253999	total: 15.3s	remaining: 22.4s
405:	learn: 1732.2204470	total: 15.3s	remaining: 22.4s
406:	learn: 1732.0078576	total: 15.3s	remaining: 22.3s
407:	learn: 1731.6408685	total: 15.4s	remaining: 22.3s
408:	learn: 1731.4376410	total: 15.4s	remaining: 22.3s
409:	learn: 1731.0719856	total: 15.4s	remaining: 22.2s
410:	learn: 1730.9297863	total: 15.5s	remaining: 22.2s
411:	learn: 1730.5167411	total: 15.5s	remaining: 22.2s
412:	learn: 1730.2006985	total: 15.6s	remaining: 22.1s
413:	learn: 1729.9278694	total: 15.6s	remaining: 22.1s
414:	learn: 1729.6756361	total: 15.6s	remaining: 22s
415:	learn: 1729.4111341	total: 15.7s	remaining: 22s
416:	learn: 1729.2568988	total: 15.7s	remaining: 22s
417:	learn: 1729.0928255	total: 15.7s	remaining: 21.9s
418:	learn: 1728.8499582	total: 15.8s	remaining: 21.9s
419:	learn: 1728.5351801	total: 15.8s	remaining: 21.9s
420:	learn: 1728.2158585	total: 15.9s	remaining: 21.8s
421:	learn: 1728.0929619	total: 15.9s	remaining: 21.8s
422:	learn: 1727.9116979	total: 15.9s	remaining: 21.7s
423:	learn: 1727.6771975	total: 16s	remaining: 21.7s
424:	learn: 1727.4173729	total: 16s	remaining: 21.7s
425:	learn: 1727.0662389	total: 16s	remaining: 21.6s
426:	learn: 1726.8669824	total: 16.1s	remaining: 21.6s
427:	learn: 1726.6371360	total: 16.1s	remaining: 21.5s
428:	learn: 1726.3195048	total: 16.1s	remaining: 21.5s
429:	learn: 1726.0887469	total: 16.2s	remaining: 21.5s
430:	learn: 1725.8260628	total: 16.2s	remaining: 21.4s
431:	learn: 1725.5895624	total: 16.3s	remaining: 21.4s
432:	learn: 1725.2705483	total: 16.3s	remaining: 21.4s
433:	learn: 1725.1367573	total: 16.3s	remaining: 21.3s
434:	learn: 1725.0005454	total: 16.4s	remaining: 21.3s
435:	learn: 1724.8051876	total: 16.4s	remaining: 21.2s
436:	learn: 1724.6564043	total: 16.4s	remaining: 21.2s
437:	learn: 1724.5366264	total: 16.5s	remaining: 21.1s
438:	learn: 1724.2321711	total: 16.5s	remaining: 21.1s
439:	learn: 1724.0565394	total: 16.5s	remaining: 21.1s
440:	learn: 1723.8850055	total: 16.6s	remaining: 21s
441:	learn: 1723.6696154	total: 16.6s	remaining: 21s
442:	learn: 1723.3431246	total: 16.7s	remaining: 20.9s
443:	learn: 1723.1398505	total: 16.7s	remaining: 20.9s
444:	learn: 1722.8639785	total: 16.7s	remaining: 20.9s
445:	learn: 1722.6735897	total: 16.8s	remaining: 20.8s
446:	learn: 1722.5845354	total: 16.8s	remaining: 20.8s
447:	learn: 1722.4097049	total: 16.8s	remaining: 20.7s
448:	learn: 1722.1139844	total: 16.9s	remaining: 20.7s
449:	learn: 1721.7760568	total: 16.9s	remaining: 20.7s
450:	learn: 1721.4713080	total: 17s	remaining: 20.6s
451:	learn: 1721.2194677	total: 17s	remaining: 20.6s
452:	learn: 1721.0612090	total: 17s	remaining: 20.6s
453:	learn: 1720.9146539	total: 17.1s	remaining: 20.5s
454:	learn: 1720.8222587	total: 17.1s	remaining: 20.5s
455:	learn: 1720.6646628	total: 17.1s	remaining: 20.5s
456:	learn: 1720.4637749	total: 17.2s	remaining: 20.4s
457:	learn: 1720.2393639	total: 17.2s	remaining: 20.4s
458:	learn: 1720.1202312	total: 17.3s	remaining: 20.3s
459:	learn: 1719.7680282	total: 17.3s	remaining: 20.3s
460:	learn: 1719.5163737	total: 17.3s	remaining: 20.3s
461:	learn: 1719.3732885	total: 17.4s	remaining: 20.2s
462:	learn: 1719.2246511	total: 17.4s	remaining: 20.2s
463:	learn: 1719.1061951	total: 17.4s	remaining: 20.1s
464:	learn: 1718.8771668	total: 17.5s	remaining: 20.1s
465:	learn: 1718.6532539	total: 17.5s	remaining: 20.1s
466:	learn: 1718.4049513	total: 17.5s	remaining: 20s
467:	learn: 1718.2375973	total: 17.6s	remaining: 20s
468:	learn: 1717.9303183	total: 17.6s	remaining: 19.9s
469:	learn: 1717.7103214	total: 17.7s	remaining: 19.9s
470:	learn: 1717.4267298	total: 17.7s	remaining: 19.9s
471:	learn: 1717.1456832	total: 17.7s	remaining: 19.8s
472:	learn: 1716.9588359	total: 17.8s	remaining: 19.8s
473:	learn: 1716.8348814	total: 17.8s	remaining: 19.8s
474:	learn: 1716.5559279	total: 17.8s	remaining: 19.7s
475:	learn: 1716.3504419	total: 17.9s	remaining: 19.7s
476:	learn: 1716.2040290	total: 17.9s	remaining: 19.6s
477:	learn: 1716.0826465	total: 17.9s	remaining: 19.6s
478:	learn: 1715.9015271	total: 18s	remaining: 19.6s
479:	learn: 1715.7024150	total: 18s	remaining: 19.5s
480:	learn: 1715.6024799	total: 18s	remaining: 19.5s
481:	learn: 1715.3289656	total: 18.1s	remaining: 19.4s
482:	learn: 1715.2076024	total: 18.1s	remaining: 19.4s
483:	learn: 1714.9178652	total: 18.2s	remaining: 19.4s
484:	learn: 1714.7177816	total: 18.2s	remaining: 19.3s
485:	learn: 1714.4598430	total: 18.2s	remaining: 19.3s
486:	learn: 1714.0183643	total: 18.3s	remaining: 19.2s
487:	learn: 1713.7304333	total: 18.3s	remaining: 19.2s
488:	learn: 1713.5103897	total: 18.3s	remaining: 19.2s
489:	learn: 1713.3742397	total: 18.4s	remaining: 19.1s
490:	learn: 1713.2619764	total: 18.4s	remaining: 19.1s
491:	learn: 1713.0387966	total: 18.4s	remaining: 19s
492:	learn: 1712.9401211	total: 18.5s	remaining: 19s
493:	learn: 1712.7394449	total: 18.5s	remaining: 19s
494:	learn: 1712.5343663	total: 18.5s	remaining: 18.9s
495:	learn: 1712.3784743	total: 18.6s	remaining: 18.9s
496:	learn: 1712.0922978	total: 18.6s	remaining: 18.9s
497:	learn: 1711.8156402	total: 18.7s	remaining: 18.8s
498:	learn: 1711.6820771	total: 18.7s	remaining: 18.8s
499:	learn: 1711.4950015	total: 18.7s	remaining: 18.7s
500:	learn: 1711.2591853	total: 18.8s	remaining: 18.7s
501:	learn: 1711.0977516	total: 18.8s	remaining: 18.7s
502:	learn: 1710.9929387	total: 18.9s	remaining: 18.6s
503:	learn: 1710.8271099	total: 18.9s	remaining: 18.6s
504:	learn: 1710.6331287	total: 18.9s	remaining: 18.6s
505:	learn: 1710.4762621	total: 19s	remaining: 18.5s
506:	learn: 1710.3142589	total: 19s	remaining: 18.5s
507:	learn: 1710.1899185	total: 19s	remaining: 18.4s
508:	learn: 1709.9372884	total: 19.1s	remaining: 18.4s
509:	learn: 1709.7034273	total: 19.1s	remaining: 18.4s
510:	learn: 1709.5120325	total: 19.2s	remaining: 18.3s
511:	learn: 1709.2075357	total: 19.2s	remaining: 18.3s
512:	learn: 1709.0654959	total: 19.2s	remaining: 18.3s
513:	learn: 1708.8787820	total: 19.3s	remaining: 18.2s
514:	learn: 1708.6184315	total: 19.3s	remaining: 18.2s
515:	learn: 1708.5472845	total: 19.3s	remaining: 18.1s
516:	learn: 1708.4376861	total: 19.4s	remaining: 18.1s
517:	learn: 1708.2355679	total: 19.4s	remaining: 18.1s
518:	learn: 1707.9584908	total: 19.4s	remaining: 18s
519:	learn: 1707.6131821	total: 19.5s	remaining: 18s
520:	learn: 1707.3502514	total: 19.5s	remaining: 17.9s
521:	learn: 1707.1687955	total: 19.6s	remaining: 17.9s
522:	learn: 1706.8852222	total: 19.6s	remaining: 17.9s
523:	learn: 1706.7647810	total: 19.6s	remaining: 17.8s
524:	learn: 1706.6047831	total: 19.7s	remaining: 17.8s
525:	learn: 1706.4204895	total: 19.7s	remaining: 17.8s
526:	learn: 1706.2095678	total: 19.7s	remaining: 17.7s
527:	learn: 1706.0232433	total: 19.8s	remaining: 17.7s
528:	learn: 1705.7865371	total: 19.8s	remaining: 17.6s
529:	learn: 1705.6659996	total: 19.8s	remaining: 17.6s
530:	learn: 1705.5108255	total: 19.9s	remaining: 17.6s
531:	learn: 1705.2820013	total: 19.9s	remaining: 17.5s
532:	learn: 1705.0822526	total: 20s	remaining: 17.5s
533:	learn: 1704.9111084	total: 20s	remaining: 17.5s
534:	learn: 1704.7166620	total: 20s	remaining: 17.4s
535:	learn: 1704.5360805	total: 20.1s	remaining: 17.4s
536:	learn: 1704.3616382	total: 20.1s	remaining: 17.3s
537:	learn: 1704.2146442	total: 20.1s	remaining: 17.3s
538:	learn: 1704.0714488	total: 20.2s	remaining: 17.3s
539:	learn: 1703.8316317	total: 20.2s	remaining: 17.2s
540:	learn: 1703.5975953	total: 20.3s	remaining: 17.2s
541:	learn: 1703.4061100	total: 20.3s	remaining: 17.2s
542:	learn: 1703.2313359	total: 20.3s	remaining: 17.1s
543:	learn: 1703.0239437	total: 20.4s	remaining: 17.1s
544:	learn: 1702.8668595	total: 20.4s	remaining: 17s
545:	learn: 1702.7368019	total: 20.4s	remaining: 17s
546:	learn: 1702.5079268	total: 20.5s	remaining: 17s
547:	learn: 1702.4216445	total: 20.5s	remaining: 16.9s
548:	learn: 1702.2927510	total: 20.5s	remaining: 16.9s
549:	learn: 1702.0422832	total: 20.6s	remaining: 16.8s
550:	learn: 1701.8703780	total: 20.6s	remaining: 16.8s
551:	learn: 1701.7375122	total: 20.6s	remaining: 16.8s
552:	learn: 1701.6181835	total: 20.7s	remaining: 16.7s
553:	learn: 1701.5040375	total: 20.7s	remaining: 16.7s
554:	learn: 1701.3217843	total: 20.8s	remaining: 16.7s
555:	learn: 1701.1424688	total: 20.8s	remaining: 16.6s
556:	learn: 1700.8518772	total: 20.8s	remaining: 16.6s
557:	learn: 1700.6148405	total: 20.9s	remaining: 16.6s
558:	learn: 1700.4257132	total: 20.9s	remaining: 16.5s
559:	learn: 1700.3250255	total: 21s	remaining: 16.5s
560:	learn: 1700.1770679	total: 21s	remaining: 16.4s
561:	learn: 1700.0776019	total: 21.1s	remaining: 16.4s
562:	learn: 1699.9274944	total: 21.1s	remaining: 16.4s
563:	learn: 1699.7862868	total: 21.1s	remaining: 16.3s
564:	learn: 1699.5850276	total: 21.2s	remaining: 16.3s
565:	learn: 1699.3798315	total: 21.2s	remaining: 16.3s
566:	learn: 1699.2410570	total: 21.2s	remaining: 16.2s
567:	learn: 1699.0771009	total: 21.3s	remaining: 16.2s
568:	learn: 1698.9200611	total: 21.3s	remaining: 16.1s
569:	learn: 1698.8371874	total: 21.3s	remaining: 16.1s
570:	learn: 1698.6693974	total: 21.4s	remaining: 16.1s
571:	learn: 1698.4458230	total: 21.4s	remaining: 16s
572:	learn: 1698.2352671	total: 21.5s	remaining: 16s
573:	learn: 1698.0114403	total: 21.5s	remaining: 16s
574:	learn: 1697.9620283	total: 21.5s	remaining: 15.9s
575:	learn: 1697.7285149	total: 21.6s	remaining: 15.9s
576:	learn: 1697.5572914	total: 21.6s	remaining: 15.8s
577:	learn: 1697.2472830	total: 21.6s	remaining: 15.8s
578:	learn: 1697.0865106	total: 21.7s	remaining: 15.8s
579:	learn: 1696.8986570	total: 21.7s	remaining: 15.7s
580:	learn: 1696.6495019	total: 21.7s	remaining: 15.7s
581:	learn: 1696.5006199	total: 21.8s	remaining: 15.6s
582:	learn: 1696.3224090	total: 21.8s	remaining: 15.6s
583:	learn: 1696.1328786	total: 21.8s	remaining: 15.6s
584:	learn: 1695.9305801	total: 21.9s	remaining: 15.5s
585:	learn: 1695.7786486	total: 21.9s	remaining: 15.5s
586:	learn: 1695.6893543	total: 21.9s	remaining: 15.4s
587:	learn: 1695.6262466	total: 22s	remaining: 15.4s
588:	learn: 1695.4207369	total: 22s	remaining: 15.4s
589:	learn: 1695.3013015	total: 22.1s	remaining: 15.3s
590:	learn: 1695.1384591	total: 22.1s	remaining: 15.3s
591:	learn: 1694.9932710	total: 22.1s	remaining: 15.2s
592:	learn: 1694.7981887	total: 22.2s	remaining: 15.2s
593:	learn: 1694.6564635	total: 22.2s	remaining: 15.2s
594:	learn: 1694.2926821	total: 22.2s	remaining: 15.1s
595:	learn: 1694.1911682	total: 22.3s	remaining: 15.1s
596:	learn: 1694.0801305	total: 22.3s	remaining: 15s
597:	learn: 1693.9037712	total: 22.3s	remaining: 15s
598:	learn: 1693.6964583	total: 22.4s	remaining: 15s
599:	learn: 1693.5709800	total: 22.4s	remaining: 14.9s
600:	learn: 1693.4147224	total: 22.4s	remaining: 14.9s
601:	learn: 1693.2299947	total: 22.5s	remaining: 14.9s
602:	learn: 1693.0585101	total: 22.5s	remaining: 14.8s
603:	learn: 1692.8997478	total: 22.6s	remaining: 14.8s
604:	learn: 1692.7430051	total: 22.6s	remaining: 14.8s
605:	learn: 1692.6348260	total: 22.6s	remaining: 14.7s
606:	learn: 1692.4622758	total: 22.7s	remaining: 14.7s
607:	learn: 1692.3065340	total: 22.7s	remaining: 14.6s
608:	learn: 1692.1929430	total: 22.7s	remaining: 14.6s
609:	learn: 1691.9846774	total: 22.8s	remaining: 14.6s
610:	learn: 1691.8124788	total: 22.8s	remaining: 14.5s
611:	learn: 1691.4861328	total: 22.9s	remaining: 14.5s
612:	learn: 1691.3641961	total: 22.9s	remaining: 14.5s
613:	learn: 1691.2283038	total: 22.9s	remaining: 14.4s
614:	learn: 1691.1405414	total: 23s	remaining: 14.4s
615:	learn: 1690.8798935	total: 23s	remaining: 14.3s
616:	learn: 1690.7862335	total: 23s	remaining: 14.3s
617:	learn: 1690.6369271	total: 23.1s	remaining: 14.3s
618:	learn: 1690.4939458	total: 23.1s	remaining: 14.2s
619:	learn: 1690.4174686	total: 23.1s	remaining: 14.2s
620:	learn: 1690.2908262	total: 23.2s	remaining: 14.1s
621:	learn: 1690.0350903	total: 23.2s	remaining: 14.1s
622:	learn: 1689.9688565	total: 23.3s	remaining: 14.1s
623:	learn: 1689.7280056	total: 23.3s	remaining: 14s
624:	learn: 1689.5527882	total: 23.3s	remaining: 14s
625:	learn: 1689.3676756	total: 23.4s	remaining: 14s
626:	learn: 1689.1537142	total: 23.4s	remaining: 13.9s
627:	learn: 1688.9982296	total: 23.4s	remaining: 13.9s
628:	learn: 1688.7045118	total: 23.5s	remaining: 13.8s
629:	learn: 1688.5835104	total: 23.5s	remaining: 13.8s
630:	learn: 1688.4210762	total: 23.6s	remaining: 13.8s
631:	learn: 1688.3263688	total: 23.6s	remaining: 13.7s
632:	learn: 1688.2075667	total: 23.6s	remaining: 13.7s
633:	learn: 1687.9733608	total: 23.7s	remaining: 13.7s
634:	learn: 1687.8498311	total: 23.7s	remaining: 13.6s
635:	learn: 1687.6652688	total: 23.7s	remaining: 13.6s
636:	learn: 1687.4933491	total: 23.8s	remaining: 13.5s
637:	learn: 1687.3639066	total: 23.8s	remaining: 13.5s
638:	learn: 1687.2164424	total: 23.8s	remaining: 13.5s
639:	learn: 1687.1215159	total: 23.9s	remaining: 13.4s
640:	learn: 1686.9672682	total: 23.9s	remaining: 13.4s
641:	learn: 1686.7835279	total: 23.9s	remaining: 13.3s
642:	learn: 1686.6344900	total: 24s	remaining: 13.3s
643:	learn: 1686.4368296	total: 24s	remaining: 13.3s
644:	learn: 1686.1283055	total: 24s	remaining: 13.2s
645:	learn: 1686.0319744	total: 24.1s	remaining: 13.2s
646:	learn: 1685.8784097	total: 24.1s	remaining: 13.2s
647:	learn: 1685.7247837	total: 24.1s	remaining: 13.1s
648:	learn: 1685.6297880	total: 24.2s	remaining: 13.1s
649:	learn: 1685.4069375	total: 24.2s	remaining: 13s
650:	learn: 1685.2322691	total: 24.2s	remaining: 13s
651:	learn: 1685.0892882	total: 24.3s	remaining: 13s
652:	learn: 1684.9853738	total: 24.3s	remaining: 12.9s
653:	learn: 1684.8911601	total: 24.4s	remaining: 12.9s
654:	learn: 1684.7264021	total: 24.4s	remaining: 12.8s
655:	learn: 1684.6448773	total: 24.4s	remaining: 12.8s
656:	learn: 1684.4500992	total: 24.5s	remaining: 12.8s
657:	learn: 1684.2712591	total: 24.5s	remaining: 12.7s
658:	learn: 1684.0837157	total: 24.5s	remaining: 12.7s
659:	learn: 1683.9473053	total: 24.6s	remaining: 12.7s
660:	learn: 1683.8144984	total: 24.6s	remaining: 12.6s
661:	learn: 1683.7403280	total: 24.6s	remaining: 12.6s
662:	learn: 1683.5161112	total: 24.7s	remaining: 12.5s
663:	learn: 1683.3907945	total: 24.7s	remaining: 12.5s
664:	learn: 1683.2898509	total: 24.8s	remaining: 12.5s
665:	learn: 1683.0970672	total: 24.8s	remaining: 12.4s
666:	learn: 1683.0061456	total: 24.8s	remaining: 12.4s
667:	learn: 1682.9051575	total: 24.9s	remaining: 12.4s
668:	learn: 1682.7057448	total: 24.9s	remaining: 12.3s
669:	learn: 1682.5544938	total: 24.9s	remaining: 12.3s
670:	learn: 1682.3945318	total: 25s	remaining: 12.2s
671:	learn: 1682.2167941	total: 25s	remaining: 12.2s
672:	learn: 1682.0770457	total: 25s	remaining: 12.2s
673:	learn: 1681.8354251	total: 25.1s	remaining: 12.1s
674:	learn: 1681.6784281	total: 25.1s	remaining: 12.1s
675:	learn: 1681.4974934	total: 25.1s	remaining: 12s
676:	learn: 1681.3850834	total: 25.2s	remaining: 12s
677:	learn: 1681.2517942	total: 25.2s	remaining: 12s
678:	learn: 1681.1611254	total: 25.2s	remaining: 11.9s
679:	learn: 1681.0016668	total: 25.3s	remaining: 11.9s
680:	learn: 1680.8935747	total: 25.3s	remaining: 11.9s
681:	learn: 1680.8247735	total: 25.3s	remaining: 11.8s
682:	learn: 1680.7218487	total: 25.4s	remaining: 11.8s
683:	learn: 1680.5864594	total: 25.4s	remaining: 11.7s
684:	learn: 1680.4605058	total: 25.5s	remaining: 11.7s
685:	learn: 1680.2376068	total: 25.5s	remaining: 11.7s
686:	learn: 1680.1758320	total: 25.5s	remaining: 11.6s
687:	learn: 1680.0241639	total: 25.6s	remaining: 11.6s
688:	learn: 1679.8791839	total: 25.6s	remaining: 11.5s
689:	learn: 1679.7162481	total: 25.6s	remaining: 11.5s
690:	learn: 1679.5416180	total: 25.7s	remaining: 11.5s
691:	learn: 1679.4170630	total: 25.7s	remaining: 11.4s
692:	learn: 1679.2436408	total: 25.7s	remaining: 11.4s
693:	learn: 1679.1117381	total: 25.8s	remaining: 11.4s
694:	learn: 1678.9576004	total: 25.8s	remaining: 11.3s
695:	learn: 1678.8058421	total: 25.8s	remaining: 11.3s
696:	learn: 1678.6894377	total: 25.9s	remaining: 11.3s
697:	learn: 1678.5200190	total: 25.9s	remaining: 11.2s
698:	learn: 1678.3965343	total: 26s	remaining: 11.2s
699:	learn: 1678.2687540	total: 26s	remaining: 11.1s
700:	learn: 1678.0802346	total: 26s	remaining: 11.1s
701:	learn: 1677.8958989	total: 26.1s	remaining: 11.1s
702:	learn: 1677.7963547	total: 26.1s	remaining: 11s
703:	learn: 1677.6392694	total: 26.1s	remaining: 11s
704:	learn: 1677.4352800	total: 26.2s	remaining: 10.9s
705:	learn: 1677.3125238	total: 26.2s	remaining: 10.9s
706:	learn: 1677.1476560	total: 26.2s	remaining: 10.9s
707:	learn: 1677.0367564	total: 26.3s	remaining: 10.8s
708:	learn: 1676.9361584	total: 26.3s	remaining: 10.8s
709:	learn: 1676.8059478	total: 26.4s	remaining: 10.8s
710:	learn: 1676.7154256	total: 26.4s	remaining: 10.7s
711:	learn: 1676.6077365	total: 26.4s	remaining: 10.7s
712:	learn: 1676.4350408	total: 26.5s	remaining: 10.7s
713:	learn: 1676.3364161	total: 26.5s	remaining: 10.6s
714:	learn: 1676.1101897	total: 26.5s	remaining: 10.6s
715:	learn: 1675.9471705	total: 26.6s	remaining: 10.5s
716:	learn: 1675.7844364	total: 26.6s	remaining: 10.5s
717:	learn: 1675.6109474	total: 26.7s	remaining: 10.5s
718:	learn: 1675.4847957	total: 26.7s	remaining: 10.4s
719:	learn: 1675.3977002	total: 26.7s	remaining: 10.4s
720:	learn: 1675.3084707	total: 26.8s	remaining: 10.4s
721:	learn: 1675.1707373	total: 26.8s	remaining: 10.3s
722:	learn: 1675.1093974	total: 26.8s	remaining: 10.3s
723:	learn: 1674.8997906	total: 26.9s	remaining: 10.2s
724:	learn: 1674.8447168	total: 26.9s	remaining: 10.2s
725:	learn: 1674.7399367	total: 26.9s	remaining: 10.2s
726:	learn: 1674.6348980	total: 27s	remaining: 10.1s
727:	learn: 1674.5470940	total: 27s	remaining: 10.1s
728:	learn: 1674.4660068	total: 27.1s	remaining: 10.1s
729:	learn: 1674.3420098	total: 27.1s	remaining: 10s
730:	learn: 1674.2147016	total: 27.1s	remaining: 9.99s
731:	learn: 1674.1328517	total: 27.2s	remaining: 9.95s
732:	learn: 1673.9818478	total: 27.2s	remaining: 9.91s
733:	learn: 1673.8285175	total: 27.3s	remaining: 9.88s
734:	learn: 1673.6368248	total: 27.3s	remaining: 9.84s
735:	learn: 1673.4523845	total: 27.3s	remaining: 9.81s
736:	learn: 1673.3858658	total: 27.4s	remaining: 9.77s
737:	learn: 1673.1952204	total: 27.4s	remaining: 9.73s
738:	learn: 1672.9860140	total: 27.5s	remaining: 9.7s
739:	learn: 1672.8757114	total: 27.5s	remaining: 9.66s
740:	learn: 1672.6993988	total: 27.5s	remaining: 9.62s
741:	learn: 1672.6454698	total: 27.6s	remaining: 9.59s
742:	learn: 1672.5069602	total: 27.6s	remaining: 9.55s
743:	learn: 1672.4251156	total: 27.6s	remaining: 9.51s
744:	learn: 1672.3218743	total: 27.7s	remaining: 9.47s
745:	learn: 1672.2356822	total: 27.7s	remaining: 9.44s
746:	learn: 1672.0915272	total: 27.8s	remaining: 9.4s
747:	learn: 1671.8967974	total: 27.8s	remaining: 9.36s
748:	learn: 1671.8407001	total: 27.8s	remaining: 9.32s
749:	learn: 1671.7207630	total: 27.9s	remaining: 9.29s
750:	learn: 1671.4845382	total: 27.9s	remaining: 9.25s
751:	learn: 1671.3925004	total: 27.9s	remaining: 9.21s
752:	learn: 1671.2904745	total: 28s	remaining: 9.18s
753:	learn: 1671.1817530	total: 28s	remaining: 9.14s
754:	learn: 1671.0265159	total: 28s	remaining: 9.1s
755:	learn: 1670.9156212	total: 28.1s	remaining: 9.06s
756:	learn: 1670.8636604	total: 28.1s	remaining: 9.03s
757:	learn: 1670.7288488	total: 28.2s	remaining: 8.99s
758:	learn: 1670.5928166	total: 28.2s	remaining: 8.95s
759:	learn: 1670.4388169	total: 28.2s	remaining: 8.91s
760:	learn: 1670.2313387	total: 28.3s	remaining: 8.88s
761:	learn: 1670.1281205	total: 28.3s	remaining: 8.84s
762:	learn: 1670.0122737	total: 28.3s	remaining: 8.8s
763:	learn: 1669.9326477	total: 28.4s	remaining: 8.77s
764:	learn: 1669.7738884	total: 28.4s	remaining: 8.73s
765:	learn: 1669.5812741	total: 28.5s	remaining: 8.69s
766:	learn: 1669.4864356	total: 28.5s	remaining: 8.65s
767:	learn: 1669.4404012	total: 28.5s	remaining: 8.62s
768:	learn: 1669.2543329	total: 28.6s	remaining: 8.58s
769:	learn: 1669.0674034	total: 28.6s	remaining: 8.54s
770:	learn: 1668.9608453	total: 28.6s	remaining: 8.5s
771:	learn: 1668.8669561	total: 28.7s	remaining: 8.47s
772:	learn: 1668.7490347	total: 28.7s	remaining: 8.43s
773:	learn: 1668.6607003	total: 28.7s	remaining: 8.39s
774:	learn: 1668.5564180	total: 28.8s	remaining: 8.35s
775:	learn: 1668.4903669	total: 28.8s	remaining: 8.31s
776:	learn: 1668.4272804	total: 28.8s	remaining: 8.28s
777:	learn: 1668.3239406	total: 28.9s	remaining: 8.24s
778:	learn: 1668.2659643	total: 28.9s	remaining: 8.2s
779:	learn: 1668.1750383	total: 29s	remaining: 8.17s
780:	learn: 1668.0925076	total: 29s	remaining: 8.13s
781:	learn: 1668.0232905	total: 29s	remaining: 8.09s
782:	learn: 1667.8234460	total: 29.1s	remaining: 8.05s
783:	learn: 1667.5820075	total: 29.1s	remaining: 8.02s
784:	learn: 1667.4734628	total: 29.1s	remaining: 7.98s
785:	learn: 1667.3592407	total: 29.2s	remaining: 7.94s
786:	learn: 1667.2361999	total: 29.2s	remaining: 7.9s
787:	learn: 1667.1536925	total: 29.2s	remaining: 7.87s
788:	learn: 1667.0105916	total: 29.3s	remaining: 7.83s
789:	learn: 1666.9582421	total: 29.3s	remaining: 7.79s
790:	learn: 1666.8017005	total: 29.4s	remaining: 7.75s
791:	learn: 1666.6900655	total: 29.4s	remaining: 7.72s
792:	learn: 1666.5414016	total: 29.4s	remaining: 7.68s
793:	learn: 1666.4293517	total: 29.5s	remaining: 7.64s
794:	learn: 1666.2746960	total: 29.5s	remaining: 7.6s
795:	learn: 1666.1624518	total: 29.5s	remaining: 7.57s
796:	learn: 1666.0229459	total: 29.6s	remaining: 7.53s
797:	learn: 1665.9226514	total: 29.6s	remaining: 7.49s
798:	learn: 1665.8103572	total: 29.6s	remaining: 7.45s
799:	learn: 1665.7351465	total: 29.7s	remaining: 7.42s
800:	learn: 1665.6280624	total: 29.7s	remaining: 7.38s
801:	learn: 1665.4438989	total: 29.7s	remaining: 7.34s
802:	learn: 1665.3764375	total: 29.8s	remaining: 7.3s
803:	learn: 1665.2819024	total: 29.8s	remaining: 7.27s
804:	learn: 1665.1838539	total: 29.8s	remaining: 7.23s
805:	learn: 1665.0629147	total: 29.9s	remaining: 7.19s
806:	learn: 1664.9524694	total: 29.9s	remaining: 7.15s
807:	learn: 1664.9092102	total: 29.9s	remaining: 7.12s
808:	learn: 1664.8031237	total: 30s	remaining: 7.08s
809:	learn: 1664.7098368	total: 30s	remaining: 7.04s
810:	learn: 1664.6523562	total: 30.1s	remaining: 7.01s
811:	learn: 1664.5839101	total: 30.1s	remaining: 6.97s
812:	learn: 1664.4589302	total: 30.1s	remaining: 6.93s
813:	learn: 1664.3875261	total: 30.2s	remaining: 6.9s
814:	learn: 1664.3105962	total: 30.2s	remaining: 6.86s
815:	learn: 1664.2588160	total: 30.3s	remaining: 6.83s
816:	learn: 1664.0818948	total: 30.3s	remaining: 6.79s
817:	learn: 1663.9482962	total: 30.4s	remaining: 6.75s
818:	learn: 1663.7970567	total: 30.4s	remaining: 6.72s
819:	learn: 1663.6541816	total: 30.4s	remaining: 6.68s
820:	learn: 1663.4795958	total: 30.5s	remaining: 6.65s
821:	learn: 1663.2679883	total: 30.5s	remaining: 6.61s
822:	learn: 1663.1442857	total: 30.6s	remaining: 6.57s
823:	learn: 1663.0255818	total: 30.6s	remaining: 6.54s
824:	learn: 1662.9347651	total: 30.6s	remaining: 6.5s
825:	learn: 1662.7862990	total: 30.7s	remaining: 6.46s
826:	learn: 1662.7249768	total: 30.7s	remaining: 6.42s
827:	learn: 1662.6156137	total: 30.7s	remaining: 6.38s
828:	learn: 1662.5073758	total: 30.8s	remaining: 6.35s
829:	learn: 1662.4122239	total: 30.8s	remaining: 6.31s
830:	learn: 1662.3202180	total: 30.8s	remaining: 6.27s
831:	learn: 1662.1821761	total: 30.9s	remaining: 6.24s
832:	learn: 1662.0872805	total: 30.9s	remaining: 6.2s
833:	learn: 1661.9780387	total: 31s	remaining: 6.17s
834:	learn: 1661.8551608	total: 31s	remaining: 6.13s
835:	learn: 1661.7299061	total: 31.1s	remaining: 6.09s
836:	learn: 1661.6226776	total: 31.1s	remaining: 6.05s
837:	learn: 1661.5175607	total: 31.1s	remaining: 6.02s
838:	learn: 1661.4005224	total: 31.2s	remaining: 5.98s
839:	learn: 1661.2881224	total: 31.2s	remaining: 5.94s
840:	learn: 1661.1634719	total: 31.2s	remaining: 5.9s
841:	learn: 1661.0212829	total: 31.3s	remaining: 5.87s
842:	learn: 1660.9705221	total: 31.3s	remaining: 5.83s
843:	learn: 1660.8269297	total: 31.3s	remaining: 5.79s
844:	learn: 1660.7710185	total: 31.4s	remaining: 5.75s
845:	learn: 1660.7101926	total: 31.4s	remaining: 5.72s
846:	learn: 1660.6050936	total: 31.5s	remaining: 5.68s
847:	learn: 1660.4695481	total: 31.5s	remaining: 5.64s
848:	learn: 1660.3535390	total: 31.5s	remaining: 5.61s
849:	learn: 1660.2096117	total: 31.6s	remaining: 5.57s
850:	learn: 1660.0370291	total: 31.6s	remaining: 5.53s
851:	learn: 1659.8604050	total: 31.6s	remaining: 5.49s
852:	learn: 1659.7291276	total: 31.7s	remaining: 5.46s
853:	learn: 1659.6404716	total: 31.7s	remaining: 5.42s
854:	learn: 1659.5241863	total: 31.7s	remaining: 5.38s
855:	learn: 1659.4011182	total: 31.8s	remaining: 5.34s
856:	learn: 1659.3228199	total: 31.8s	remaining: 5.3s
857:	learn: 1659.2441871	total: 31.8s	remaining: 5.27s
858:	learn: 1659.1544882	total: 31.9s	remaining: 5.23s
859:	learn: 1659.0466642	total: 31.9s	remaining: 5.19s
860:	learn: 1658.9797395	total: 31.9s	remaining: 5.16s
861:	learn: 1658.8539859	total: 32s	remaining: 5.12s
862:	learn: 1658.7501992	total: 32s	remaining: 5.08s
863:	learn: 1658.6791128	total: 32.1s	remaining: 5.04s
864:	learn: 1658.6117976	total: 32.1s	remaining: 5.01s
865:	learn: 1658.5427267	total: 32.1s	remaining: 4.97s
866:	learn: 1658.3896135	total: 32.2s	remaining: 4.93s
867:	learn: 1658.2685957	total: 32.2s	remaining: 4.89s
868:	learn: 1658.1483773	total: 32.2s	remaining: 4.86s
869:	learn: 1658.1136286	total: 32.3s	remaining: 4.82s
870:	learn: 1657.9893127	total: 32.3s	remaining: 4.78s
871:	learn: 1657.9530228	total: 32.3s	remaining: 4.75s
872:	learn: 1657.8841781	total: 32.4s	remaining: 4.71s
873:	learn: 1657.8441870	total: 32.4s	remaining: 4.67s
874:	learn: 1657.7298265	total: 32.4s	remaining: 4.63s
875:	learn: 1657.5645105	total: 32.5s	remaining: 4.6s
876:	learn: 1657.4149168	total: 32.5s	remaining: 4.56s
877:	learn: 1657.2922775	total: 32.5s	remaining: 4.52s
878:	learn: 1657.2065193	total: 32.6s	remaining: 4.48s
879:	learn: 1657.0564731	total: 32.6s	remaining: 4.45s
880:	learn: 1656.9803490	total: 32.7s	remaining: 4.41s
881:	learn: 1656.8775548	total: 32.7s	remaining: 4.37s
882:	learn: 1656.7923397	total: 32.7s	remaining: 4.34s
883:	learn: 1656.6830786	total: 32.8s	remaining: 4.3s
884:	learn: 1656.5719416	total: 32.8s	remaining: 4.26s
885:	learn: 1656.4009333	total: 32.8s	remaining: 4.22s
886:	learn: 1656.2932785	total: 32.9s	remaining: 4.19s
887:	learn: 1656.1893944	total: 32.9s	remaining: 4.15s
888:	learn: 1656.0869241	total: 33s	remaining: 4.11s
889:	learn: 1655.9653013	total: 33s	remaining: 4.08s
890:	learn: 1655.8005605	total: 33s	remaining: 4.04s
891:	learn: 1655.7291182	total: 33.1s	remaining: 4s
892:	learn: 1655.6244260	total: 33.1s	remaining: 3.96s
893:	learn: 1655.5210290	total: 33.1s	remaining: 3.93s
894:	learn: 1655.4644645	total: 33.2s	remaining: 3.89s
895:	learn: 1655.4008028	total: 33.2s	remaining: 3.85s
896:	learn: 1655.3411920	total: 33.2s	remaining: 3.81s
897:	learn: 1655.2021571	total: 33.3s	remaining: 3.78s
898:	learn: 1655.1483780	total: 33.3s	remaining: 3.74s
899:	learn: 1655.0558898	total: 33.3s	remaining: 3.7s
900:	learn: 1655.0051558	total: 33.4s	remaining: 3.67s
901:	learn: 1654.9429659	total: 33.4s	remaining: 3.63s
902:	learn: 1654.8447666	total: 33.4s	remaining: 3.59s
903:	learn: 1654.6648911	total: 33.5s	remaining: 3.55s
904:	learn: 1654.5540094	total: 33.5s	remaining: 3.52s
905:	learn: 1654.4748853	total: 33.5s	remaining: 3.48s
906:	learn: 1654.3005618	total: 33.6s	remaining: 3.44s
907:	learn: 1654.2435769	total: 33.6s	remaining: 3.41s
908:	learn: 1654.1453072	total: 33.7s	remaining: 3.37s
909:	learn: 1654.0064250	total: 33.7s	remaining: 3.33s
910:	learn: 1653.9616946	total: 33.7s	remaining: 3.29s
911:	learn: 1653.7641910	total: 33.8s	remaining: 3.26s
912:	learn: 1653.6989258	total: 33.8s	remaining: 3.22s
913:	learn: 1653.6453149	total: 33.9s	remaining: 3.19s
914:	learn: 1653.5486930	total: 33.9s	remaining: 3.15s
915:	learn: 1653.4114197	total: 33.9s	remaining: 3.11s
916:	learn: 1653.3095058	total: 34s	remaining: 3.07s
917:	learn: 1653.1816451	total: 34s	remaining: 3.04s
918:	learn: 1653.0882668	total: 34s	remaining: 3s
919:	learn: 1652.9353790	total: 34.1s	remaining: 2.96s
920:	learn: 1652.7593966	total: 34.1s	remaining: 2.93s
921:	learn: 1652.6690695	total: 34.2s	remaining: 2.89s
922:	learn: 1652.6176983	total: 34.2s	remaining: 2.85s
923:	learn: 1652.5320858	total: 34.2s	remaining: 2.81s
924:	learn: 1652.4458549	total: 34.3s	remaining: 2.78s
925:	learn: 1652.3911495	total: 34.3s	remaining: 2.74s
926:	learn: 1652.2986861	total: 34.3s	remaining: 2.7s
927:	learn: 1652.1576076	total: 34.4s	remaining: 2.67s
928:	learn: 1652.0645619	total: 34.4s	remaining: 2.63s
929:	learn: 1651.9870058	total: 34.4s	remaining: 2.59s
930:	learn: 1651.9224912	total: 34.5s	remaining: 2.56s
931:	learn: 1651.8234396	total: 34.5s	remaining: 2.52s
932:	learn: 1651.7612482	total: 34.6s	remaining: 2.48s
933:	learn: 1651.7129760	total: 34.6s	remaining: 2.44s
934:	learn: 1651.6155328	total: 34.6s	remaining: 2.41s
935:	learn: 1651.4623266	total: 34.7s	remaining: 2.37s
936:	learn: 1651.3770334	total: 34.7s	remaining: 2.33s
937:	learn: 1651.2849192	total: 34.7s	remaining: 2.3s
938:	learn: 1651.2420981	total: 34.8s	remaining: 2.26s
939:	learn: 1651.1610870	total: 34.8s	remaining: 2.22s
940:	learn: 1651.0465573	total: 34.9s	remaining: 2.19s
941:	learn: 1650.9578085	total: 34.9s	remaining: 2.15s
942:	learn: 1650.8343009	total: 34.9s	remaining: 2.11s
943:	learn: 1650.7619780	total: 35s	remaining: 2.07s
944:	learn: 1650.5560382	total: 35s	remaining: 2.04s
945:	learn: 1650.5123775	total: 35s	remaining: 2s
946:	learn: 1650.4054168	total: 35.1s	remaining: 1.96s
947:	learn: 1650.2885719	total: 35.1s	remaining: 1.93s
948:	learn: 1650.1734628	total: 35.2s	remaining: 1.89s
949:	learn: 1650.0581258	total: 35.2s	remaining: 1.85s
950:	learn: 1649.9169632	total: 35.2s	remaining: 1.81s
951:	learn: 1649.8252828	total: 35.3s	remaining: 1.78s
952:	learn: 1649.7296482	total: 35.3s	remaining: 1.74s
953:	learn: 1649.6505258	total: 35.4s	remaining: 1.71s
954:	learn: 1649.5890916	total: 35.4s	remaining: 1.67s
955:	learn: 1649.4368426	total: 35.4s	remaining: 1.63s
956:	learn: 1649.3196911	total: 35.5s	remaining: 1.59s
957:	learn: 1649.2532661	total: 35.5s	remaining: 1.56s
958:	learn: 1649.2175616	total: 35.6s	remaining: 1.52s
959:	learn: 1649.0834561	total: 35.6s	remaining: 1.48s
960:	learn: 1649.0037356	total: 35.6s	remaining: 1.45s
961:	learn: 1648.8409269	total: 35.7s	remaining: 1.41s
962:	learn: 1648.7188453	total: 35.7s	remaining: 1.37s
963:	learn: 1648.6059574	total: 35.7s	remaining: 1.33s
964:	learn: 1648.5285705	total: 35.8s	remaining: 1.3s
965:	learn: 1648.3602970	total: 35.8s	remaining: 1.26s
966:	learn: 1648.1969677	total: 35.9s	remaining: 1.22s
967:	learn: 1648.0400003	total: 35.9s	remaining: 1.19s
968:	learn: 1647.9618039	total: 35.9s	remaining: 1.15s
969:	learn: 1647.8484358	total: 36s	remaining: 1.11s
970:	learn: 1647.7433993	total: 36s	remaining: 1.07s
971:	learn: 1647.5803916	total: 36.1s	remaining: 1.04s
972:	learn: 1647.4964278	total: 36.1s	remaining: 1s
973:	learn: 1647.3827909	total: 36.1s	remaining: 965ms
974:	learn: 1647.2962397	total: 36.2s	remaining: 928ms
975:	learn: 1647.1139250	total: 36.2s	remaining: 891ms
976:	learn: 1646.9720188	total: 36.2s	remaining: 853ms
977:	learn: 1646.8701372	total: 36.3s	remaining: 816ms
978:	learn: 1646.7621446	total: 36.3s	remaining: 779ms
979:	learn: 1646.5576286	total: 36.4s	remaining: 742ms
980:	learn: 1646.4652620	total: 36.4s	remaining: 705ms
981:	learn: 1646.3408469	total: 36.4s	remaining: 668ms
982:	learn: 1646.1384320	total: 36.5s	remaining: 631ms
983:	learn: 1646.0612687	total: 36.5s	remaining: 594ms
984:	learn: 1645.8514629	total: 36.6s	remaining: 557ms
985:	learn: 1645.7763870	total: 36.6s	remaining: 520ms
986:	learn: 1645.7180827	total: 36.6s	remaining: 483ms
987:	learn: 1645.5174151	total: 36.7s	remaining: 445ms
988:	learn: 1645.4243853	total: 36.7s	remaining: 408ms
989:	learn: 1645.3219164	total: 36.8s	remaining: 371ms
990:	learn: 1645.1874401	total: 36.8s	remaining: 334ms
991:	learn: 1645.1293047	total: 36.8s	remaining: 297ms
992:	learn: 1645.0970315	total: 36.9s	remaining: 260ms
993:	learn: 1644.9962728	total: 36.9s	remaining: 223ms
994:	learn: 1644.8872127	total: 36.9s	remaining: 186ms
995:	learn: 1644.8296122	total: 37s	remaining: 149ms
996:	learn: 1644.7116419	total: 37s	remaining: 111ms
997:	learn: 1644.6253627	total: 37.1s	remaining: 74.3ms
998:	learn: 1644.4871057	total: 37.1s	remaining: 37.1ms
999:	learn: 1644.4227966	total: 37.1s	remaining: 0us
Learning rate set to 0.093467
0:	learn: 4277.2386860	total: 37.5ms	remaining: 37.5s
1:	learn: 4032.6477010	total: 68.9ms	remaining: 34.4s
2:	learn: 3808.1555221	total: 97.9ms	remaining: 32.5s
3:	learn: 3616.0740922	total: 126ms	remaining: 31.5s
4:	learn: 3439.2300473	total: 163ms	remaining: 32.4s
5:	learn: 3285.7991890	total: 191ms	remaining: 31.7s
6:	learn: 3148.5306234	total: 219ms	remaining: 31s
7:	learn: 3024.1460373	total: 256ms	remaining: 31.7s
8:	learn: 2912.8322757	total: 283ms	remaining: 31.2s
9:	learn: 2817.5780104	total: 311ms	remaining: 30.8s
10:	learn: 2729.8572278	total: 348ms	remaining: 31.3s
11:	learn: 2652.9948381	total: 375ms	remaining: 30.9s
12:	learn: 2585.8922852	total: 403ms	remaining: 30.6s
13:	learn: 2524.4160891	total: 443ms	remaining: 31.2s
14:	learn: 2470.4920396	total: 473ms	remaining: 31.1s
15:	learn: 2423.9701369	total: 498ms	remaining: 30.7s
16:	learn: 2378.9911579	total: 533ms	remaining: 30.8s
17:	learn: 2342.1163650	total: 561ms	remaining: 30.6s
18:	learn: 2306.5154988	total: 588ms	remaining: 30.4s
19:	learn: 2277.2430070	total: 617ms	remaining: 30.2s
20:	learn: 2249.0935259	total: 651ms	remaining: 30.3s
21:	learn: 2225.5446163	total: 677ms	remaining: 30.1s
22:	learn: 2205.5099001	total: 703ms	remaining: 29.9s
23:	learn: 2185.4149713	total: 740ms	remaining: 30.1s
24:	learn: 2168.8686458	total: 766ms	remaining: 29.9s
25:	learn: 2152.6436513	total: 791ms	remaining: 29.6s
26:	learn: 2137.8076627	total: 822ms	remaining: 29.6s
27:	learn: 2123.2768969	total: 850ms	remaining: 29.5s
28:	learn: 2110.7885981	total: 876ms	remaining: 29.3s
29:	learn: 2100.0711229	total: 901ms	remaining: 29.1s
30:	learn: 2089.3710725	total: 934ms	remaining: 29.2s
31:	learn: 2080.0626025	total: 958ms	remaining: 29s
32:	learn: 2070.9291686	total: 985ms	remaining: 28.9s
33:	learn: 2063.3706741	total: 1.02s	remaining: 29s
34:	learn: 2056.0921242	total: 1.04s	remaining: 28.8s
35:	learn: 2049.5636179	total: 1.07s	remaining: 28.7s
36:	learn: 2043.4168011	total: 1.1s	remaining: 28.6s
37:	learn: 2035.1724959	total: 1.13s	remaining: 28.6s
38:	learn: 2026.5914843	total: 1.15s	remaining: 28.4s
39:	learn: 2022.2101683	total: 1.18s	remaining: 28.3s
40:	learn: 2018.1275903	total: 1.21s	remaining: 28.4s
41:	learn: 2010.6744150	total: 1.24s	remaining: 28.3s
42:	learn: 2006.0738133	total: 1.26s	remaining: 28.2s
43:	learn: 2001.8509896	total: 1.3s	remaining: 28.2s
44:	learn: 1997.8904058	total: 1.32s	remaining: 28.1s
45:	learn: 1993.7361384	total: 1.35s	remaining: 27.9s
46:	learn: 1989.9652636	total: 1.37s	remaining: 27.8s
47:	learn: 1986.7223824	total: 1.41s	remaining: 27.9s
48:	learn: 1981.9722053	total: 1.44s	remaining: 27.9s
49:	learn: 1978.8174458	total: 1.47s	remaining: 27.9s
50:	learn: 1976.2932818	total: 1.5s	remaining: 28s
51:	learn: 1970.6526902	total: 1.53s	remaining: 27.9s
52:	learn: 1967.9581424	total: 1.56s	remaining: 27.9s
53:	learn: 1965.4738345	total: 1.59s	remaining: 27.9s
54:	learn: 1962.5036508	total: 1.63s	remaining: 28s
55:	learn: 1958.6809921	total: 1.67s	remaining: 28.1s
56:	learn: 1955.5580781	total: 1.7s	remaining: 28.2s
57:	learn: 1953.3887981	total: 1.73s	remaining: 28.2s
58:	learn: 1951.6462817	total: 1.76s	remaining: 28.1s
59:	learn: 1949.0203845	total: 1.8s	remaining: 28.2s
60:	learn: 1946.1212020	total: 1.83s	remaining: 28.2s
61:	learn: 1941.9323941	total: 1.86s	remaining: 28.1s
62:	learn: 1940.2736286	total: 1.91s	remaining: 28.3s
63:	learn: 1938.2698606	total: 1.93s	remaining: 28.2s
64:	learn: 1936.6338561	total: 1.95s	remaining: 28.1s
65:	learn: 1933.7779590	total: 1.99s	remaining: 28.1s
66:	learn: 1931.3999515	total: 2.01s	remaining: 28s
67:	learn: 1929.8982848	total: 2.04s	remaining: 27.9s
68:	learn: 1927.7129755	total: 2.06s	remaining: 27.8s
69:	learn: 1926.1079148	total: 2.1s	remaining: 27.8s
70:	learn: 1924.8548996	total: 2.12s	remaining: 27.8s
71:	learn: 1923.2521141	total: 2.15s	remaining: 27.7s
72:	learn: 1921.0713832	total: 2.18s	remaining: 27.7s
73:	learn: 1919.9159201	total: 2.21s	remaining: 27.6s
74:	learn: 1917.0147653	total: 2.23s	remaining: 27.6s
75:	learn: 1915.5793419	total: 2.26s	remaining: 27.5s
76:	learn: 1913.9638962	total: 2.29s	remaining: 27.5s
77:	learn: 1911.7715678	total: 2.32s	remaining: 27.4s
78:	learn: 1909.6030625	total: 2.35s	remaining: 27.3s
79:	learn: 1907.7182271	total: 2.38s	remaining: 27.4s
80:	learn: 1904.7749589	total: 2.42s	remaining: 27.4s
81:	learn: 1902.4830619	total: 2.45s	remaining: 27.4s
82:	learn: 1900.3388165	total: 2.49s	remaining: 27.5s
83:	learn: 1899.2461629	total: 2.52s	remaining: 27.5s
84:	learn: 1897.8193110	total: 2.54s	remaining: 27.4s
85:	learn: 1895.2296976	total: 2.58s	remaining: 27.4s
86:	learn: 1893.6002197	total: 2.61s	remaining: 27.4s
87:	learn: 1892.1006985	total: 2.63s	remaining: 27.3s
88:	learn: 1890.6834909	total: 2.66s	remaining: 27.3s
89:	learn: 1889.7731538	total: 2.69s	remaining: 27.2s
90:	learn: 1888.0035842	total: 2.72s	remaining: 27.2s
91:	learn: 1886.2187124	total: 2.75s	remaining: 27.1s
92:	learn: 1884.3966666	total: 2.78s	remaining: 27.1s
93:	learn: 1883.2593955	total: 2.81s	remaining: 27.1s
94:	learn: 1881.9590722	total: 2.83s	remaining: 27s
95:	learn: 1880.9972773	total: 2.87s	remaining: 27s
96:	learn: 1879.6939212	total: 2.89s	remaining: 26.9s
97:	learn: 1879.1119393	total: 2.92s	remaining: 26.9s
98:	learn: 1877.7171932	total: 2.95s	remaining: 26.8s
99:	learn: 1876.5546709	total: 2.98s	remaining: 26.8s
100:	learn: 1875.6280646	total: 3.01s	remaining: 26.8s
101:	learn: 1874.4961281	total: 3.03s	remaining: 26.7s
102:	learn: 1873.3406081	total: 3.07s	remaining: 26.8s
103:	learn: 1871.7290135	total: 3.1s	remaining: 26.7s
104:	learn: 1870.0415272	total: 3.13s	remaining: 26.7s
105:	learn: 1868.4537925	total: 3.17s	remaining: 26.7s
106:	learn: 1867.0480355	total: 3.19s	remaining: 26.7s
107:	learn: 1866.2399327	total: 3.22s	remaining: 26.6s
108:	learn: 1865.5084659	total: 3.25s	remaining: 26.6s
109:	learn: 1864.1244326	total: 3.28s	remaining: 26.5s
110:	learn: 1863.1017070	total: 3.3s	remaining: 26.5s
111:	learn: 1861.8065286	total: 3.33s	remaining: 26.4s
112:	learn: 1860.8256031	total: 3.36s	remaining: 26.4s
113:	learn: 1859.9372856	total: 3.39s	remaining: 26.3s
114:	learn: 1859.4139952	total: 3.41s	remaining: 26.3s
115:	learn: 1858.5868863	total: 3.44s	remaining: 26.2s
116:	learn: 1857.5906761	total: 3.47s	remaining: 26.2s
117:	learn: 1856.7949388	total: 3.5s	remaining: 26.1s
118:	learn: 1855.8602484	total: 3.53s	remaining: 26.1s
119:	learn: 1855.0933068	total: 3.56s	remaining: 26.1s
120:	learn: 1853.9458085	total: 3.58s	remaining: 26s
121:	learn: 1852.9244955	total: 3.61s	remaining: 26s
122:	learn: 1851.7661373	total: 3.64s	remaining: 26s
123:	learn: 1851.1748177	total: 3.67s	remaining: 25.9s
124:	learn: 1850.5588761	total: 3.69s	remaining: 25.9s
125:	learn: 1849.0836991	total: 3.73s	remaining: 25.8s
126:	learn: 1847.8563631	total: 3.76s	remaining: 25.8s
127:	learn: 1846.8058164	total: 3.78s	remaining: 25.8s
128:	learn: 1845.8637404	total: 3.81s	remaining: 25.7s
129:	learn: 1845.1379105	total: 3.84s	remaining: 25.7s
130:	learn: 1844.2246736	total: 3.87s	remaining: 25.7s
131:	learn: 1843.3161483	total: 3.9s	remaining: 25.6s
132:	learn: 1842.4607064	total: 3.93s	remaining: 25.6s
133:	learn: 1841.3802894	total: 3.96s	remaining: 25.6s
134:	learn: 1840.3311672	total: 3.98s	remaining: 25.5s
135:	learn: 1839.7568608	total: 4.01s	remaining: 25.5s
136:	learn: 1839.1948021	total: 4.04s	remaining: 25.5s
137:	learn: 1838.7489777	total: 4.07s	remaining: 25.4s
138:	learn: 1837.8040611	total: 4.09s	remaining: 25.4s
139:	learn: 1837.1506449	total: 4.13s	remaining: 25.4s
140:	learn: 1836.3586191	total: 4.15s	remaining: 25.3s
141:	learn: 1835.5653833	total: 4.18s	remaining: 25.3s
142:	learn: 1834.2220680	total: 4.21s	remaining: 25.2s
143:	learn: 1833.2386940	total: 4.24s	remaining: 25.2s
144:	learn: 1832.6200268	total: 4.27s	remaining: 25.2s
145:	learn: 1832.0213275	total: 4.3s	remaining: 25.2s
146:	learn: 1831.2797574	total: 4.34s	remaining: 25.2s
147:	learn: 1830.7013471	total: 4.36s	remaining: 25.1s
148:	learn: 1830.2170310	total: 4.38s	remaining: 25s
149:	learn: 1829.5785941	total: 4.42s	remaining: 25.1s
150:	learn: 1828.8860979	total: 4.45s	remaining: 25s
151:	learn: 1828.2185925	total: 4.47s	remaining: 25s
152:	learn: 1827.6911506	total: 4.5s	remaining: 24.9s
153:	learn: 1826.8271491	total: 4.53s	remaining: 24.9s
154:	learn: 1826.0499855	total: 4.56s	remaining: 24.9s
155:	learn: 1825.0277780	total: 4.59s	remaining: 24.8s
156:	learn: 1824.0599318	total: 4.62s	remaining: 24.8s
157:	learn: 1823.5836558	total: 4.65s	remaining: 24.8s
158:	learn: 1823.1139242	total: 4.67s	remaining: 24.7s
159:	learn: 1822.2340815	total: 4.71s	remaining: 24.7s
160:	learn: 1821.6191909	total: 4.74s	remaining: 24.7s
161:	learn: 1820.7569769	total: 4.76s	remaining: 24.6s
162:	learn: 1820.0052953	total: 4.79s	remaining: 24.6s
163:	learn: 1819.0820714	total: 4.82s	remaining: 24.6s
164:	learn: 1818.2490231	total: 4.85s	remaining: 24.5s
165:	learn: 1817.8989493	total: 4.87s	remaining: 24.5s
166:	learn: 1817.2620924	total: 4.9s	remaining: 24.5s
167:	learn: 1816.7117130	total: 4.93s	remaining: 24.4s
168:	learn: 1816.1395479	total: 4.96s	remaining: 24.4s
169:	learn: 1815.7994350	total: 4.98s	remaining: 24.3s
170:	learn: 1815.2587213	total: 5.01s	remaining: 24.3s
171:	learn: 1814.7101885	total: 5.04s	remaining: 24.3s
172:	learn: 1813.9918959	total: 5.06s	remaining: 24.2s
173:	learn: 1813.3698600	total: 5.1s	remaining: 24.2s
174:	learn: 1812.5043932	total: 5.13s	remaining: 24.2s
175:	learn: 1812.0363191	total: 5.15s	remaining: 24.1s
176:	learn: 1811.2982057	total: 5.18s	remaining: 24.1s
177:	learn: 1810.6963365	total: 5.21s	remaining: 24.1s
178:	learn: 1810.0871833	total: 5.24s	remaining: 24s
179:	learn: 1809.7765654	total: 5.26s	remaining: 24s
180:	learn: 1809.3629627	total: 5.29s	remaining: 24s
181:	learn: 1808.8134918	total: 5.32s	remaining: 23.9s
182:	learn: 1808.5397309	total: 5.34s	remaining: 23.9s
183:	learn: 1808.0341984	total: 5.38s	remaining: 23.9s
184:	learn: 1807.3412671	total: 5.41s	remaining: 23.8s
185:	learn: 1806.6129288	total: 5.43s	remaining: 23.8s
186:	learn: 1806.2315900	total: 5.47s	remaining: 23.8s
187:	learn: 1805.7666876	total: 5.5s	remaining: 23.8s
188:	learn: 1805.3278984	total: 5.53s	remaining: 23.7s
189:	learn: 1804.8153356	total: 5.55s	remaining: 23.7s
190:	learn: 1804.1291547	total: 5.58s	remaining: 23.7s
191:	learn: 1803.1806384	total: 5.61s	remaining: 23.6s
192:	learn: 1802.6394194	total: 5.64s	remaining: 23.6s
193:	learn: 1802.0706489	total: 5.68s	remaining: 23.6s
194:	learn: 1801.4956422	total: 5.7s	remaining: 23.5s
195:	learn: 1801.1139697	total: 5.73s	remaining: 23.5s
196:	learn: 1800.5403291	total: 5.75s	remaining: 23.5s
197:	learn: 1800.1359796	total: 5.78s	remaining: 23.4s
198:	learn: 1799.6853290	total: 5.81s	remaining: 23.4s
199:	learn: 1799.1304371	total: 5.83s	remaining: 23.3s
200:	learn: 1798.4257208	total: 5.87s	remaining: 23.3s
201:	learn: 1797.6912991	total: 5.89s	remaining: 23.3s
202:	learn: 1797.0924777	total: 5.92s	remaining: 23.2s
203:	learn: 1796.7146422	total: 5.95s	remaining: 23.2s
204:	learn: 1796.2290387	total: 5.98s	remaining: 23.2s
205:	learn: 1795.5335020	total: 6s	remaining: 23.1s
206:	learn: 1795.1015976	total: 6.03s	remaining: 23.1s
207:	learn: 1794.4695869	total: 6.06s	remaining: 23.1s
208:	learn: 1793.9634650	total: 6.09s	remaining: 23s
209:	learn: 1793.3472727	total: 6.11s	remaining: 23s
210:	learn: 1792.7876973	total: 6.14s	remaining: 23s
211:	learn: 1792.2920042	total: 6.17s	remaining: 22.9s
212:	learn: 1791.5263013	total: 6.2s	remaining: 22.9s
213:	learn: 1791.1337845	total: 6.22s	remaining: 22.9s
214:	learn: 1790.7559309	total: 6.26s	remaining: 22.8s
215:	learn: 1790.5375905	total: 6.28s	remaining: 22.8s
216:	learn: 1789.9413151	total: 6.31s	remaining: 22.8s
217:	learn: 1789.3554876	total: 6.34s	remaining: 22.7s
218:	learn: 1788.9315425	total: 6.37s	remaining: 22.7s
219:	learn: 1788.5330345	total: 6.39s	remaining: 22.7s
220:	learn: 1788.0191809	total: 6.42s	remaining: 22.6s
221:	learn: 1787.5678315	total: 6.46s	remaining: 22.6s
222:	learn: 1786.7654300	total: 6.48s	remaining: 22.6s
223:	learn: 1786.4057842	total: 6.51s	remaining: 22.6s
224:	learn: 1785.9267508	total: 6.54s	remaining: 22.5s
225:	learn: 1785.4356716	total: 6.57s	remaining: 22.5s
226:	learn: 1784.8156856	total: 6.59s	remaining: 22.5s
227:	learn: 1784.1141773	total: 6.62s	remaining: 22.4s
228:	learn: 1783.7743057	total: 6.66s	remaining: 22.4s
229:	learn: 1783.4491244	total: 6.69s	remaining: 22.4s
230:	learn: 1783.0832457	total: 6.71s	remaining: 22.4s
231:	learn: 1782.8434703	total: 6.75s	remaining: 22.3s
232:	learn: 1782.4566895	total: 6.78s	remaining: 22.3s
233:	learn: 1781.8108693	total: 6.8s	remaining: 22.3s
234:	learn: 1781.3064620	total: 6.84s	remaining: 22.3s
235:	learn: 1780.8422530	total: 6.86s	remaining: 22.2s
236:	learn: 1780.2612283	total: 6.89s	remaining: 22.2s
237:	learn: 1779.8045193	total: 6.92s	remaining: 22.2s
238:	learn: 1779.1997873	total: 6.95s	remaining: 22.1s
239:	learn: 1778.7861735	total: 6.98s	remaining: 22.1s
240:	learn: 1778.1657852	total: 7s	remaining: 22.1s
241:	learn: 1777.8261026	total: 7.04s	remaining: 22s
242:	learn: 1777.2219901	total: 7.07s	remaining: 22s
243:	learn: 1776.7904951	total: 7.09s	remaining: 22s
244:	learn: 1776.4942244	total: 7.13s	remaining: 22s
245:	learn: 1776.1700501	total: 7.16s	remaining: 21.9s
246:	learn: 1775.7273573	total: 7.18s	remaining: 21.9s
247:	learn: 1775.4453699	total: 7.21s	remaining: 21.9s
248:	learn: 1775.2582803	total: 7.24s	remaining: 21.8s
249:	learn: 1774.7715611	total: 7.27s	remaining: 21.8s
250:	learn: 1774.3905857	total: 7.3s	remaining: 21.8s
251:	learn: 1773.9931519	total: 7.33s	remaining: 21.8s
252:	learn: 1773.5990154	total: 7.36s	remaining: 21.7s
253:	learn: 1773.1561994	total: 7.39s	remaining: 21.7s
254:	learn: 1772.7200998	total: 7.42s	remaining: 21.7s
255:	learn: 1772.4368066	total: 7.45s	remaining: 21.7s
256:	learn: 1772.1918295	total: 7.47s	remaining: 21.6s
257:	learn: 1771.8129810	total: 7.51s	remaining: 21.6s
258:	learn: 1771.2055706	total: 7.54s	remaining: 21.6s
259:	learn: 1770.6137641	total: 7.57s	remaining: 21.5s
260:	learn: 1770.1851959	total: 7.6s	remaining: 21.5s
261:	learn: 1769.6735399	total: 7.63s	remaining: 21.5s
262:	learn: 1769.4599843	total: 7.66s	remaining: 21.5s
263:	learn: 1768.9958686	total: 7.68s	remaining: 21.4s
264:	learn: 1768.7487235	total: 7.71s	remaining: 21.4s
265:	learn: 1768.4169105	total: 7.74s	remaining: 21.4s
266:	learn: 1767.9900595	total: 7.77s	remaining: 21.3s
267:	learn: 1767.6399494	total: 7.8s	remaining: 21.3s
268:	learn: 1767.3382464	total: 7.83s	remaining: 21.3s
269:	learn: 1766.9581579	total: 7.86s	remaining: 21.2s
270:	learn: 1766.6912953	total: 7.88s	remaining: 21.2s
271:	learn: 1766.3953943	total: 7.91s	remaining: 21.2s
272:	learn: 1766.0479860	total: 7.94s	remaining: 21.1s
273:	learn: 1765.7212446	total: 7.96s	remaining: 21.1s
274:	learn: 1765.4036985	total: 8s	remaining: 21.1s
275:	learn: 1765.0164153	total: 8.03s	remaining: 21.1s
276:	learn: 1764.4988623	total: 8.05s	remaining: 21s
277:	learn: 1764.1724654	total: 8.09s	remaining: 21s
278:	learn: 1763.7969171	total: 8.12s	remaining: 21s
279:	learn: 1763.4216090	total: 8.14s	remaining: 20.9s
280:	learn: 1763.1587711	total: 8.17s	remaining: 20.9s
281:	learn: 1762.9193544	total: 8.2s	remaining: 20.9s
282:	learn: 1762.4394396	total: 8.23s	remaining: 20.9s
283:	learn: 1762.1504811	total: 8.26s	remaining: 20.8s
284:	learn: 1761.9679651	total: 8.29s	remaining: 20.8s
285:	learn: 1761.6612142	total: 8.32s	remaining: 20.8s
286:	learn: 1761.1932486	total: 8.35s	remaining: 20.7s
287:	learn: 1760.7917529	total: 8.38s	remaining: 20.7s
288:	learn: 1760.3798841	total: 8.41s	remaining: 20.7s
289:	learn: 1760.0327060	total: 8.44s	remaining: 20.7s
290:	learn: 1759.7745621	total: 8.46s	remaining: 20.6s
291:	learn: 1759.2513694	total: 8.5s	remaining: 20.6s
292:	learn: 1758.9318977	total: 8.52s	remaining: 20.6s
293:	learn: 1758.7699805	total: 8.55s	remaining: 20.5s
294:	learn: 1758.4562110	total: 8.58s	remaining: 20.5s
295:	learn: 1757.9987372	total: 8.61s	remaining: 20.5s
296:	learn: 1757.7823393	total: 8.63s	remaining: 20.4s
297:	learn: 1757.4749337	total: 8.66s	remaining: 20.4s
298:	learn: 1757.0239135	total: 8.69s	remaining: 20.4s
299:	learn: 1756.6517902	total: 8.71s	remaining: 20.3s
300:	learn: 1756.3336632	total: 8.74s	remaining: 20.3s
301:	learn: 1756.0756178	total: 8.77s	remaining: 20.3s
302:	learn: 1755.7958319	total: 8.8s	remaining: 20.2s
303:	learn: 1755.5439262	total: 8.82s	remaining: 20.2s
304:	learn: 1755.2472297	total: 8.85s	remaining: 20.2s
305:	learn: 1754.8539476	total: 8.89s	remaining: 20.2s
306:	learn: 1754.5647213	total: 8.91s	remaining: 20.1s
307:	learn: 1754.2183540	total: 8.94s	remaining: 20.1s
308:	learn: 1754.0150596	total: 8.98s	remaining: 20.1s
309:	learn: 1753.5839210	total: 9.01s	remaining: 20s
310:	learn: 1753.3801192	total: 9.04s	remaining: 20s
311:	learn: 1752.9340494	total: 9.07s	remaining: 20s
312:	learn: 1752.6737970	total: 9.1s	remaining: 20s
313:	learn: 1752.4405622	total: 9.13s	remaining: 19.9s
314:	learn: 1752.0143585	total: 9.17s	remaining: 19.9s
315:	learn: 1751.7429201	total: 9.2s	remaining: 19.9s
316:	learn: 1751.4630161	total: 9.23s	remaining: 19.9s
317:	learn: 1751.0644207	total: 9.26s	remaining: 19.9s
318:	learn: 1750.7127820	total: 9.29s	remaining: 19.8s
319:	learn: 1750.3590842	total: 9.32s	remaining: 19.8s
320:	learn: 1750.1328069	total: 9.35s	remaining: 19.8s
321:	learn: 1749.8882872	total: 9.38s	remaining: 19.7s
322:	learn: 1749.6942677	total: 9.4s	remaining: 19.7s
323:	learn: 1749.2208487	total: 9.43s	remaining: 19.7s
324:	learn: 1748.8955142	total: 9.46s	remaining: 19.7s
325:	learn: 1748.6624081	total: 9.49s	remaining: 19.6s
326:	learn: 1748.3307721	total: 9.51s	remaining: 19.6s
327:	learn: 1748.0456127	total: 9.55s	remaining: 19.6s
328:	learn: 1747.7375802	total: 9.57s	remaining: 19.5s
329:	learn: 1747.4476781	total: 9.6s	remaining: 19.5s
330:	learn: 1747.0838595	total: 9.63s	remaining: 19.5s
331:	learn: 1746.8258646	total: 9.66s	remaining: 19.4s
332:	learn: 1746.6034330	total: 9.69s	remaining: 19.4s
333:	learn: 1746.3802371	total: 9.71s	remaining: 19.4s
334:	learn: 1745.9591388	total: 9.74s	remaining: 19.3s
335:	learn: 1745.7044707	total: 9.77s	remaining: 19.3s
336:	learn: 1745.4049604	total: 9.8s	remaining: 19.3s
337:	learn: 1745.1878059	total: 9.83s	remaining: 19.3s
338:	learn: 1744.9137669	total: 9.86s	remaining: 19.2s
339:	learn: 1744.5298261	total: 9.89s	remaining: 19.2s
340:	learn: 1744.2651723	total: 9.92s	remaining: 19.2s
341:	learn: 1743.8730136	total: 9.95s	remaining: 19.1s
342:	learn: 1743.5214814	total: 9.98s	remaining: 19.1s
343:	learn: 1743.1863890	total: 10s	remaining: 19.1s
344:	learn: 1742.8006334	total: 10s	remaining: 19.1s
345:	learn: 1742.6200979	total: 10.1s	remaining: 19s
346:	learn: 1742.4595162	total: 10.1s	remaining: 19s
347:	learn: 1742.1631047	total: 10.1s	remaining: 19s
348:	learn: 1741.9838450	total: 10.2s	remaining: 18.9s
349:	learn: 1741.6401679	total: 10.2s	remaining: 18.9s
350:	learn: 1741.3587550	total: 10.2s	remaining: 18.9s
351:	learn: 1741.0191492	total: 10.2s	remaining: 18.9s
352:	learn: 1740.7298219	total: 10.3s	remaining: 18.8s
353:	learn: 1740.2958804	total: 10.3s	remaining: 18.8s
354:	learn: 1739.9614148	total: 10.3s	remaining: 18.8s
355:	learn: 1739.6312553	total: 10.4s	remaining: 18.7s
356:	learn: 1739.3618050	total: 10.4s	remaining: 18.7s
357:	learn: 1739.1567641	total: 10.4s	remaining: 18.7s
358:	learn: 1738.9109630	total: 10.4s	remaining: 18.6s
359:	learn: 1738.6696861	total: 10.5s	remaining: 18.6s
360:	learn: 1738.3902903	total: 10.5s	remaining: 18.6s
361:	learn: 1738.1346637	total: 10.5s	remaining: 18.6s
362:	learn: 1737.7414617	total: 10.6s	remaining: 18.5s
363:	learn: 1737.4071684	total: 10.6s	remaining: 18.5s
364:	learn: 1737.0857966	total: 10.6s	remaining: 18.5s
365:	learn: 1736.7076707	total: 10.7s	remaining: 18.5s
366:	learn: 1736.3676687	total: 10.7s	remaining: 18.4s
367:	learn: 1736.1446879	total: 10.7s	remaining: 18.4s
368:	learn: 1735.7844798	total: 10.7s	remaining: 18.4s
369:	learn: 1735.5221539	total: 10.8s	remaining: 18.3s
370:	learn: 1735.2283852	total: 10.8s	remaining: 18.3s
371:	learn: 1734.9898507	total: 10.8s	remaining: 18.3s
372:	learn: 1734.6273420	total: 10.8s	remaining: 18.2s
373:	learn: 1734.4658828	total: 10.9s	remaining: 18.2s
374:	learn: 1734.1797135	total: 10.9s	remaining: 18.2s
375:	learn: 1733.8898912	total: 10.9s	remaining: 18.1s
376:	learn: 1733.5755101	total: 11s	remaining: 18.1s
377:	learn: 1733.3665108	total: 11s	remaining: 18.1s
378:	learn: 1733.0898889	total: 11s	remaining: 18.1s
379:	learn: 1732.7490094	total: 11s	remaining: 18s
380:	learn: 1732.4499560	total: 11.1s	remaining: 18s
381:	learn: 1732.1169000	total: 11.1s	remaining: 18s
382:	learn: 1731.7940426	total: 11.1s	remaining: 17.9s
383:	learn: 1731.5233118	total: 11.2s	remaining: 17.9s
384:	learn: 1731.1645556	total: 11.2s	remaining: 17.9s
385:	learn: 1730.7802649	total: 11.2s	remaining: 17.9s
386:	learn: 1730.4648749	total: 11.3s	remaining: 17.8s
387:	learn: 1730.2574649	total: 11.3s	remaining: 17.8s
388:	learn: 1730.0277637	total: 11.3s	remaining: 17.8s
389:	learn: 1729.7370470	total: 11.3s	remaining: 17.7s
390:	learn: 1729.3090300	total: 11.4s	remaining: 17.7s
391:	learn: 1729.0378373	total: 11.4s	remaining: 17.7s
392:	learn: 1728.7318887	total: 11.4s	remaining: 17.7s
393:	learn: 1728.4048513	total: 11.5s	remaining: 17.6s
394:	learn: 1728.0235725	total: 11.5s	remaining: 17.6s
395:	learn: 1727.6950999	total: 11.5s	remaining: 17.6s
396:	learn: 1727.4460374	total: 11.5s	remaining: 17.5s
397:	learn: 1727.1829721	total: 11.6s	remaining: 17.5s
398:	learn: 1726.9565435	total: 11.6s	remaining: 17.5s
399:	learn: 1726.6639978	total: 11.7s	remaining: 17.5s
400:	learn: 1726.3944984	total: 11.7s	remaining: 17.5s
401:	learn: 1725.8735467	total: 11.7s	remaining: 17.4s
402:	learn: 1725.7052120	total: 11.8s	remaining: 17.4s
403:	learn: 1725.5468968	total: 11.8s	remaining: 17.4s
404:	learn: 1725.3331349	total: 11.8s	remaining: 17.4s
405:	learn: 1725.0923359	total: 11.8s	remaining: 17.3s
406:	learn: 1724.9687416	total: 11.9s	remaining: 17.3s
407:	learn: 1724.8119787	total: 11.9s	remaining: 17.3s
408:	learn: 1724.5689603	total: 11.9s	remaining: 17.3s
409:	learn: 1724.2986576	total: 12s	remaining: 17.2s
410:	learn: 1724.1464751	total: 12s	remaining: 17.2s
411:	learn: 1723.8610766	total: 12s	remaining: 17.2s
412:	learn: 1723.6498411	total: 12.1s	remaining: 17.1s
413:	learn: 1723.4199699	total: 12.1s	remaining: 17.1s
414:	learn: 1723.2249446	total: 12.1s	remaining: 17.1s
415:	learn: 1722.9882141	total: 12.2s	remaining: 17.1s
416:	learn: 1722.8051836	total: 12.2s	remaining: 17s
417:	learn: 1722.4917493	total: 12.2s	remaining: 17s
418:	learn: 1722.2621785	total: 12.2s	remaining: 17s
419:	learn: 1722.0006083	total: 12.3s	remaining: 16.9s
420:	learn: 1721.8358539	total: 12.3s	remaining: 16.9s
421:	learn: 1721.4504458	total: 12.3s	remaining: 16.9s
422:	learn: 1721.2309071	total: 12.3s	remaining: 16.8s
423:	learn: 1721.0065340	total: 12.4s	remaining: 16.8s
424:	learn: 1720.7786383	total: 12.4s	remaining: 16.8s
425:	learn: 1720.5819168	total: 12.4s	remaining: 16.8s
426:	learn: 1720.4528220	total: 12.5s	remaining: 16.7s
427:	learn: 1720.2183993	total: 12.5s	remaining: 16.7s
428:	learn: 1720.0816345	total: 12.5s	remaining: 16.6s
429:	learn: 1719.5931471	total: 12.5s	remaining: 16.6s
430:	learn: 1719.4008621	total: 12.6s	remaining: 16.6s
431:	learn: 1719.1768370	total: 12.6s	remaining: 16.6s
432:	learn: 1719.0166373	total: 12.6s	remaining: 16.5s
433:	learn: 1718.7629883	total: 12.6s	remaining: 16.5s
434:	learn: 1718.4763898	total: 12.7s	remaining: 16.5s
435:	learn: 1718.1423764	total: 12.7s	remaining: 16.4s
436:	learn: 1717.8608265	total: 12.7s	remaining: 16.4s
437:	learn: 1717.7036752	total: 12.8s	remaining: 16.4s
438:	learn: 1717.3906696	total: 12.8s	remaining: 16.3s
439:	learn: 1717.1210499	total: 12.8s	remaining: 16.3s
440:	learn: 1716.9560759	total: 12.8s	remaining: 16.3s
441:	learn: 1716.7197737	total: 12.9s	remaining: 16.3s
442:	learn: 1716.5839613	total: 12.9s	remaining: 16.2s
443:	learn: 1716.3577222	total: 12.9s	remaining: 16.2s
444:	learn: 1716.1072867	total: 13s	remaining: 16.2s
445:	learn: 1715.7791102	total: 13s	remaining: 16.1s
446:	learn: 1715.5883602	total: 13s	remaining: 16.1s
447:	learn: 1715.2437751	total: 13s	remaining: 16.1s
448:	learn: 1714.9405904	total: 13.1s	remaining: 16s
449:	learn: 1714.5672153	total: 13.1s	remaining: 16s
450:	learn: 1714.1272687	total: 13.1s	remaining: 16s
451:	learn: 1713.8258765	total: 13.2s	remaining: 16s
452:	learn: 1713.5438880	total: 13.2s	remaining: 15.9s
453:	learn: 1713.3124349	total: 13.2s	remaining: 15.9s
454:	learn: 1712.8347346	total: 13.2s	remaining: 15.9s
455:	learn: 1712.6316354	total: 13.3s	remaining: 15.8s
456:	learn: 1712.4735989	total: 13.3s	remaining: 15.8s
457:	learn: 1712.2656260	total: 13.3s	remaining: 15.8s
458:	learn: 1712.1656384	total: 13.3s	remaining: 15.7s
459:	learn: 1711.9870762	total: 13.4s	remaining: 15.7s
460:	learn: 1711.8767364	total: 13.4s	remaining: 15.7s
461:	learn: 1711.6441626	total: 13.4s	remaining: 15.6s
462:	learn: 1711.4446273	total: 13.5s	remaining: 15.6s
463:	learn: 1711.0563310	total: 13.5s	remaining: 15.6s
464:	learn: 1710.7795536	total: 13.5s	remaining: 15.6s
465:	learn: 1710.5167999	total: 13.5s	remaining: 15.5s
466:	learn: 1710.0889112	total: 13.6s	remaining: 15.5s
467:	learn: 1709.9635676	total: 13.6s	remaining: 15.5s
468:	learn: 1709.7393706	total: 13.6s	remaining: 15.4s
469:	learn: 1709.5177423	total: 13.7s	remaining: 15.4s
470:	learn: 1709.2314713	total: 13.7s	remaining: 15.4s
471:	learn: 1709.0086938	total: 13.7s	remaining: 15.3s
472:	learn: 1708.7147268	total: 13.7s	remaining: 15.3s
473:	learn: 1708.4088735	total: 13.8s	remaining: 15.3s
474:	learn: 1708.2548565	total: 13.8s	remaining: 15.3s
475:	learn: 1707.8560462	total: 13.8s	remaining: 15.2s
476:	learn: 1707.6604211	total: 13.9s	remaining: 15.2s
477:	learn: 1707.5380311	total: 13.9s	remaining: 15.2s
478:	learn: 1707.4064951	total: 13.9s	remaining: 15.1s
479:	learn: 1707.1408656	total: 13.9s	remaining: 15.1s
480:	learn: 1706.9886452	total: 14s	remaining: 15.1s
481:	learn: 1706.7313445	total: 14s	remaining: 15s
482:	learn: 1706.5795363	total: 14s	remaining: 15s
483:	learn: 1706.4430943	total: 14.1s	remaining: 15s
484:	learn: 1706.3323181	total: 14.1s	remaining: 15s
485:	learn: 1706.0819412	total: 14.1s	remaining: 14.9s
486:	learn: 1705.8951706	total: 14.1s	remaining: 14.9s
487:	learn: 1705.6461846	total: 14.2s	remaining: 14.9s
488:	learn: 1705.3290078	total: 14.2s	remaining: 14.8s
489:	learn: 1705.0990387	total: 14.2s	remaining: 14.8s
490:	learn: 1704.8864308	total: 14.3s	remaining: 14.8s
491:	learn: 1704.6162689	total: 14.3s	remaining: 14.8s
492:	learn: 1704.4577032	total: 14.3s	remaining: 14.7s
493:	learn: 1704.2115296	total: 14.3s	remaining: 14.7s
494:	learn: 1703.9893058	total: 14.4s	remaining: 14.7s
495:	learn: 1703.8285473	total: 14.4s	remaining: 14.6s
496:	learn: 1703.6557770	total: 14.4s	remaining: 14.6s
497:	learn: 1703.4495885	total: 14.5s	remaining: 14.6s
498:	learn: 1703.2790702	total: 14.5s	remaining: 14.5s
499:	learn: 1703.1324970	total: 14.5s	remaining: 14.5s
500:	learn: 1702.9442527	total: 14.5s	remaining: 14.5s
501:	learn: 1702.7308946	total: 14.6s	remaining: 14.4s
502:	learn: 1702.5723240	total: 14.6s	remaining: 14.4s
503:	learn: 1702.3487256	total: 14.6s	remaining: 14.4s
504:	learn: 1702.1368932	total: 14.6s	remaining: 14.4s
505:	learn: 1701.9646900	total: 14.7s	remaining: 14.3s
506:	learn: 1701.6340688	total: 14.7s	remaining: 14.3s
507:	learn: 1701.4899583	total: 14.7s	remaining: 14.3s
508:	learn: 1701.3457213	total: 14.8s	remaining: 14.2s
509:	learn: 1701.1697283	total: 14.8s	remaining: 14.2s
510:	learn: 1701.0372405	total: 14.8s	remaining: 14.2s
511:	learn: 1700.9051215	total: 14.9s	remaining: 14.2s
512:	learn: 1700.7802272	total: 14.9s	remaining: 14.1s
513:	learn: 1700.6424140	total: 14.9s	remaining: 14.1s
514:	learn: 1700.4822122	total: 14.9s	remaining: 14.1s
515:	learn: 1700.2545427	total: 15s	remaining: 14s
516:	learn: 1700.1016135	total: 15s	remaining: 14s
517:	learn: 1699.9299846	total: 15s	remaining: 14s
518:	learn: 1699.7150241	total: 15.1s	remaining: 14s
519:	learn: 1699.5735557	total: 15.1s	remaining: 13.9s
520:	learn: 1699.4129720	total: 15.1s	remaining: 13.9s
521:	learn: 1699.2043953	total: 15.1s	remaining: 13.9s
522:	learn: 1698.9419279	total: 15.2s	remaining: 13.8s
523:	learn: 1698.7586777	total: 15.2s	remaining: 13.8s
524:	learn: 1698.5739071	total: 15.2s	remaining: 13.8s
525:	learn: 1698.3100633	total: 15.3s	remaining: 13.7s
526:	learn: 1698.1364775	total: 15.3s	remaining: 13.7s
527:	learn: 1697.9392171	total: 15.3s	remaining: 13.7s
528:	learn: 1697.6870421	total: 15.3s	remaining: 13.7s
529:	learn: 1697.4893731	total: 15.4s	remaining: 13.6s
530:	learn: 1697.2302035	total: 15.4s	remaining: 13.6s
531:	learn: 1697.1264438	total: 15.4s	remaining: 13.6s
532:	learn: 1697.0051951	total: 15.5s	remaining: 13.6s
533:	learn: 1696.7794168	total: 15.5s	remaining: 13.5s
534:	learn: 1696.6424670	total: 15.5s	remaining: 13.5s
535:	learn: 1696.4196493	total: 15.6s	remaining: 13.5s
536:	learn: 1696.2661922	total: 15.6s	remaining: 13.4s
537:	learn: 1696.0052766	total: 15.6s	remaining: 13.4s
538:	learn: 1695.8125702	total: 15.6s	remaining: 13.4s
539:	learn: 1695.5722747	total: 15.7s	remaining: 13.3s
540:	learn: 1695.3909911	total: 15.7s	remaining: 13.3s
541:	learn: 1695.2357349	total: 15.7s	remaining: 13.3s
542:	learn: 1694.9968641	total: 15.7s	remaining: 13.3s
543:	learn: 1694.8418895	total: 15.8s	remaining: 13.2s
544:	learn: 1694.6510611	total: 15.8s	remaining: 13.2s
545:	learn: 1694.4882830	total: 15.8s	remaining: 13.2s
546:	learn: 1694.2697286	total: 15.9s	remaining: 13.1s
547:	learn: 1694.1700307	total: 15.9s	remaining: 13.1s
548:	learn: 1693.9446996	total: 15.9s	remaining: 13.1s
549:	learn: 1693.8207678	total: 16s	remaining: 13.1s
550:	learn: 1693.6151541	total: 16s	remaining: 13s
551:	learn: 1693.4111451	total: 16s	remaining: 13s
552:	learn: 1693.1339002	total: 16s	remaining: 13s
553:	learn: 1692.8004559	total: 16.1s	remaining: 12.9s
554:	learn: 1692.6016845	total: 16.1s	remaining: 12.9s
555:	learn: 1692.3524062	total: 16.1s	remaining: 12.9s
556:	learn: 1692.2172105	total: 16.2s	remaining: 12.9s
557:	learn: 1692.1509968	total: 16.2s	remaining: 12.8s
558:	learn: 1691.9240536	total: 16.2s	remaining: 12.8s
559:	learn: 1691.6650618	total: 16.3s	remaining: 12.8s
560:	learn: 1691.4490233	total: 16.3s	remaining: 12.7s
561:	learn: 1691.2757003	total: 16.3s	remaining: 12.7s
562:	learn: 1691.1050821	total: 16.3s	remaining: 12.7s
563:	learn: 1690.9298014	total: 16.4s	remaining: 12.7s
564:	learn: 1690.7593795	total: 16.4s	remaining: 12.6s
565:	learn: 1690.4267327	total: 16.4s	remaining: 12.6s
566:	learn: 1690.0638899	total: 16.5s	remaining: 12.6s
567:	learn: 1689.9009723	total: 16.5s	remaining: 12.5s
568:	learn: 1689.7827637	total: 16.5s	remaining: 12.5s
569:	learn: 1689.6233618	total: 16.5s	remaining: 12.5s
570:	learn: 1689.4661668	total: 16.6s	remaining: 12.5s
571:	learn: 1689.3226015	total: 16.6s	remaining: 12.4s
572:	learn: 1689.1569215	total: 16.6s	remaining: 12.4s
573:	learn: 1688.8427672	total: 16.7s	remaining: 12.4s
574:	learn: 1688.6683430	total: 16.7s	remaining: 12.3s
575:	learn: 1688.4707827	total: 16.7s	remaining: 12.3s
576:	learn: 1688.2997034	total: 16.7s	remaining: 12.3s
577:	learn: 1688.0691792	total: 16.8s	remaining: 12.3s
578:	learn: 1687.9089709	total: 16.8s	remaining: 12.2s
579:	learn: 1687.7449830	total: 16.8s	remaining: 12.2s
580:	learn: 1687.5392990	total: 16.9s	remaining: 12.2s
581:	learn: 1687.3753262	total: 16.9s	remaining: 12.1s
582:	learn: 1687.2173582	total: 16.9s	remaining: 12.1s
583:	learn: 1687.0834067	total: 17s	remaining: 12.1s
584:	learn: 1686.9619118	total: 17s	remaining: 12.1s
585:	learn: 1686.8216626	total: 17s	remaining: 12s
586:	learn: 1686.6934718	total: 17s	remaining: 12s
587:	learn: 1686.5704256	total: 17.1s	remaining: 12s
588:	learn: 1686.4638830	total: 17.1s	remaining: 11.9s
589:	learn: 1686.2681391	total: 17.1s	remaining: 11.9s
590:	learn: 1686.1500491	total: 17.2s	remaining: 11.9s
591:	learn: 1685.7848879	total: 17.2s	remaining: 11.9s
592:	learn: 1685.5767093	total: 17.2s	remaining: 11.8s
593:	learn: 1685.3857174	total: 17.3s	remaining: 11.8s
594:	learn: 1685.1647869	total: 17.3s	remaining: 11.8s
595:	learn: 1684.9993421	total: 17.3s	remaining: 11.7s
596:	learn: 1684.9340374	total: 17.4s	remaining: 11.7s
597:	learn: 1684.7370741	total: 17.4s	remaining: 11.7s
598:	learn: 1684.6488975	total: 17.4s	remaining: 11.7s
599:	learn: 1684.4532853	total: 17.4s	remaining: 11.6s
600:	learn: 1684.3054463	total: 17.5s	remaining: 11.6s
601:	learn: 1684.1422881	total: 17.5s	remaining: 11.6s
602:	learn: 1683.9874557	total: 17.5s	remaining: 11.5s
603:	learn: 1683.7906024	total: 17.6s	remaining: 11.5s
604:	learn: 1683.6008706	total: 17.6s	remaining: 11.5s
605:	learn: 1683.4478961	total: 17.6s	remaining: 11.4s
606:	learn: 1683.2603235	total: 17.6s	remaining: 11.4s
607:	learn: 1682.9577354	total: 17.7s	remaining: 11.4s
608:	learn: 1682.7229346	total: 17.7s	remaining: 11.4s
609:	learn: 1682.5782078	total: 17.7s	remaining: 11.3s
610:	learn: 1682.4277250	total: 17.8s	remaining: 11.3s
611:	learn: 1682.2573466	total: 17.8s	remaining: 11.3s
612:	learn: 1682.1148474	total: 17.8s	remaining: 11.3s
613:	learn: 1682.0429406	total: 17.9s	remaining: 11.2s
614:	learn: 1681.9523659	total: 17.9s	remaining: 11.2s
615:	learn: 1681.7280430	total: 17.9s	remaining: 11.2s
616:	learn: 1681.5854086	total: 17.9s	remaining: 11.1s
617:	learn: 1681.3631487	total: 18s	remaining: 11.1s
618:	learn: 1681.2270050	total: 18s	remaining: 11.1s
619:	learn: 1681.0593979	total: 18s	remaining: 11s
620:	learn: 1680.9225028	total: 18s	remaining: 11s
621:	learn: 1680.7998821	total: 18.1s	remaining: 11s
622:	learn: 1680.6420615	total: 18.1s	remaining: 11s
623:	learn: 1680.4620367	total: 18.1s	remaining: 10.9s
624:	learn: 1680.2278570	total: 18.2s	remaining: 10.9s
625:	learn: 1679.9849113	total: 18.2s	remaining: 10.9s
626:	learn: 1679.8004431	total: 18.2s	remaining: 10.8s
627:	learn: 1679.6356711	total: 18.3s	remaining: 10.8s
628:	learn: 1679.4122985	total: 18.3s	remaining: 10.8s
629:	learn: 1679.2256760	total: 18.3s	remaining: 10.8s
630:	learn: 1679.0571388	total: 18.3s	remaining: 10.7s
631:	learn: 1678.8370154	total: 18.4s	remaining: 10.7s
632:	learn: 1678.6746439	total: 18.4s	remaining: 10.7s
633:	learn: 1678.5602932	total: 18.4s	remaining: 10.6s
634:	learn: 1678.3678930	total: 18.5s	remaining: 10.6s
635:	learn: 1678.2312164	total: 18.5s	remaining: 10.6s
636:	learn: 1678.0272103	total: 18.5s	remaining: 10.6s
637:	learn: 1677.9570091	total: 18.6s	remaining: 10.5s
638:	learn: 1677.7705891	total: 18.6s	remaining: 10.5s
639:	learn: 1677.6178469	total: 18.6s	remaining: 10.5s
640:	learn: 1677.3150912	total: 18.7s	remaining: 10.4s
641:	learn: 1677.1322319	total: 18.7s	remaining: 10.4s
642:	learn: 1676.9370416	total: 18.7s	remaining: 10.4s
643:	learn: 1676.7887142	total: 18.7s	remaining: 10.4s
644:	learn: 1676.6098884	total: 18.8s	remaining: 10.3s
645:	learn: 1676.4826128	total: 18.8s	remaining: 10.3s
646:	learn: 1676.2867199	total: 18.8s	remaining: 10.3s
647:	learn: 1676.1723545	total: 18.9s	remaining: 10.2s
648:	learn: 1676.0412755	total: 18.9s	remaining: 10.2s
649:	learn: 1675.7901147	total: 18.9s	remaining: 10.2s
650:	learn: 1675.6261420	total: 18.9s	remaining: 10.2s
651:	learn: 1675.4651997	total: 19s	remaining: 10.1s
652:	learn: 1675.3580253	total: 19s	remaining: 10.1s
653:	learn: 1675.2157450	total: 19s	remaining: 10.1s
654:	learn: 1675.0639806	total: 19.1s	remaining: 10s
655:	learn: 1675.0019543	total: 19.1s	remaining: 10s
656:	learn: 1674.9096325	total: 19.1s	remaining: 9.98s
657:	learn: 1674.8163987	total: 19.2s	remaining: 9.95s
658:	learn: 1674.6538250	total: 19.2s	remaining: 9.93s
659:	learn: 1674.5183279	total: 19.2s	remaining: 9.9s
660:	learn: 1674.3156237	total: 19.2s	remaining: 9.87s
661:	learn: 1674.0804233	total: 19.3s	remaining: 9.84s
662:	learn: 1673.8530593	total: 19.3s	remaining: 9.81s
663:	learn: 1673.6804681	total: 19.3s	remaining: 9.79s
664:	learn: 1673.5891327	total: 19.4s	remaining: 9.76s
665:	learn: 1673.4475777	total: 19.4s	remaining: 9.73s
666:	learn: 1673.3097337	total: 19.4s	remaining: 9.7s
667:	learn: 1673.1963480	total: 19.5s	remaining: 9.67s
668:	learn: 1673.0075437	total: 19.5s	remaining: 9.64s
669:	learn: 1672.8187252	total: 19.5s	remaining: 9.62s
670:	learn: 1672.6820960	total: 19.6s	remaining: 9.59s
671:	learn: 1672.5616633	total: 19.6s	remaining: 9.56s
672:	learn: 1672.4028104	total: 19.6s	remaining: 9.53s
673:	learn: 1672.2146590	total: 19.6s	remaining: 9.5s
674:	learn: 1672.0689577	total: 19.7s	remaining: 9.47s
675:	learn: 1671.9054512	total: 19.7s	remaining: 9.44s
676:	learn: 1671.8454341	total: 19.7s	remaining: 9.41s
677:	learn: 1671.7301053	total: 19.8s	remaining: 9.38s
678:	learn: 1671.5368074	total: 19.8s	remaining: 9.36s
679:	learn: 1671.2897378	total: 19.8s	remaining: 9.33s
680:	learn: 1671.0059839	total: 19.9s	remaining: 9.31s
681:	learn: 1670.8800207	total: 19.9s	remaining: 9.28s
682:	learn: 1670.7905654	total: 19.9s	remaining: 9.24s
683:	learn: 1670.5875903	total: 20s	remaining: 9.22s
684:	learn: 1670.4816350	total: 20s	remaining: 9.19s
685:	learn: 1670.2326904	total: 20s	remaining: 9.16s
686:	learn: 1670.0692922	total: 20s	remaining: 9.13s
687:	learn: 1669.9022250	total: 20.1s	remaining: 9.1s
688:	learn: 1669.8239963	total: 20.1s	remaining: 9.07s
689:	learn: 1669.6953137	total: 20.1s	remaining: 9.04s
690:	learn: 1669.5804847	total: 20.2s	remaining: 9.01s
691:	learn: 1669.5241003	total: 20.2s	remaining: 8.98s
692:	learn: 1669.3037806	total: 20.2s	remaining: 8.95s
693:	learn: 1669.1291526	total: 20.2s	remaining: 8.92s
694:	learn: 1668.9536510	total: 20.3s	remaining: 8.89s
695:	learn: 1668.8198107	total: 20.3s	remaining: 8.87s
696:	learn: 1668.7418047	total: 20.3s	remaining: 8.84s
697:	learn: 1668.6104481	total: 20.4s	remaining: 8.81s
698:	learn: 1668.2861595	total: 20.4s	remaining: 8.78s
699:	learn: 1668.1785980	total: 20.4s	remaining: 8.75s
700:	learn: 1668.1032665	total: 20.4s	remaining: 8.72s
701:	learn: 1667.9821402	total: 20.5s	remaining: 8.69s
702:	learn: 1667.8719981	total: 20.5s	remaining: 8.66s
703:	learn: 1667.8033297	total: 20.5s	remaining: 8.63s
704:	learn: 1667.6767404	total: 20.6s	remaining: 8.61s
705:	learn: 1667.6431346	total: 20.6s	remaining: 8.57s
706:	learn: 1667.5783451	total: 20.6s	remaining: 8.54s
707:	learn: 1667.3630410	total: 20.7s	remaining: 8.52s
708:	learn: 1667.2218648	total: 20.7s	remaining: 8.49s
709:	learn: 1667.0888187	total: 20.7s	remaining: 8.46s
710:	learn: 1666.8875215	total: 20.7s	remaining: 8.43s
711:	learn: 1666.7247152	total: 20.8s	remaining: 8.4s
712:	learn: 1666.5627260	total: 20.8s	remaining: 8.37s
713:	learn: 1666.4303443	total: 20.8s	remaining: 8.34s
714:	learn: 1666.3328721	total: 20.9s	remaining: 8.31s
715:	learn: 1666.2185652	total: 20.9s	remaining: 8.29s
716:	learn: 1666.0997685	total: 20.9s	remaining: 8.26s
717:	learn: 1665.9657274	total: 21s	remaining: 8.23s
718:	learn: 1665.7747743	total: 21s	remaining: 8.21s
719:	learn: 1665.7235641	total: 21s	remaining: 8.18s
720:	learn: 1665.6208865	total: 21.1s	remaining: 8.15s
721:	learn: 1665.5081453	total: 21.1s	remaining: 8.13s
722:	learn: 1665.3684899	total: 21.1s	remaining: 8.1s
723:	learn: 1665.1256088	total: 21.2s	remaining: 8.07s
724:	learn: 1664.9677016	total: 21.2s	remaining: 8.04s
725:	learn: 1664.8031268	total: 21.2s	remaining: 8.02s
726:	learn: 1664.6477978	total: 21.3s	remaining: 7.99s
727:	learn: 1664.5823660	total: 21.3s	remaining: 7.96s
728:	learn: 1664.4576320	total: 21.3s	remaining: 7.93s
729:	learn: 1664.3583129	total: 21.4s	remaining: 7.9s
730:	learn: 1664.2256381	total: 21.4s	remaining: 7.87s
731:	learn: 1664.0564342	total: 21.4s	remaining: 7.84s
732:	learn: 1663.7250182	total: 21.4s	remaining: 7.81s
733:	learn: 1663.5032602	total: 21.5s	remaining: 7.78s
734:	learn: 1663.2711849	total: 21.5s	remaining: 7.75s
735:	learn: 1663.1798552	total: 21.5s	remaining: 7.72s
736:	learn: 1663.0318694	total: 21.6s	remaining: 7.69s
737:	learn: 1662.8710314	total: 21.6s	remaining: 7.66s
738:	learn: 1662.7321918	total: 21.6s	remaining: 7.63s
739:	learn: 1662.5439969	total: 21.6s	remaining: 7.6s
740:	learn: 1662.4146058	total: 21.7s	remaining: 7.57s
741:	learn: 1662.2304933	total: 21.7s	remaining: 7.55s
742:	learn: 1662.0594733	total: 21.7s	remaining: 7.52s
743:	learn: 1661.9820192	total: 21.8s	remaining: 7.49s
744:	learn: 1661.6712399	total: 21.8s	remaining: 7.46s
745:	learn: 1661.5747247	total: 21.8s	remaining: 7.43s
746:	learn: 1661.4391614	total: 21.9s	remaining: 7.41s
747:	learn: 1661.3439686	total: 21.9s	remaining: 7.38s
748:	learn: 1661.1167348	total: 21.9s	remaining: 7.35s
749:	learn: 1660.9913182	total: 22s	remaining: 7.32s
750:	learn: 1660.9063178	total: 22s	remaining: 7.29s
751:	learn: 1660.8078721	total: 22s	remaining: 7.26s
752:	learn: 1660.6799985	total: 22s	remaining: 7.23s
753:	learn: 1660.5347506	total: 22.1s	remaining: 7.2s
754:	learn: 1660.4444240	total: 22.1s	remaining: 7.17s
755:	learn: 1660.3308743	total: 22.1s	remaining: 7.14s
756:	learn: 1660.2126637	total: 22.1s	remaining: 7.11s
757:	learn: 1660.0225519	total: 22.2s	remaining: 7.08s
758:	learn: 1659.9472686	total: 22.2s	remaining: 7.05s
759:	learn: 1659.8688535	total: 22.2s	remaining: 7.02s
760:	learn: 1659.6882989	total: 22.3s	remaining: 6.99s
761:	learn: 1659.5745620	total: 22.3s	remaining: 6.96s
762:	learn: 1659.5008574	total: 22.3s	remaining: 6.93s
763:	learn: 1659.3635984	total: 22.3s	remaining: 6.9s
764:	learn: 1659.2931793	total: 22.4s	remaining: 6.87s
765:	learn: 1659.1475174	total: 22.4s	remaining: 6.84s
766:	learn: 1659.0621497	total: 22.4s	remaining: 6.81s
767:	learn: 1658.8783379	total: 22.4s	remaining: 6.78s
768:	learn: 1658.7724839	total: 22.5s	remaining: 6.75s
769:	learn: 1658.5997745	total: 22.5s	remaining: 6.72s
770:	learn: 1658.4820268	total: 22.5s	remaining: 6.69s
771:	learn: 1658.3480213	total: 22.6s	remaining: 6.66s
772:	learn: 1658.2997785	total: 22.6s	remaining: 6.63s
773:	learn: 1658.2114792	total: 22.6s	remaining: 6.6s
774:	learn: 1658.1681374	total: 22.6s	remaining: 6.57s
775:	learn: 1657.9863993	total: 22.7s	remaining: 6.54s
776:	learn: 1657.9337327	total: 22.7s	remaining: 6.51s
777:	learn: 1657.8050027	total: 22.7s	remaining: 6.48s
778:	learn: 1657.7075561	total: 22.7s	remaining: 6.45s
779:	learn: 1657.4572815	total: 22.8s	remaining: 6.42s
780:	learn: 1657.3249606	total: 22.8s	remaining: 6.39s
781:	learn: 1657.2633278	total: 22.8s	remaining: 6.36s
782:	learn: 1657.1924793	total: 22.8s	remaining: 6.33s
783:	learn: 1657.1308049	total: 22.9s	remaining: 6.3s
784:	learn: 1656.9358269	total: 22.9s	remaining: 6.27s
785:	learn: 1656.8516296	total: 22.9s	remaining: 6.24s
786:	learn: 1656.6714112	total: 23s	remaining: 6.21s
787:	learn: 1656.6200426	total: 23s	remaining: 6.18s
788:	learn: 1656.3786527	total: 23s	remaining: 6.15s
789:	learn: 1656.2791209	total: 23s	remaining: 6.12s
790:	learn: 1656.1555381	total: 23.1s	remaining: 6.09s
791:	learn: 1656.0892004	total: 23.1s	remaining: 6.06s
792:	learn: 1655.9833136	total: 23.1s	remaining: 6.03s
793:	learn: 1655.8426979	total: 23.1s	remaining: 6s
794:	learn: 1655.7432422	total: 23.2s	remaining: 5.97s
795:	learn: 1655.6538100	total: 23.2s	remaining: 5.94s
796:	learn: 1655.4919839	total: 23.2s	remaining: 5.91s
797:	learn: 1655.4004694	total: 23.2s	remaining: 5.88s
798:	learn: 1655.2784826	total: 23.3s	remaining: 5.86s
799:	learn: 1655.1799682	total: 23.3s	remaining: 5.83s
800:	learn: 1654.9879784	total: 23.3s	remaining: 5.8s
801:	learn: 1654.8891580	total: 23.4s	remaining: 5.77s
802:	learn: 1654.7905689	total: 23.4s	remaining: 5.74s
803:	learn: 1654.5988556	total: 23.4s	remaining: 5.71s
804:	learn: 1654.4969478	total: 23.4s	remaining: 5.68s
805:	learn: 1654.3879817	total: 23.5s	remaining: 5.65s
806:	learn: 1654.3250362	total: 23.5s	remaining: 5.62s
807:	learn: 1654.1048255	total: 23.5s	remaining: 5.59s
808:	learn: 1653.9563398	total: 23.6s	remaining: 5.56s
809:	learn: 1653.7711298	total: 23.6s	remaining: 5.53s
810:	learn: 1653.5724000	total: 23.6s	remaining: 5.5s
811:	learn: 1653.4297717	total: 23.7s	remaining: 5.48s
812:	learn: 1653.3444509	total: 23.7s	remaining: 5.45s
813:	learn: 1653.2051971	total: 23.7s	remaining: 5.42s
814:	learn: 1653.1067730	total: 23.7s	remaining: 5.39s
815:	learn: 1653.0380138	total: 23.8s	remaining: 5.36s
816:	learn: 1652.9718116	total: 23.8s	remaining: 5.33s
817:	learn: 1652.8025921	total: 23.8s	remaining: 5.3s
818:	learn: 1652.7017675	total: 23.9s	remaining: 5.27s
819:	learn: 1652.6480054	total: 23.9s	remaining: 5.24s
820:	learn: 1652.5767603	total: 23.9s	remaining: 5.21s
821:	learn: 1652.3757188	total: 23.9s	remaining: 5.18s
822:	learn: 1652.2587807	total: 24s	remaining: 5.16s
823:	learn: 1652.0784629	total: 24s	remaining: 5.13s
824:	learn: 1651.9417322	total: 24s	remaining: 5.1s
825:	learn: 1651.7996393	total: 24.1s	remaining: 5.07s
826:	learn: 1651.5518396	total: 24.1s	remaining: 5.04s
827:	learn: 1651.3293451	total: 24.1s	remaining: 5.01s
828:	learn: 1651.1406178	total: 24.1s	remaining: 4.98s
829:	learn: 1651.0886518	total: 24.2s	remaining: 4.95s
830:	learn: 1650.9693678	total: 24.2s	remaining: 4.92s
831:	learn: 1650.7083634	total: 24.2s	remaining: 4.89s
832:	learn: 1650.4689711	total: 24.3s	remaining: 4.86s
833:	learn: 1650.2898676	total: 24.3s	remaining: 4.83s
834:	learn: 1650.1744473	total: 24.3s	remaining: 4.8s
835:	learn: 1650.0397776	total: 24.3s	remaining: 4.78s
836:	learn: 1649.9225184	total: 24.4s	remaining: 4.75s
837:	learn: 1649.8513921	total: 24.4s	remaining: 4.72s
838:	learn: 1649.7754737	total: 24.4s	remaining: 4.69s
839:	learn: 1649.6310682	total: 24.5s	remaining: 4.66s
840:	learn: 1649.5006096	total: 24.5s	remaining: 4.63s
841:	learn: 1649.3546379	total: 24.5s	remaining: 4.6s
842:	learn: 1649.2489637	total: 24.6s	remaining: 4.57s
843:	learn: 1649.1743167	total: 24.6s	remaining: 4.54s
844:	learn: 1649.0271489	total: 24.6s	remaining: 4.52s
845:	learn: 1648.9533715	total: 24.6s	remaining: 4.49s
846:	learn: 1648.8520581	total: 24.7s	remaining: 4.46s
847:	learn: 1648.7835959	total: 24.7s	remaining: 4.43s
848:	learn: 1648.6705578	total: 24.7s	remaining: 4.4s
849:	learn: 1648.5568898	total: 24.8s	remaining: 4.37s
850:	learn: 1648.4171182	total: 24.8s	remaining: 4.34s
851:	learn: 1648.2744400	total: 24.8s	remaining: 4.31s
852:	learn: 1648.1786282	total: 24.8s	remaining: 4.28s
853:	learn: 1648.0913977	total: 24.9s	remaining: 4.25s
854:	learn: 1647.8376577	total: 24.9s	remaining: 4.22s
855:	learn: 1647.8129033	total: 24.9s	remaining: 4.19s
856:	learn: 1647.7403333	total: 25s	remaining: 4.16s
857:	learn: 1647.6536967	total: 25s	remaining: 4.13s
858:	learn: 1647.4645691	total: 25s	remaining: 4.11s
859:	learn: 1647.1248062	total: 25s	remaining: 4.08s
860:	learn: 1646.9926983	total: 25.1s	remaining: 4.05s
861:	learn: 1646.8768315	total: 25.1s	remaining: 4.02s
862:	learn: 1646.7657501	total: 25.1s	remaining: 3.99s
863:	learn: 1646.6472210	total: 25.2s	remaining: 3.96s
864:	learn: 1646.5503609	total: 25.2s	remaining: 3.93s
865:	learn: 1646.4268556	total: 25.2s	remaining: 3.9s
866:	learn: 1646.3800948	total: 25.2s	remaining: 3.87s
867:	learn: 1646.2958666	total: 25.3s	remaining: 3.84s
868:	learn: 1646.1770951	total: 25.3s	remaining: 3.81s
869:	learn: 1646.0772712	total: 25.3s	remaining: 3.78s
870:	learn: 1645.9614394	total: 25.3s	remaining: 3.75s
871:	learn: 1645.9145574	total: 25.4s	remaining: 3.73s
872:	learn: 1645.7467503	total: 25.4s	remaining: 3.7s
873:	learn: 1645.7029527	total: 25.4s	remaining: 3.67s
874:	learn: 1645.6350654	total: 25.5s	remaining: 3.64s
875:	learn: 1645.5461468	total: 25.5s	remaining: 3.61s
876:	learn: 1645.4221690	total: 25.5s	remaining: 3.58s
877:	learn: 1645.3112056	total: 25.5s	remaining: 3.55s
878:	learn: 1645.1720099	total: 25.6s	remaining: 3.52s
879:	learn: 1644.9768722	total: 25.6s	remaining: 3.49s
880:	learn: 1644.9035241	total: 25.6s	remaining: 3.46s
881:	learn: 1644.7168954	total: 25.7s	remaining: 3.43s
882:	learn: 1644.6658919	total: 25.7s	remaining: 3.4s
883:	learn: 1644.5141809	total: 25.7s	remaining: 3.38s
884:	learn: 1644.4421866	total: 25.8s	remaining: 3.35s
885:	learn: 1644.3535951	total: 25.8s	remaining: 3.32s
886:	learn: 1644.1738932	total: 25.8s	remaining: 3.29s
887:	learn: 1644.0032082	total: 25.8s	remaining: 3.26s
888:	learn: 1643.9220343	total: 25.9s	remaining: 3.23s
889:	learn: 1643.7233862	total: 25.9s	remaining: 3.2s
890:	learn: 1643.5849140	total: 25.9s	remaining: 3.17s
891:	learn: 1643.4944688	total: 26s	remaining: 3.14s
892:	learn: 1643.4016479	total: 26s	remaining: 3.11s
893:	learn: 1643.3037820	total: 26s	remaining: 3.08s
894:	learn: 1643.1747685	total: 26s	remaining: 3.06s
895:	learn: 1643.0892863	total: 26.1s	remaining: 3.03s
896:	learn: 1642.8389551	total: 26.1s	remaining: 3s
897:	learn: 1642.6437440	total: 26.1s	remaining: 2.97s
898:	learn: 1642.5808519	total: 26.2s	remaining: 2.94s
899:	learn: 1642.4940310	total: 26.2s	remaining: 2.91s
900:	learn: 1642.3471813	total: 26.2s	remaining: 2.88s
901:	learn: 1642.2271629	total: 26.3s	remaining: 2.85s
902:	learn: 1642.1387080	total: 26.3s	remaining: 2.82s
903:	learn: 1642.0078092	total: 26.3s	remaining: 2.79s
904:	learn: 1641.9225001	total: 26.3s	remaining: 2.77s
905:	learn: 1641.8312013	total: 26.4s	remaining: 2.74s
906:	learn: 1641.7361952	total: 26.4s	remaining: 2.71s
907:	learn: 1641.6311369	total: 26.5s	remaining: 2.68s
908:	learn: 1641.5664414	total: 26.5s	remaining: 2.65s
909:	learn: 1641.5100752	total: 26.5s	remaining: 2.62s
910:	learn: 1641.3933162	total: 26.5s	remaining: 2.59s
911:	learn: 1641.2761493	total: 26.6s	remaining: 2.56s
912:	learn: 1641.1158090	total: 26.6s	remaining: 2.53s
913:	learn: 1641.0094889	total: 26.6s	remaining: 2.51s
914:	learn: 1640.9643126	total: 26.7s	remaining: 2.48s
915:	learn: 1640.7616467	total: 26.7s	remaining: 2.45s
916:	learn: 1640.6431269	total: 26.7s	remaining: 2.42s
917:	learn: 1640.4677812	total: 26.8s	remaining: 2.39s
918:	learn: 1640.3994504	total: 26.8s	remaining: 2.36s
919:	learn: 1640.3131512	total: 26.8s	remaining: 2.33s
920:	learn: 1640.1382643	total: 26.8s	remaining: 2.3s
921:	learn: 1640.0254308	total: 26.9s	remaining: 2.27s
922:	learn: 1639.9339304	total: 26.9s	remaining: 2.24s
923:	learn: 1639.8590696	total: 26.9s	remaining: 2.21s
924:	learn: 1639.7921716	total: 27s	remaining: 2.19s
925:	learn: 1639.6856556	total: 27s	remaining: 2.16s
926:	learn: 1639.5801026	total: 27s	remaining: 2.13s
927:	learn: 1639.4979233	total: 27s	remaining: 2.1s
928:	learn: 1639.2564722	total: 27.1s	remaining: 2.07s
929:	learn: 1639.1958209	total: 27.1s	remaining: 2.04s
930:	learn: 1639.0882047	total: 27.1s	remaining: 2.01s
931:	learn: 1638.9891140	total: 27.2s	remaining: 1.98s
932:	learn: 1638.8945513	total: 27.2s	remaining: 1.95s
933:	learn: 1638.8346825	total: 27.2s	remaining: 1.92s
934:	learn: 1638.6715397	total: 27.2s	remaining: 1.89s
935:	learn: 1638.5973436	total: 27.3s	remaining: 1.86s
936:	learn: 1638.5182031	total: 27.3s	remaining: 1.83s
937:	learn: 1638.4167795	total: 27.3s	remaining: 1.81s
938:	learn: 1638.2726922	total: 27.4s	remaining: 1.78s
939:	learn: 1638.2082184	total: 27.4s	remaining: 1.75s
940:	learn: 1638.1190096	total: 27.4s	remaining: 1.72s
941:	learn: 1637.9815290	total: 27.4s	remaining: 1.69s
942:	learn: 1637.7874971	total: 27.5s	remaining: 1.66s
943:	learn: 1637.6728511	total: 27.5s	remaining: 1.63s
944:	learn: 1637.5900000	total: 27.5s	remaining: 1.6s
945:	learn: 1637.3968202	total: 27.6s	remaining: 1.57s
946:	learn: 1637.3309657	total: 27.6s	remaining: 1.54s
947:	learn: 1637.1470105	total: 27.6s	remaining: 1.51s
948:	learn: 1636.9755155	total: 27.6s	remaining: 1.49s
949:	learn: 1636.8674865	total: 27.7s	remaining: 1.46s
950:	learn: 1636.8043718	total: 27.7s	remaining: 1.43s
951:	learn: 1636.7069882	total: 27.7s	remaining: 1.4s
952:	learn: 1636.6110330	total: 27.8s	remaining: 1.37s
953:	learn: 1636.4771642	total: 27.8s	remaining: 1.34s
954:	learn: 1636.4101498	total: 27.8s	remaining: 1.31s
955:	learn: 1636.2242997	total: 27.8s	remaining: 1.28s
956:	learn: 1636.0657794	total: 27.9s	remaining: 1.25s
957:	learn: 1635.9543358	total: 27.9s	remaining: 1.22s
958:	learn: 1635.8534374	total: 27.9s	remaining: 1.19s
959:	learn: 1635.7959567	total: 28s	remaining: 1.17s
960:	learn: 1635.6424488	total: 28s	remaining: 1.14s
961:	learn: 1635.5339222	total: 28s	remaining: 1.11s
962:	learn: 1635.4003301	total: 28.1s	remaining: 1.08s
963:	learn: 1635.3609981	total: 28.1s	remaining: 1.05s
964:	learn: 1635.2558747	total: 28.1s	remaining: 1.02s
965:	learn: 1635.1166317	total: 28.1s	remaining: 991ms
966:	learn: 1635.0391128	total: 28.2s	remaining: 962ms
967:	learn: 1634.8796060	total: 28.2s	remaining: 932ms
968:	learn: 1634.7164308	total: 28.2s	remaining: 903ms
969:	learn: 1634.6420026	total: 28.3s	remaining: 874ms
970:	learn: 1634.5466780	total: 28.3s	remaining: 845ms
971:	learn: 1634.4351428	total: 28.3s	remaining: 816ms
972:	learn: 1634.3092525	total: 28.4s	remaining: 787ms
973:	learn: 1634.1755026	total: 28.4s	remaining: 758ms
974:	learn: 1634.0352110	total: 28.4s	remaining: 729ms
975:	learn: 1633.9665497	total: 28.4s	remaining: 700ms
976:	learn: 1633.8193831	total: 28.5s	remaining: 670ms
977:	learn: 1633.7125721	total: 28.5s	remaining: 641ms
978:	learn: 1633.6248569	total: 28.5s	remaining: 612ms
979:	learn: 1633.5991355	total: 28.6s	remaining: 583ms
980:	learn: 1633.5084158	total: 28.6s	remaining: 554ms
981:	learn: 1633.4127890	total: 28.6s	remaining: 525ms
982:	learn: 1633.2675143	total: 28.7s	remaining: 496ms
983:	learn: 1633.1908785	total: 28.7s	remaining: 466ms
984:	learn: 1633.0911046	total: 28.7s	remaining: 437ms
985:	learn: 1632.9943457	total: 28.7s	remaining: 408ms
986:	learn: 1632.8874552	total: 28.8s	remaining: 379ms
987:	learn: 1632.7933168	total: 28.8s	remaining: 350ms
988:	learn: 1632.7031159	total: 28.9s	remaining: 321ms
989:	learn: 1632.6797276	total: 28.9s	remaining: 292ms
990:	learn: 1632.6054841	total: 28.9s	remaining: 263ms
991:	learn: 1632.4339542	total: 28.9s	remaining: 233ms
992:	learn: 1632.2224587	total: 29s	remaining: 204ms
993:	learn: 1632.1985260	total: 29s	remaining: 175ms
994:	learn: 1632.0828759	total: 29s	remaining: 146ms
995:	learn: 1631.9189624	total: 29.1s	remaining: 117ms
996:	learn: 1631.8043582	total: 29.1s	remaining: 87.5ms
997:	learn: 1631.6735051	total: 29.1s	remaining: 58.4ms
998:	learn: 1631.5437615	total: 29.2s	remaining: 29.2ms
999:	learn: 1631.4348315	total: 29.2s	remaining: 0us
Learning rate set to 0.093467
0:	learn: 4275.2355469	total: 28.6ms	remaining: 28.6s
1:	learn: 4025.9042026	total: 67.4ms	remaining: 33.6s
2:	learn: 3803.8634566	total: 97.3ms	remaining: 32.3s
3:	learn: 3610.7820397	total: 133ms	remaining: 33.2s
4:	learn: 3435.3061241	total: 176ms	remaining: 35s
5:	learn: 3285.8768362	total: 209ms	remaining: 34.6s
6:	learn: 3150.0529085	total: 247ms	remaining: 35s
7:	learn: 3031.0023248	total: 285ms	remaining: 35.4s
8:	learn: 2918.0691381	total: 322ms	remaining: 35.5s
9:	learn: 2819.2128047	total: 361ms	remaining: 35.7s
10:	learn: 2729.1363750	total: 402ms	remaining: 36.1s
11:	learn: 2653.4402673	total: 438ms	remaining: 36s
12:	learn: 2584.1778258	total: 467ms	remaining: 35.4s
13:	learn: 2524.5988310	total: 493ms	remaining: 34.8s
14:	learn: 2471.4374967	total: 524ms	remaining: 34.4s
15:	learn: 2423.3158891	total: 554ms	remaining: 34.1s
16:	learn: 2381.1458552	total: 580ms	remaining: 33.6s
17:	learn: 2344.8481438	total: 610ms	remaining: 33.3s
18:	learn: 2308.7842239	total: 647ms	remaining: 33.4s
19:	learn: 2277.3891642	total: 674ms	remaining: 33s
20:	learn: 2248.9745875	total: 701ms	remaining: 32.7s
21:	learn: 2225.5762773	total: 740ms	remaining: 32.9s
22:	learn: 2203.6921921	total: 768ms	remaining: 32.6s
23:	learn: 2183.9628465	total: 797ms	remaining: 32.4s
24:	learn: 2164.1732850	total: 832ms	remaining: 32.4s
25:	learn: 2147.5349410	total: 860ms	remaining: 32.2s
26:	learn: 2133.0298756	total: 889ms	remaining: 32s
27:	learn: 2119.7841882	total: 929ms	remaining: 32.2s
28:	learn: 2108.6253278	total: 959ms	remaining: 32.1s
29:	learn: 2097.9497470	total: 996ms	remaining: 32.2s
30:	learn: 2086.7779347	total: 1.03s	remaining: 32.2s
31:	learn: 2076.7509079	total: 1.06s	remaining: 32s
32:	learn: 2069.1232144	total: 1.08s	remaining: 31.8s
33:	learn: 2061.2506227	total: 1.12s	remaining: 31.8s
34:	learn: 2054.2583917	total: 1.14s	remaining: 31.5s
35:	learn: 2046.7331159	total: 1.17s	remaining: 31.3s
36:	learn: 2040.6416588	total: 1.2s	remaining: 31.3s
37:	learn: 2033.0436625	total: 1.24s	remaining: 31.5s
38:	learn: 2026.5709368	total: 1.27s	remaining: 31.3s
39:	learn: 2021.5127981	total: 1.31s	remaining: 31.3s
40:	learn: 2016.0610174	total: 1.33s	remaining: 31.2s
41:	learn: 2011.2965279	total: 1.36s	remaining: 30.9s
42:	learn: 2006.7963773	total: 1.39s	remaining: 30.9s
43:	learn: 2002.5893536	total: 1.42s	remaining: 30.8s
44:	learn: 1996.7236360	total: 1.44s	remaining: 30.6s
45:	learn: 1991.4611801	total: 1.47s	remaining: 30.4s
46:	learn: 1986.9498050	total: 1.5s	remaining: 30.4s
47:	learn: 1983.4790205	total: 1.52s	remaining: 30.2s
48:	learn: 1978.7046377	total: 1.55s	remaining: 30.1s
49:	learn: 1975.9126878	total: 1.58s	remaining: 30.1s
50:	learn: 1972.9554326	total: 1.61s	remaining: 30s
51:	learn: 1969.9208506	total: 1.64s	remaining: 29.9s
52:	learn: 1966.9283324	total: 1.66s	remaining: 29.7s
53:	learn: 1964.6916717	total: 1.69s	remaining: 29.7s
54:	learn: 1962.4120299	total: 1.72s	remaining: 29.5s
55:	learn: 1958.7038595	total: 1.74s	remaining: 29.4s
56:	learn: 1956.6624735	total: 1.77s	remaining: 29.3s
57:	learn: 1954.4425003	total: 1.8s	remaining: 29.3s
58:	learn: 1952.0327671	total: 1.83s	remaining: 29.1s
59:	learn: 1948.1225772	total: 1.85s	remaining: 29s
60:	learn: 1944.5625270	total: 1.89s	remaining: 29.1s
61:	learn: 1942.7260018	total: 1.91s	remaining: 29s
62:	learn: 1940.2090193	total: 1.94s	remaining: 28.8s
63:	learn: 1938.5597173	total: 1.97s	remaining: 28.8s
64:	learn: 1936.1187662	total: 1.99s	remaining: 28.7s
65:	learn: 1933.6530626	total: 2.02s	remaining: 28.6s
66:	learn: 1930.1922692	total: 2.05s	remaining: 28.5s
67:	learn: 1927.4618600	total: 2.08s	remaining: 28.5s
68:	learn: 1925.8785241	total: 2.1s	remaining: 28.4s
69:	learn: 1924.3308700	total: 2.13s	remaining: 28.3s
70:	learn: 1922.9165511	total: 2.16s	remaining: 28.2s
71:	learn: 1920.0692379	total: 2.19s	remaining: 28.2s
72:	learn: 1918.4006003	total: 2.21s	remaining: 28.1s
73:	learn: 1917.2542984	total: 2.23s	remaining: 28s
74:	learn: 1914.9609135	total: 2.27s	remaining: 28s
75:	learn: 1913.3665381	total: 2.29s	remaining: 27.9s
76:	learn: 1910.4657974	total: 2.32s	remaining: 27.8s
77:	learn: 1908.3855877	total: 2.35s	remaining: 27.8s
78:	learn: 1906.9132255	total: 2.38s	remaining: 27.8s
79:	learn: 1906.0333023	total: 2.4s	remaining: 27.7s
80:	learn: 1903.7745586	total: 2.43s	remaining: 27.6s
81:	learn: 1901.5524580	total: 2.47s	remaining: 27.6s
82:	learn: 1899.7267866	total: 2.49s	remaining: 27.5s
83:	learn: 1897.7407369	total: 2.52s	remaining: 27.5s
84:	learn: 1896.6584460	total: 2.55s	remaining: 27.5s
85:	learn: 1895.8617452	total: 2.58s	remaining: 27.4s
86:	learn: 1893.9479856	total: 2.6s	remaining: 27.3s
87:	learn: 1892.0844051	total: 2.63s	remaining: 27.2s
88:	learn: 1890.4218763	total: 2.66s	remaining: 27.3s
89:	learn: 1889.0388874	total: 2.69s	remaining: 27.2s
90:	learn: 1888.1222622	total: 2.71s	remaining: 27.1s
91:	learn: 1886.4377236	total: 2.75s	remaining: 27.1s
92:	learn: 1885.0900132	total: 2.78s	remaining: 27.1s
93:	learn: 1883.8490530	total: 2.81s	remaining: 27.1s
94:	learn: 1882.4755718	total: 2.84s	remaining: 27s
95:	learn: 1880.3721267	total: 2.87s	remaining: 27s
96:	learn: 1879.0885065	total: 2.89s	remaining: 26.9s
97:	learn: 1878.3138725	total: 2.92s	remaining: 26.8s
98:	learn: 1876.7799522	total: 2.95s	remaining: 26.9s
99:	learn: 1875.9146245	total: 2.98s	remaining: 26.8s
100:	learn: 1874.6507743	total: 3s	remaining: 26.7s
101:	learn: 1873.4305932	total: 3.03s	remaining: 26.7s
102:	learn: 1872.7122821	total: 3.06s	remaining: 26.6s
103:	learn: 1870.9863111	total: 3.08s	remaining: 26.6s
104:	learn: 1869.9058915	total: 3.11s	remaining: 26.5s
105:	learn: 1868.5167354	total: 3.15s	remaining: 26.5s
106:	learn: 1867.8366988	total: 3.17s	remaining: 26.5s
107:	learn: 1866.2896978	total: 3.2s	remaining: 26.4s
108:	learn: 1865.1700683	total: 3.23s	remaining: 26.4s
109:	learn: 1863.4106343	total: 3.25s	remaining: 26.3s
110:	learn: 1862.2078847	total: 3.28s	remaining: 26.3s
111:	learn: 1860.9965168	total: 3.3s	remaining: 26.2s
112:	learn: 1859.9207966	total: 3.34s	remaining: 26.2s
113:	learn: 1859.2084101	total: 3.36s	remaining: 26.1s
114:	learn: 1858.6863591	total: 3.38s	remaining: 26.1s
115:	learn: 1857.8922929	total: 3.41s	remaining: 26s
116:	learn: 1856.6329072	total: 3.44s	remaining: 26s
117:	learn: 1855.4936424	total: 3.48s	remaining: 26s
118:	learn: 1854.2760233	total: 3.5s	remaining: 26s
119:	learn: 1853.7396066	total: 3.53s	remaining: 25.9s
120:	learn: 1852.2226495	total: 3.56s	remaining: 25.9s
121:	learn: 1851.2346821	total: 3.58s	remaining: 25.8s
122:	learn: 1850.3090659	total: 3.62s	remaining: 25.8s
123:	learn: 1849.4995269	total: 3.64s	remaining: 25.7s
124:	learn: 1848.3766238	total: 3.67s	remaining: 25.7s
125:	learn: 1847.3531351	total: 3.69s	remaining: 25.6s
126:	learn: 1846.5934585	total: 3.73s	remaining: 25.6s
127:	learn: 1845.4459636	total: 3.76s	remaining: 25.6s
128:	learn: 1844.8998429	total: 3.78s	remaining: 25.5s
129:	learn: 1844.3610185	total: 3.81s	remaining: 25.5s
130:	learn: 1843.6393204	total: 3.84s	remaining: 25.5s
131:	learn: 1842.6493689	total: 3.86s	remaining: 25.4s
132:	learn: 1842.0389953	total: 3.89s	remaining: 25.4s
133:	learn: 1841.3964055	total: 3.92s	remaining: 25.4s
134:	learn: 1840.3015273	total: 3.95s	remaining: 25.3s
135:	learn: 1839.8102330	total: 3.97s	remaining: 25.2s
136:	learn: 1838.9469464	total: 4.01s	remaining: 25.2s
137:	learn: 1838.3926390	total: 4.03s	remaining: 25.2s
138:	learn: 1837.2966070	total: 4.06s	remaining: 25.1s
139:	learn: 1836.1794755	total: 4.09s	remaining: 25.1s
140:	learn: 1835.2427953	total: 4.12s	remaining: 25.1s
141:	learn: 1834.2330111	total: 4.14s	remaining: 25s
142:	learn: 1833.3917824	total: 4.17s	remaining: 25s
143:	learn: 1832.6245671	total: 4.21s	remaining: 25s
144:	learn: 1832.0635490	total: 4.23s	remaining: 25s
145:	learn: 1831.1364408	total: 4.26s	remaining: 24.9s
146:	learn: 1830.4837790	total: 4.29s	remaining: 24.9s
147:	learn: 1829.5169175	total: 4.32s	remaining: 24.9s
148:	learn: 1828.7698322	total: 4.34s	remaining: 24.8s
149:	learn: 1828.2531950	total: 4.37s	remaining: 24.7s
150:	learn: 1827.2319691	total: 4.4s	remaining: 24.7s
151:	learn: 1826.6235117	total: 4.42s	remaining: 24.7s
152:	learn: 1825.7540078	total: 4.45s	remaining: 24.6s
153:	learn: 1825.1724332	total: 4.48s	remaining: 24.6s
154:	learn: 1824.7042380	total: 4.51s	remaining: 24.6s
155:	learn: 1824.1956086	total: 4.53s	remaining: 24.5s
156:	learn: 1823.6827111	total: 4.55s	remaining: 24.5s
157:	learn: 1822.9004302	total: 4.59s	remaining: 24.5s
158:	learn: 1822.4200255	total: 4.62s	remaining: 24.4s
159:	learn: 1821.5698627	total: 4.64s	remaining: 24.4s
160:	learn: 1821.1689057	total: 4.67s	remaining: 24.3s
161:	learn: 1820.1894348	total: 4.7s	remaining: 24.3s
162:	learn: 1819.3021322	total: 4.72s	remaining: 24.3s
163:	learn: 1818.3812561	total: 4.75s	remaining: 24.2s
164:	learn: 1817.6083088	total: 4.79s	remaining: 24.2s
165:	learn: 1816.8873970	total: 4.82s	remaining: 24.2s
166:	learn: 1816.4461884	total: 4.84s	remaining: 24.2s
167:	learn: 1815.8642491	total: 4.88s	remaining: 24.1s
168:	learn: 1815.3104113	total: 4.91s	remaining: 24.1s
169:	learn: 1814.4733764	total: 4.93s	remaining: 24.1s
170:	learn: 1814.0508715	total: 4.95s	remaining: 24s
171:	learn: 1813.6177374	total: 4.99s	remaining: 24s
172:	learn: 1813.2137011	total: 5.01s	remaining: 24s
173:	learn: 1812.4944938	total: 5.04s	remaining: 23.9s
174:	learn: 1811.7996304	total: 5.07s	remaining: 23.9s
175:	learn: 1811.0102690	total: 5.1s	remaining: 23.9s
176:	learn: 1810.4881810	total: 5.12s	remaining: 23.8s
177:	learn: 1810.0756338	total: 5.15s	remaining: 23.8s
178:	learn: 1809.5094234	total: 5.18s	remaining: 23.8s
179:	learn: 1809.0209057	total: 5.21s	remaining: 23.7s
180:	learn: 1808.3244739	total: 5.24s	remaining: 23.7s
181:	learn: 1807.7461368	total: 5.27s	remaining: 23.7s
182:	learn: 1806.9991916	total: 5.3s	remaining: 23.6s
183:	learn: 1806.3294584	total: 5.32s	remaining: 23.6s
184:	learn: 1805.7780162	total: 5.35s	remaining: 23.6s
185:	learn: 1805.2922564	total: 5.38s	remaining: 23.6s
186:	learn: 1805.0092380	total: 5.41s	remaining: 23.5s
187:	learn: 1804.3564023	total: 5.43s	remaining: 23.5s
188:	learn: 1803.8581654	total: 5.46s	remaining: 23.5s
189:	learn: 1803.5123565	total: 5.49s	remaining: 23.4s
190:	learn: 1802.8130040	total: 5.52s	remaining: 23.4s
191:	learn: 1802.2732641	total: 5.55s	remaining: 23.4s
192:	learn: 1801.5374844	total: 5.58s	remaining: 23.3s
193:	learn: 1800.9621504	total: 5.61s	remaining: 23.3s
194:	learn: 1800.5380463	total: 5.63s	remaining: 23.2s
195:	learn: 1800.1985516	total: 5.67s	remaining: 23.2s
196:	learn: 1799.4326533	total: 5.69s	remaining: 23.2s
197:	learn: 1798.8454802	total: 5.72s	remaining: 23.2s
198:	learn: 1798.3609811	total: 5.75s	remaining: 23.1s
199:	learn: 1797.6274230	total: 5.78s	remaining: 23.1s
200:	learn: 1797.3824334	total: 5.8s	remaining: 23.1s
201:	learn: 1796.9489100	total: 5.83s	remaining: 23s
202:	learn: 1796.3121909	total: 5.86s	remaining: 23s
203:	learn: 1795.7837712	total: 5.89s	remaining: 23s
204:	learn: 1795.3339366	total: 5.92s	remaining: 22.9s
205:	learn: 1794.7342778	total: 5.95s	remaining: 22.9s
206:	learn: 1794.3554465	total: 5.98s	remaining: 22.9s
207:	learn: 1793.7469293	total: 6.01s	remaining: 22.9s
208:	learn: 1792.9032786	total: 6.04s	remaining: 22.9s
209:	learn: 1792.5501973	total: 6.07s	remaining: 22.8s
210:	learn: 1791.8988305	total: 6.09s	remaining: 22.8s
211:	learn: 1791.3431591	total: 6.13s	remaining: 22.8s
212:	learn: 1790.7115494	total: 6.16s	remaining: 22.8s
213:	learn: 1790.3550288	total: 6.18s	remaining: 22.7s
214:	learn: 1789.7201395	total: 6.21s	remaining: 22.7s
215:	learn: 1789.1616176	total: 6.25s	remaining: 22.7s
216:	learn: 1788.6257476	total: 6.27s	remaining: 22.6s
217:	learn: 1787.9189065	total: 6.3s	remaining: 22.6s
218:	learn: 1787.4181256	total: 6.33s	remaining: 22.6s
219:	learn: 1786.8979180	total: 6.36s	remaining: 22.5s
220:	learn: 1786.4763998	total: 6.38s	remaining: 22.5s
221:	learn: 1786.0667049	total: 6.42s	remaining: 22.5s
222:	learn: 1785.6237481	total: 6.45s	remaining: 22.5s
223:	learn: 1785.2257705	total: 6.47s	remaining: 22.4s
224:	learn: 1784.7061464	total: 6.5s	remaining: 22.4s
225:	learn: 1784.5005711	total: 6.53s	remaining: 22.4s
226:	learn: 1783.8337504	total: 6.56s	remaining: 22.3s
227:	learn: 1783.3570839	total: 6.58s	remaining: 22.3s
228:	learn: 1783.0367646	total: 6.61s	remaining: 22.3s
229:	learn: 1782.7279734	total: 6.64s	remaining: 22.2s
230:	learn: 1782.4528362	total: 6.66s	remaining: 22.2s
231:	learn: 1782.0622218	total: 6.69s	remaining: 22.2s
232:	learn: 1781.8016494	total: 6.73s	remaining: 22.2s
233:	learn: 1781.2871844	total: 6.76s	remaining: 22.1s
234:	learn: 1780.9025503	total: 6.78s	remaining: 22.1s
235:	learn: 1780.2730839	total: 6.82s	remaining: 22.1s
236:	learn: 1779.8837391	total: 6.85s	remaining: 22s
237:	learn: 1779.5320131	total: 6.87s	remaining: 22s
238:	learn: 1778.7360598	total: 6.91s	remaining: 22s
239:	learn: 1778.3107353	total: 6.93s	remaining: 22s
240:	learn: 1777.8407073	total: 6.96s	remaining: 21.9s
241:	learn: 1777.4187280	total: 6.99s	remaining: 21.9s
242:	learn: 1777.2415233	total: 7.01s	remaining: 21.9s
243:	learn: 1776.7103374	total: 7.04s	remaining: 21.8s
244:	learn: 1776.3044193	total: 7.07s	remaining: 21.8s
245:	learn: 1776.0646215	total: 7.1s	remaining: 21.8s
246:	learn: 1775.7753970	total: 7.13s	remaining: 21.7s
247:	learn: 1775.3383548	total: 7.15s	remaining: 21.7s
248:	learn: 1775.0672711	total: 7.18s	remaining: 21.7s
249:	learn: 1774.6143437	total: 7.21s	remaining: 21.6s
250:	learn: 1774.1800994	total: 7.24s	remaining: 21.6s
251:	learn: 1773.9528720	total: 7.26s	remaining: 21.6s
252:	learn: 1773.5084504	total: 7.3s	remaining: 21.5s
253:	learn: 1773.3388978	total: 7.32s	remaining: 21.5s
254:	learn: 1772.7296413	total: 7.35s	remaining: 21.5s
255:	learn: 1772.3648519	total: 7.38s	remaining: 21.5s
256:	learn: 1771.9886721	total: 7.41s	remaining: 21.4s
257:	learn: 1771.7097131	total: 7.44s	remaining: 21.4s
258:	learn: 1771.4977019	total: 7.46s	remaining: 21.3s
259:	learn: 1771.0345109	total: 7.49s	remaining: 21.3s
260:	learn: 1770.6753839	total: 7.52s	remaining: 21.3s
261:	learn: 1770.3047685	total: 7.55s	remaining: 21.3s
262:	learn: 1769.9369181	total: 7.58s	remaining: 21.3s
263:	learn: 1769.6042869	total: 7.61s	remaining: 21.2s
264:	learn: 1769.3256619	total: 7.63s	remaining: 21.2s
265:	learn: 1768.9840020	total: 7.66s	remaining: 21.1s
266:	learn: 1768.3395188	total: 7.69s	remaining: 21.1s
267:	learn: 1768.1601042	total: 7.72s	remaining: 21.1s
268:	learn: 1767.9112095	total: 7.75s	remaining: 21s
269:	learn: 1767.7154445	total: 7.78s	remaining: 21s
270:	learn: 1767.4481872	total: 7.8s	remaining: 21s
271:	learn: 1767.3098904	total: 7.83s	remaining: 21s
272:	learn: 1766.8607288	total: 7.86s	remaining: 20.9s
273:	learn: 1766.0882817	total: 7.89s	remaining: 20.9s
274:	learn: 1765.8752130	total: 7.92s	remaining: 20.9s
275:	learn: 1765.6948227	total: 7.94s	remaining: 20.8s
276:	learn: 1765.1899312	total: 7.97s	remaining: 20.8s
277:	learn: 1764.7933506	total: 8s	remaining: 20.8s
278:	learn: 1764.6314409	total: 8.02s	remaining: 20.7s
279:	learn: 1764.2633840	total: 8.06s	remaining: 20.7s
280:	learn: 1763.8723450	total: 8.09s	remaining: 20.7s
281:	learn: 1763.5559212	total: 8.11s	remaining: 20.7s
282:	learn: 1763.3899665	total: 8.14s	remaining: 20.6s
283:	learn: 1762.9338922	total: 8.17s	remaining: 20.6s
284:	learn: 1762.5043302	total: 8.2s	remaining: 20.6s
285:	learn: 1762.0620516	total: 8.22s	remaining: 20.5s
286:	learn: 1761.7059080	total: 8.26s	remaining: 20.5s
287:	learn: 1761.3185166	total: 8.29s	remaining: 20.5s
288:	learn: 1760.9604407	total: 8.32s	remaining: 20.5s
289:	learn: 1760.5499314	total: 8.35s	remaining: 20.4s
290:	learn: 1760.2409451	total: 8.38s	remaining: 20.4s
291:	learn: 1759.8866814	total: 8.4s	remaining: 20.4s
292:	learn: 1759.3913073	total: 8.43s	remaining: 20.4s
293:	learn: 1759.0275942	total: 8.46s	remaining: 20.3s
294:	learn: 1758.7501972	total: 8.49s	remaining: 20.3s
295:	learn: 1758.3798634	total: 8.52s	remaining: 20.3s
296:	learn: 1758.2065168	total: 8.55s	remaining: 20.2s
297:	learn: 1757.6037220	total: 8.59s	remaining: 20.2s
298:	learn: 1757.1806139	total: 8.63s	remaining: 20.2s
299:	learn: 1756.7947900	total: 8.67s	remaining: 20.2s
300:	learn: 1756.6147921	total: 8.7s	remaining: 20.2s
301:	learn: 1756.1302881	total: 8.73s	remaining: 20.2s
302:	learn: 1755.5556848	total: 8.76s	remaining: 20.2s
303:	learn: 1755.2214368	total: 8.79s	remaining: 20.1s
304:	learn: 1754.9375655	total: 8.82s	remaining: 20.1s
305:	learn: 1754.6108611	total: 8.85s	remaining: 20.1s
306:	learn: 1754.2558761	total: 8.88s	remaining: 20s
307:	learn: 1753.9377721	total: 8.9s	remaining: 20s
308:	learn: 1753.6246321	total: 8.94s	remaining: 20s
309:	learn: 1753.2224482	total: 8.97s	remaining: 20s
310:	learn: 1752.8451765	total: 8.99s	remaining: 19.9s
311:	learn: 1752.4246856	total: 9.02s	remaining: 19.9s
312:	learn: 1752.0314788	total: 9.05s	remaining: 19.9s
313:	learn: 1751.7570586	total: 9.08s	remaining: 19.8s
314:	learn: 1751.3993996	total: 9.11s	remaining: 19.8s
315:	learn: 1751.1362876	total: 9.15s	remaining: 19.8s
316:	learn: 1750.9595552	total: 9.17s	remaining: 19.8s
317:	learn: 1750.6982972	total: 9.2s	remaining: 19.7s
318:	learn: 1750.4583194	total: 9.23s	remaining: 19.7s
319:	learn: 1750.2995854	total: 9.25s	remaining: 19.7s
320:	learn: 1750.0346460	total: 9.28s	remaining: 19.6s
321:	learn: 1749.8055383	total: 9.31s	remaining: 19.6s
322:	learn: 1749.4187634	total: 9.34s	remaining: 19.6s
323:	learn: 1749.1792661	total: 9.36s	remaining: 19.5s
324:	learn: 1748.8049340	total: 9.39s	remaining: 19.5s
325:	learn: 1748.6236052	total: 9.42s	remaining: 19.5s
326:	learn: 1748.3749425	total: 9.45s	remaining: 19.4s
327:	learn: 1748.1794975	total: 9.47s	remaining: 19.4s
328:	learn: 1747.9360648	total: 9.5s	remaining: 19.4s
329:	learn: 1747.7196361	total: 9.53s	remaining: 19.4s
330:	learn: 1747.1739791	total: 9.56s	remaining: 19.3s
331:	learn: 1746.9583548	total: 9.58s	remaining: 19.3s
332:	learn: 1746.3234091	total: 9.62s	remaining: 19.3s
333:	learn: 1746.0112451	total: 9.64s	remaining: 19.2s
334:	learn: 1745.7621842	total: 9.67s	remaining: 19.2s
335:	learn: 1745.4706551	total: 9.71s	remaining: 19.2s
336:	learn: 1745.0722790	total: 9.75s	remaining: 19.2s
337:	learn: 1744.9388059	total: 9.78s	remaining: 19.1s
338:	learn: 1744.5523863	total: 9.81s	remaining: 19.1s
339:	learn: 1744.1700949	total: 9.84s	remaining: 19.1s
340:	learn: 1743.8947690	total: 9.86s	remaining: 19.1s
341:	learn: 1743.6025135	total: 9.88s	remaining: 19s
342:	learn: 1743.3779342	total: 9.92s	remaining: 19s
343:	learn: 1743.1297104	total: 9.95s	remaining: 19s
344:	learn: 1742.9431716	total: 9.99s	remaining: 19s
345:	learn: 1742.5938654	total: 10s	remaining: 18.9s
346:	learn: 1742.2808246	total: 10.1s	remaining: 18.9s
347:	learn: 1741.9959367	total: 10.1s	remaining: 18.9s
348:	learn: 1741.8208957	total: 10.1s	remaining: 18.9s
349:	learn: 1741.5574931	total: 10.2s	remaining: 18.9s
350:	learn: 1741.3508074	total: 10.2s	remaining: 18.8s
351:	learn: 1741.0473669	total: 10.2s	remaining: 18.8s
352:	learn: 1740.7527046	total: 10.2s	remaining: 18.8s
353:	learn: 1740.4376814	total: 10.3s	remaining: 18.7s
354:	learn: 1740.2260965	total: 10.3s	remaining: 18.7s
355:	learn: 1739.8518419	total: 10.3s	remaining: 18.7s
356:	learn: 1739.5107463	total: 10.3s	remaining: 18.6s
357:	learn: 1739.2744994	total: 10.4s	remaining: 18.6s
358:	learn: 1739.0320892	total: 10.4s	remaining: 18.6s
359:	learn: 1738.7449689	total: 10.4s	remaining: 18.6s
360:	learn: 1738.5872600	total: 10.5s	remaining: 18.5s
361:	learn: 1738.2819168	total: 10.5s	remaining: 18.5s
362:	learn: 1737.9732819	total: 10.5s	remaining: 18.5s
363:	learn: 1737.7074683	total: 10.5s	remaining: 18.4s
364:	learn: 1737.2512490	total: 10.6s	remaining: 18.4s
365:	learn: 1736.8637153	total: 10.6s	remaining: 18.4s
366:	learn: 1736.5370671	total: 10.6s	remaining: 18.4s
367:	learn: 1736.2583507	total: 10.7s	remaining: 18.3s
368:	learn: 1735.9664384	total: 10.7s	remaining: 18.3s
369:	learn: 1735.7577312	total: 10.7s	remaining: 18.3s
370:	learn: 1735.4378325	total: 10.8s	remaining: 18.3s
371:	learn: 1735.1671095	total: 10.8s	remaining: 18.2s
372:	learn: 1734.8602486	total: 10.8s	remaining: 18.2s
373:	learn: 1734.6183898	total: 10.9s	remaining: 18.2s
374:	learn: 1734.4011805	total: 10.9s	remaining: 18.1s
375:	learn: 1734.1227586	total: 10.9s	remaining: 18.1s
376:	learn: 1733.7815158	total: 10.9s	remaining: 18.1s
377:	learn: 1733.4587306	total: 11s	remaining: 18.1s
378:	learn: 1733.2433103	total: 11s	remaining: 18s
379:	learn: 1733.0199783	total: 11s	remaining: 18s
380:	learn: 1732.7913090	total: 11.1s	remaining: 18s
381:	learn: 1732.3455445	total: 11.1s	remaining: 18s
382:	learn: 1732.0874360	total: 11.1s	remaining: 17.9s
383:	learn: 1731.8234442	total: 11.2s	remaining: 17.9s
384:	learn: 1731.6062862	total: 11.2s	remaining: 17.9s
385:	learn: 1731.4504955	total: 11.2s	remaining: 17.8s
386:	learn: 1731.1690808	total: 11.2s	remaining: 17.8s
387:	learn: 1730.9958053	total: 11.3s	remaining: 17.8s
388:	learn: 1730.8332306	total: 11.3s	remaining: 17.7s
389:	learn: 1730.5708132	total: 11.3s	remaining: 17.7s
390:	learn: 1730.2356655	total: 11.4s	remaining: 17.7s
391:	learn: 1729.9734548	total: 11.4s	remaining: 17.7s
392:	learn: 1729.7913850	total: 11.4s	remaining: 17.6s
393:	learn: 1729.4887928	total: 11.4s	remaining: 17.6s
394:	learn: 1729.0844738	total: 11.5s	remaining: 17.6s
395:	learn: 1728.9537894	total: 11.5s	remaining: 17.5s
396:	learn: 1728.7048904	total: 11.5s	remaining: 17.5s
397:	learn: 1728.4320440	total: 11.6s	remaining: 17.5s
398:	learn: 1728.2256394	total: 11.6s	remaining: 17.5s
399:	learn: 1727.9186235	total: 11.6s	remaining: 17.4s
400:	learn: 1727.6436235	total: 11.7s	remaining: 17.4s
401:	learn: 1727.4169872	total: 11.7s	remaining: 17.4s
402:	learn: 1727.2319154	total: 11.7s	remaining: 17.3s
403:	learn: 1727.0121326	total: 11.7s	remaining: 17.3s
404:	learn: 1726.7183610	total: 11.8s	remaining: 17.3s
405:	learn: 1726.4290739	total: 11.8s	remaining: 17.2s
406:	learn: 1726.1869342	total: 11.8s	remaining: 17.2s
407:	learn: 1725.9129382	total: 11.8s	remaining: 17.2s
408:	learn: 1725.7209125	total: 11.9s	remaining: 17.2s
409:	learn: 1725.4603901	total: 11.9s	remaining: 17.2s
410:	learn: 1725.3202937	total: 11.9s	remaining: 17.1s
411:	learn: 1725.1309979	total: 12s	remaining: 17.1s
412:	learn: 1724.8196418	total: 12s	remaining: 17.1s
413:	learn: 1724.6208692	total: 12s	remaining: 17s
414:	learn: 1724.4698737	total: 12.1s	remaining: 17s
415:	learn: 1724.3155931	total: 12.1s	remaining: 17s
416:	learn: 1724.0567917	total: 12.1s	remaining: 16.9s
417:	learn: 1723.8357114	total: 12.1s	remaining: 16.9s
418:	learn: 1723.7114094	total: 12.2s	remaining: 16.9s
419:	learn: 1723.5496472	total: 12.2s	remaining: 16.9s
420:	learn: 1723.1345098	total: 12.2s	remaining: 16.8s
421:	learn: 1722.8461808	total: 12.3s	remaining: 16.8s
422:	learn: 1722.6522044	total: 12.3s	remaining: 16.8s
423:	learn: 1722.5369946	total: 12.3s	remaining: 16.7s
424:	learn: 1722.3748202	total: 12.3s	remaining: 16.7s
425:	learn: 1722.1303779	total: 12.4s	remaining: 16.7s
426:	learn: 1721.8217202	total: 12.4s	remaining: 16.6s
427:	learn: 1721.5906286	total: 12.4s	remaining: 16.6s
428:	learn: 1721.2851561	total: 12.5s	remaining: 16.6s
429:	learn: 1720.8295259	total: 12.5s	remaining: 16.6s
430:	learn: 1720.6164359	total: 12.5s	remaining: 16.5s
431:	learn: 1720.3283105	total: 12.6s	remaining: 16.5s
432:	learn: 1719.9237775	total: 12.6s	remaining: 16.5s
433:	learn: 1719.7620804	total: 12.6s	remaining: 16.4s
434:	learn: 1719.5978713	total: 12.6s	remaining: 16.4s
435:	learn: 1719.3842152	total: 12.7s	remaining: 16.4s
436:	learn: 1719.2232434	total: 12.7s	remaining: 16.4s
437:	learn: 1719.0019408	total: 12.7s	remaining: 16.3s
438:	learn: 1718.6392561	total: 12.8s	remaining: 16.3s
439:	learn: 1718.4295188	total: 12.8s	remaining: 16.3s
440:	learn: 1718.2445233	total: 12.8s	remaining: 16.2s
441:	learn: 1718.0999605	total: 12.8s	remaining: 16.2s
442:	learn: 1717.9180341	total: 12.9s	remaining: 16.2s
443:	learn: 1717.6577531	total: 12.9s	remaining: 16.1s
444:	learn: 1717.4957123	total: 12.9s	remaining: 16.1s
445:	learn: 1717.2824807	total: 13s	remaining: 16.1s
446:	learn: 1717.1734293	total: 13s	remaining: 16.1s
447:	learn: 1717.0997409	total: 13s	remaining: 16s
448:	learn: 1716.6546139	total: 13s	remaining: 16s
449:	learn: 1716.4635907	total: 13.1s	remaining: 16s
450:	learn: 1716.2512049	total: 13.1s	remaining: 15.9s
451:	learn: 1715.7920880	total: 13.1s	remaining: 15.9s
452:	learn: 1715.5239105	total: 13.2s	remaining: 15.9s
453:	learn: 1715.3264845	total: 13.2s	remaining: 15.9s
454:	learn: 1715.1536280	total: 13.2s	remaining: 15.8s
455:	learn: 1714.9451997	total: 13.2s	remaining: 15.8s
456:	learn: 1714.7188691	total: 13.3s	remaining: 15.8s
457:	learn: 1714.5335762	total: 13.3s	remaining: 15.7s
458:	learn: 1714.2417758	total: 13.3s	remaining: 15.7s
459:	learn: 1714.0575957	total: 13.4s	remaining: 15.7s
460:	learn: 1713.9067449	total: 13.4s	remaining: 15.6s
461:	learn: 1713.7251529	total: 13.4s	remaining: 15.6s
462:	learn: 1713.6329250	total: 13.4s	remaining: 15.6s
463:	learn: 1713.4685838	total: 13.5s	remaining: 15.5s
464:	learn: 1713.1377279	total: 13.5s	remaining: 15.5s
465:	learn: 1712.9426485	total: 13.5s	remaining: 15.5s
466:	learn: 1712.7144267	total: 13.5s	remaining: 15.5s
467:	learn: 1712.4403257	total: 13.6s	remaining: 15.4s
468:	learn: 1712.1955236	total: 13.6s	remaining: 15.4s
469:	learn: 1711.9863569	total: 13.6s	remaining: 15.4s
470:	learn: 1711.8146818	total: 13.6s	remaining: 15.3s
471:	learn: 1711.5892543	total: 13.7s	remaining: 15.3s
472:	learn: 1711.4275575	total: 13.7s	remaining: 15.3s
473:	learn: 1711.1612277	total: 13.7s	remaining: 15.2s
474:	learn: 1710.9839599	total: 13.8s	remaining: 15.2s
475:	learn: 1710.8741068	total: 13.8s	remaining: 15.2s
476:	learn: 1710.6589338	total: 13.8s	remaining: 15.2s
477:	learn: 1710.4240706	total: 13.8s	remaining: 15.1s
478:	learn: 1710.1560435	total: 13.9s	remaining: 15.1s
479:	learn: 1709.9921607	total: 13.9s	remaining: 15.1s
480:	learn: 1709.8490882	total: 13.9s	remaining: 15s
481:	learn: 1709.7311258	total: 14s	remaining: 15s
482:	learn: 1709.4895432	total: 14s	remaining: 15s
483:	learn: 1709.0884517	total: 14s	remaining: 14.9s
484:	learn: 1708.8016874	total: 14s	remaining: 14.9s
485:	learn: 1708.5850288	total: 14.1s	remaining: 14.9s
486:	learn: 1708.3634190	total: 14.1s	remaining: 14.9s
487:	learn: 1708.2242398	total: 14.1s	remaining: 14.8s
488:	learn: 1707.9602073	total: 14.2s	remaining: 14.8s
489:	learn: 1707.7367062	total: 14.2s	remaining: 14.8s
490:	learn: 1707.4973231	total: 14.2s	remaining: 14.7s
491:	learn: 1707.1760772	total: 14.2s	remaining: 14.7s
492:	learn: 1706.9440952	total: 14.3s	remaining: 14.7s
493:	learn: 1706.7974894	total: 14.3s	remaining: 14.7s
494:	learn: 1706.4014127	total: 14.3s	remaining: 14.6s
495:	learn: 1706.1859417	total: 14.4s	remaining: 14.6s
496:	learn: 1705.9164086	total: 14.4s	remaining: 14.6s
497:	learn: 1705.7013334	total: 14.4s	remaining: 14.5s
498:	learn: 1705.5872547	total: 14.5s	remaining: 14.5s
499:	learn: 1705.4497669	total: 14.5s	remaining: 14.5s
500:	learn: 1705.2367888	total: 14.5s	remaining: 14.5s
501:	learn: 1704.9701498	total: 14.5s	remaining: 14.4s
502:	learn: 1704.7599372	total: 14.6s	remaining: 14.4s
503:	learn: 1704.4875180	total: 14.6s	remaining: 14.4s
504:	learn: 1704.3092848	total: 14.6s	remaining: 14.3s
505:	learn: 1703.9293336	total: 14.6s	remaining: 14.3s
506:	learn: 1703.8143876	total: 14.7s	remaining: 14.3s
507:	learn: 1703.6710883	total: 14.7s	remaining: 14.2s
508:	learn: 1703.4157707	total: 14.7s	remaining: 14.2s
509:	learn: 1703.2956473	total: 14.8s	remaining: 14.2s
510:	learn: 1703.1466932	total: 14.8s	remaining: 14.1s
511:	learn: 1702.9149426	total: 14.8s	remaining: 14.1s
512:	learn: 1702.7343299	total: 14.9s	remaining: 14.1s
513:	learn: 1702.6193861	total: 14.9s	remaining: 14.1s
514:	learn: 1702.4690258	total: 14.9s	remaining: 14s
515:	learn: 1702.3233712	total: 14.9s	remaining: 14s
516:	learn: 1701.9915959	total: 15s	remaining: 14s
517:	learn: 1701.7287352	total: 15s	remaining: 13.9s
518:	learn: 1701.4654730	total: 15s	remaining: 13.9s
519:	learn: 1701.3491549	total: 15s	remaining: 13.9s
520:	learn: 1701.1247116	total: 15.1s	remaining: 13.8s
521:	learn: 1700.9548406	total: 15.1s	remaining: 13.8s
522:	learn: 1700.6687852	total: 15.1s	remaining: 13.8s
523:	learn: 1700.4789442	total: 15.2s	remaining: 13.8s
524:	learn: 1700.1771939	total: 15.2s	remaining: 13.7s
525:	learn: 1699.9532787	total: 15.2s	remaining: 13.7s
526:	learn: 1699.7881874	total: 15.2s	remaining: 13.7s
527:	learn: 1699.6537241	total: 15.3s	remaining: 13.7s
528:	learn: 1699.3310750	total: 15.3s	remaining: 13.6s
529:	learn: 1699.0993393	total: 15.3s	remaining: 13.6s
530:	learn: 1699.0392712	total: 15.3s	remaining: 13.6s
531:	learn: 1698.8859196	total: 15.4s	remaining: 13.5s
532:	learn: 1698.7663868	total: 15.4s	remaining: 13.5s
533:	learn: 1698.6345450	total: 15.4s	remaining: 13.5s
534:	learn: 1698.4363490	total: 15.5s	remaining: 13.4s
535:	learn: 1698.1399826	total: 15.5s	remaining: 13.4s
536:	learn: 1697.8343272	total: 15.5s	remaining: 13.4s
537:	learn: 1697.6942194	total: 15.5s	remaining: 13.4s
538:	learn: 1697.5777714	total: 15.6s	remaining: 13.3s
539:	learn: 1697.3771034	total: 15.6s	remaining: 13.3s
540:	learn: 1697.2139003	total: 15.6s	remaining: 13.3s
541:	learn: 1696.9732260	total: 15.7s	remaining: 13.2s
542:	learn: 1696.6663454	total: 15.7s	remaining: 13.2s
543:	learn: 1696.4584443	total: 15.7s	remaining: 13.2s
544:	learn: 1696.3300308	total: 15.8s	remaining: 13.2s
545:	learn: 1696.1459706	total: 15.8s	remaining: 13.1s
546:	learn: 1696.0292760	total: 15.8s	remaining: 13.1s
547:	learn: 1695.8142329	total: 15.8s	remaining: 13.1s
548:	learn: 1695.5343916	total: 15.9s	remaining: 13s
549:	learn: 1695.4013223	total: 15.9s	remaining: 13s
550:	learn: 1695.2973342	total: 15.9s	remaining: 13s
551:	learn: 1695.0914738	total: 16s	remaining: 13s
552:	learn: 1694.9553720	total: 16s	remaining: 12.9s
553:	learn: 1694.7989630	total: 16s	remaining: 12.9s
554:	learn: 1694.5676173	total: 16s	remaining: 12.9s
555:	learn: 1694.3650124	total: 16.1s	remaining: 12.8s
556:	learn: 1694.1690115	total: 16.1s	remaining: 12.8s
557:	learn: 1693.9422772	total: 16.1s	remaining: 12.8s
558:	learn: 1693.7942860	total: 16.2s	remaining: 12.8s
559:	learn: 1693.6682630	total: 16.2s	remaining: 12.7s
560:	learn: 1693.4982752	total: 16.2s	remaining: 12.7s
561:	learn: 1693.3805216	total: 16.3s	remaining: 12.7s
562:	learn: 1693.0483267	total: 16.3s	remaining: 12.6s
563:	learn: 1692.7784317	total: 16.3s	remaining: 12.6s
564:	learn: 1692.5272861	total: 16.3s	remaining: 12.6s
565:	learn: 1692.3072137	total: 16.4s	remaining: 12.6s
566:	learn: 1692.0689559	total: 16.4s	remaining: 12.5s
567:	learn: 1691.8829497	total: 16.4s	remaining: 12.5s
568:	learn: 1691.6567784	total: 16.5s	remaining: 12.5s
569:	learn: 1691.4518368	total: 16.5s	remaining: 12.4s
570:	learn: 1691.3116370	total: 16.5s	remaining: 12.4s
571:	learn: 1691.2298872	total: 16.5s	remaining: 12.4s
572:	learn: 1691.0800758	total: 16.6s	remaining: 12.3s
573:	learn: 1690.9613812	total: 16.6s	remaining: 12.3s
574:	learn: 1690.8424564	total: 16.6s	remaining: 12.3s
575:	learn: 1690.6983461	total: 16.6s	remaining: 12.2s
576:	learn: 1690.6315348	total: 16.7s	remaining: 12.2s
577:	learn: 1690.5542321	total: 16.7s	remaining: 12.2s
578:	learn: 1690.3116101	total: 16.7s	remaining: 12.2s
579:	learn: 1690.0861418	total: 16.7s	remaining: 12.1s
580:	learn: 1689.9615215	total: 16.8s	remaining: 12.1s
581:	learn: 1689.6233299	total: 16.8s	remaining: 12.1s
582:	learn: 1689.4918648	total: 16.8s	remaining: 12s
583:	learn: 1689.3414152	total: 16.9s	remaining: 12s
584:	learn: 1689.0787859	total: 16.9s	remaining: 12s
585:	learn: 1688.9113029	total: 16.9s	remaining: 11.9s
586:	learn: 1688.7586642	total: 16.9s	remaining: 11.9s
587:	learn: 1688.5585843	total: 17s	remaining: 11.9s
588:	learn: 1688.3361426	total: 17s	remaining: 11.9s
589:	learn: 1688.1268731	total: 17s	remaining: 11.8s
590:	learn: 1687.9854899	total: 17s	remaining: 11.8s
591:	learn: 1687.8229089	total: 17.1s	remaining: 11.8s
592:	learn: 1687.5796256	total: 17.1s	remaining: 11.7s
593:	learn: 1687.3118422	total: 17.1s	remaining: 11.7s
594:	learn: 1687.1253712	total: 17.2s	remaining: 11.7s
595:	learn: 1686.8596715	total: 17.2s	remaining: 11.7s
596:	learn: 1686.6012383	total: 17.2s	remaining: 11.6s
597:	learn: 1686.3079411	total: 17.2s	remaining: 11.6s
598:	learn: 1685.9858211	total: 17.3s	remaining: 11.6s
599:	learn: 1685.7613338	total: 17.3s	remaining: 11.5s
600:	learn: 1685.6398753	total: 17.3s	remaining: 11.5s
601:	learn: 1685.5455856	total: 17.3s	remaining: 11.5s
602:	learn: 1685.4045259	total: 17.4s	remaining: 11.4s
603:	learn: 1685.2296047	total: 17.4s	remaining: 11.4s
604:	learn: 1685.0222226	total: 17.4s	remaining: 11.4s
605:	learn: 1684.7894045	total: 17.5s	remaining: 11.3s
606:	learn: 1684.5902773	total: 17.5s	remaining: 11.3s
607:	learn: 1684.4308779	total: 17.5s	remaining: 11.3s
608:	learn: 1684.3071727	total: 17.5s	remaining: 11.3s
609:	learn: 1684.1399471	total: 17.6s	remaining: 11.2s
610:	learn: 1684.0359694	total: 17.6s	remaining: 11.2s
611:	learn: 1683.9159226	total: 17.6s	remaining: 11.2s
612:	learn: 1683.7673159	total: 17.6s	remaining: 11.1s
613:	learn: 1683.6508210	total: 17.7s	remaining: 11.1s
614:	learn: 1683.4977619	total: 17.7s	remaining: 11.1s
615:	learn: 1683.4215152	total: 17.7s	remaining: 11s
616:	learn: 1683.2737958	total: 17.8s	remaining: 11s
617:	learn: 1683.1073463	total: 17.8s	remaining: 11s
618:	learn: 1682.9982313	total: 17.8s	remaining: 11s
619:	learn: 1682.7628859	total: 17.8s	remaining: 10.9s
620:	learn: 1682.5925102	total: 17.9s	remaining: 10.9s
621:	learn: 1682.4460951	total: 17.9s	remaining: 10.9s
622:	learn: 1682.3004735	total: 17.9s	remaining: 10.8s
623:	learn: 1682.0879791	total: 18s	remaining: 10.8s
624:	learn: 1681.9803428	total: 18s	remaining: 10.8s
625:	learn: 1681.7414593	total: 18s	remaining: 10.8s
626:	learn: 1681.6471646	total: 18s	remaining: 10.7s
627:	learn: 1681.5215978	total: 18.1s	remaining: 10.7s
628:	learn: 1681.3817904	total: 18.1s	remaining: 10.7s
629:	learn: 1681.2404126	total: 18.1s	remaining: 10.6s
630:	learn: 1681.0759402	total: 18.2s	remaining: 10.6s
631:	learn: 1680.8388830	total: 18.2s	remaining: 10.6s
632:	learn: 1680.7975697	total: 18.2s	remaining: 10.6s
633:	learn: 1680.6330024	total: 18.2s	remaining: 10.5s
634:	learn: 1680.5250434	total: 18.3s	remaining: 10.5s
635:	learn: 1680.1506942	total: 18.3s	remaining: 10.5s
636:	learn: 1680.0220771	total: 18.3s	remaining: 10.4s
637:	learn: 1679.8489432	total: 18.4s	remaining: 10.4s
638:	learn: 1679.6929813	total: 18.4s	remaining: 10.4s
639:	learn: 1679.5882470	total: 18.4s	remaining: 10.4s
640:	learn: 1679.4287919	total: 18.4s	remaining: 10.3s
641:	learn: 1679.2512083	total: 18.5s	remaining: 10.3s
642:	learn: 1679.1207468	total: 18.5s	remaining: 10.3s
643:	learn: 1679.0018421	total: 18.5s	remaining: 10.2s
644:	learn: 1678.7867782	total: 18.6s	remaining: 10.2s
645:	learn: 1678.5869061	total: 18.6s	remaining: 10.2s
646:	learn: 1678.4757296	total: 18.6s	remaining: 10.2s
647:	learn: 1678.3162349	total: 18.6s	remaining: 10.1s
648:	learn: 1678.2230216	total: 18.7s	remaining: 10.1s
649:	learn: 1678.0980247	total: 18.7s	remaining: 10.1s
650:	learn: 1677.9520409	total: 18.7s	remaining: 10s
651:	learn: 1677.7827492	total: 18.8s	remaining: 10s
652:	learn: 1677.6666682	total: 18.8s	remaining: 9.99s
653:	learn: 1677.5694224	total: 18.8s	remaining: 9.96s
654:	learn: 1677.4430978	total: 18.8s	remaining: 9.93s
655:	learn: 1677.3689433	total: 18.9s	remaining: 9.9s
656:	learn: 1677.1799164	total: 18.9s	remaining: 9.87s
657:	learn: 1677.0993359	total: 18.9s	remaining: 9.84s
658:	learn: 1676.9483349	total: 19s	remaining: 9.81s
659:	learn: 1676.7887829	total: 19s	remaining: 9.78s
660:	learn: 1676.7274584	total: 19s	remaining: 9.75s
661:	learn: 1676.5851743	total: 19s	remaining: 9.72s
662:	learn: 1676.5355314	total: 19.1s	remaining: 9.69s
663:	learn: 1676.4263949	total: 19.1s	remaining: 9.66s
664:	learn: 1676.1976133	total: 19.1s	remaining: 9.63s
665:	learn: 1676.0533986	total: 19.2s	remaining: 9.6s
666:	learn: 1675.9094629	total: 19.2s	remaining: 9.57s
667:	learn: 1675.7211894	total: 19.2s	remaining: 9.54s
668:	learn: 1675.5670463	total: 19.2s	remaining: 9.51s
669:	learn: 1675.3222778	total: 19.3s	remaining: 9.49s
670:	learn: 1675.2348452	total: 19.3s	remaining: 9.46s
671:	learn: 1675.1512748	total: 19.3s	remaining: 9.43s
672:	learn: 1675.0473159	total: 19.4s	remaining: 9.4s
673:	learn: 1674.9182261	total: 19.4s	remaining: 9.38s
674:	learn: 1674.8477472	total: 19.4s	remaining: 9.35s
675:	learn: 1674.7652863	total: 19.4s	remaining: 9.32s
676:	learn: 1674.5484231	total: 19.5s	remaining: 9.29s
677:	learn: 1674.4416227	total: 19.5s	remaining: 9.26s
678:	learn: 1674.3083815	total: 19.5s	remaining: 9.24s
679:	learn: 1674.1963886	total: 19.6s	remaining: 9.21s
680:	learn: 1674.0686333	total: 19.6s	remaining: 9.18s
681:	learn: 1673.9288755	total: 19.6s	remaining: 9.16s
682:	learn: 1673.8050401	total: 19.7s	remaining: 9.13s
683:	learn: 1673.6514292	total: 19.7s	remaining: 9.1s
684:	learn: 1673.4860633	total: 19.7s	remaining: 9.07s
685:	learn: 1673.3801088	total: 19.8s	remaining: 9.04s
686:	learn: 1673.2668741	total: 19.8s	remaining: 9.01s
687:	learn: 1673.1320566	total: 19.8s	remaining: 8.98s
688:	learn: 1672.9242312	total: 19.8s	remaining: 8.96s
689:	learn: 1672.7783689	total: 19.9s	remaining: 8.93s
690:	learn: 1672.4485632	total: 19.9s	remaining: 8.9s
691:	learn: 1672.1944329	total: 19.9s	remaining: 8.87s
692:	learn: 1672.0744131	total: 20s	remaining: 8.84s
693:	learn: 1671.9306390	total: 20s	remaining: 8.81s
694:	learn: 1671.8273277	total: 20s	remaining: 8.78s
695:	learn: 1671.7406952	total: 20s	remaining: 8.75s
696:	learn: 1671.6277956	total: 20.1s	remaining: 8.72s
697:	learn: 1671.5190435	total: 20.1s	remaining: 8.69s
698:	learn: 1671.3293064	total: 20.1s	remaining: 8.67s
699:	learn: 1671.1395288	total: 20.2s	remaining: 8.64s
700:	learn: 1671.0305570	total: 20.2s	remaining: 8.61s
701:	learn: 1670.8830534	total: 20.2s	remaining: 8.58s
702:	learn: 1670.6987243	total: 20.2s	remaining: 8.55s
703:	learn: 1670.5264775	total: 20.3s	remaining: 8.52s
704:	learn: 1670.3736131	total: 20.3s	remaining: 8.49s
705:	learn: 1670.2284878	total: 20.3s	remaining: 8.46s
706:	learn: 1670.0830671	total: 20.4s	remaining: 8.43s
707:	learn: 1669.9169052	total: 20.4s	remaining: 8.4s
708:	learn: 1669.7214450	total: 20.4s	remaining: 8.38s
709:	learn: 1669.5088222	total: 20.4s	remaining: 8.35s
710:	learn: 1669.2480764	total: 20.5s	remaining: 8.32s
711:	learn: 1669.0603715	total: 20.5s	remaining: 8.29s
712:	learn: 1668.8969739	total: 20.5s	remaining: 8.26s
713:	learn: 1668.6829470	total: 20.5s	remaining: 8.23s
714:	learn: 1668.5752067	total: 20.6s	remaining: 8.2s
715:	learn: 1668.3884092	total: 20.6s	remaining: 8.17s
716:	learn: 1668.1975212	total: 20.6s	remaining: 8.14s
717:	learn: 1668.0154505	total: 20.7s	remaining: 8.11s
718:	learn: 1667.9175926	total: 20.7s	remaining: 8.08s
719:	learn: 1667.8238627	total: 20.7s	remaining: 8.05s
720:	learn: 1667.7312796	total: 20.7s	remaining: 8.02s
721:	learn: 1667.6126765	total: 20.8s	remaining: 7.99s
722:	learn: 1667.3937945	total: 20.8s	remaining: 7.97s
723:	learn: 1667.1127097	total: 20.8s	remaining: 7.94s
724:	learn: 1666.9495735	total: 20.8s	remaining: 7.91s
725:	learn: 1666.8083724	total: 20.9s	remaining: 7.88s
726:	learn: 1666.6817798	total: 20.9s	remaining: 7.85s
727:	learn: 1666.5707962	total: 20.9s	remaining: 7.82s
728:	learn: 1666.4429256	total: 20.9s	remaining: 7.79s
729:	learn: 1666.3242118	total: 21s	remaining: 7.76s
730:	learn: 1666.1656276	total: 21s	remaining: 7.73s
731:	learn: 1666.0032168	total: 21s	remaining: 7.7s
732:	learn: 1665.9098690	total: 21.1s	remaining: 7.67s
733:	learn: 1665.7050392	total: 21.1s	remaining: 7.64s
734:	learn: 1665.5150651	total: 21.1s	remaining: 7.61s
735:	learn: 1665.2716827	total: 21.1s	remaining: 7.58s
736:	learn: 1665.1770778	total: 21.2s	remaining: 7.55s
737:	learn: 1664.9812968	total: 21.2s	remaining: 7.53s
738:	learn: 1664.7602800	total: 21.2s	remaining: 7.5s
739:	learn: 1664.5857007	total: 21.3s	remaining: 7.47s
740:	learn: 1664.4995463	total: 21.3s	remaining: 7.44s
741:	learn: 1664.3400607	total: 21.3s	remaining: 7.41s
742:	learn: 1664.1186782	total: 21.3s	remaining: 7.38s
743:	learn: 1663.9704859	total: 21.4s	remaining: 7.35s
744:	learn: 1663.8010351	total: 21.4s	remaining: 7.32s
745:	learn: 1663.6506792	total: 21.4s	remaining: 7.29s
746:	learn: 1663.4934007	total: 21.4s	remaining: 7.26s
747:	learn: 1663.3464955	total: 21.5s	remaining: 7.23s
748:	learn: 1663.1735558	total: 21.5s	remaining: 7.21s
749:	learn: 1663.0615475	total: 21.5s	remaining: 7.17s
750:	learn: 1662.9499940	total: 21.6s	remaining: 7.14s
751:	learn: 1662.7739172	total: 21.6s	remaining: 7.12s
752:	learn: 1662.6127146	total: 21.6s	remaining: 7.09s
753:	learn: 1662.4772361	total: 21.6s	remaining: 7.06s
754:	learn: 1662.3319876	total: 21.7s	remaining: 7.03s
755:	learn: 1662.1768739	total: 21.7s	remaining: 7s
756:	learn: 1662.0959500	total: 21.7s	remaining: 6.97s
757:	learn: 1662.0016278	total: 21.7s	remaining: 6.94s
758:	learn: 1661.8932123	total: 21.8s	remaining: 6.91s
759:	learn: 1661.7286823	total: 21.8s	remaining: 6.88s
760:	learn: 1661.4308160	total: 21.8s	remaining: 6.85s
761:	learn: 1661.2244930	total: 21.8s	remaining: 6.82s
762:	learn: 1661.0430265	total: 21.9s	remaining: 6.8s
763:	learn: 1660.9016803	total: 21.9s	remaining: 6.77s
764:	learn: 1660.7519186	total: 21.9s	remaining: 6.74s
765:	learn: 1660.6919924	total: 22s	remaining: 6.71s
766:	learn: 1660.6510818	total: 22s	remaining: 6.68s
767:	learn: 1660.5238766	total: 22s	remaining: 6.65s
768:	learn: 1660.2959578	total: 22s	remaining: 6.62s
769:	learn: 1660.1977078	total: 22.1s	remaining: 6.59s
770:	learn: 1660.0682231	total: 22.1s	remaining: 6.56s
771:	learn: 1660.0250200	total: 22.1s	remaining: 6.53s
772:	learn: 1659.7939417	total: 22.2s	remaining: 6.5s
773:	learn: 1659.6785231	total: 22.2s	remaining: 6.47s
774:	learn: 1659.6154814	total: 22.2s	remaining: 6.45s
775:	learn: 1659.5016007	total: 22.2s	remaining: 6.42s
776:	learn: 1659.3840846	total: 22.3s	remaining: 6.39s
777:	learn: 1659.2215946	total: 22.3s	remaining: 6.36s
778:	learn: 1659.1048202	total: 22.3s	remaining: 6.33s
779:	learn: 1659.0272619	total: 22.3s	remaining: 6.3s
780:	learn: 1658.9368641	total: 22.4s	remaining: 6.27s
781:	learn: 1658.7297152	total: 22.4s	remaining: 6.24s
782:	learn: 1658.6284756	total: 22.4s	remaining: 6.21s
783:	learn: 1658.5328595	total: 22.5s	remaining: 6.19s
784:	learn: 1658.4699870	total: 22.5s	remaining: 6.16s
785:	learn: 1658.2520911	total: 22.5s	remaining: 6.13s
786:	learn: 1658.1544988	total: 22.5s	remaining: 6.1s
787:	learn: 1658.0614611	total: 22.6s	remaining: 6.07s
788:	learn: 1657.9651399	total: 22.6s	remaining: 6.04s
789:	learn: 1657.9097966	total: 22.6s	remaining: 6.01s
790:	learn: 1657.8577004	total: 22.6s	remaining: 5.98s
791:	learn: 1657.7055601	total: 22.7s	remaining: 5.95s
792:	learn: 1657.6229137	total: 22.7s	remaining: 5.92s
793:	learn: 1657.5310572	total: 22.7s	remaining: 5.89s
794:	learn: 1657.4033092	total: 22.7s	remaining: 5.87s
795:	learn: 1657.2210777	total: 22.8s	remaining: 5.84s
796:	learn: 1657.0554759	total: 22.8s	remaining: 5.81s
797:	learn: 1656.9314139	total: 22.8s	remaining: 5.78s
798:	learn: 1656.7801125	total: 22.9s	remaining: 5.75s
799:	learn: 1656.6828771	total: 22.9s	remaining: 5.72s
800:	learn: 1656.6096840	total: 22.9s	remaining: 5.69s
801:	learn: 1656.4998164	total: 22.9s	remaining: 5.67s
802:	learn: 1656.3610308	total: 23s	remaining: 5.64s
803:	learn: 1656.2376654	total: 23s	remaining: 5.61s
804:	learn: 1656.0039753	total: 23s	remaining: 5.58s
805:	learn: 1655.9022811	total: 23.1s	remaining: 5.55s
806:	learn: 1655.8402089	total: 23.1s	remaining: 5.52s
807:	learn: 1655.7522931	total: 23.1s	remaining: 5.49s
808:	learn: 1655.6101223	total: 23.1s	remaining: 5.46s
809:	learn: 1655.3613385	total: 23.2s	remaining: 5.44s
810:	learn: 1655.2796242	total: 23.2s	remaining: 5.41s
811:	learn: 1655.1841729	total: 23.2s	remaining: 5.38s
812:	learn: 1655.0948238	total: 23.3s	remaining: 5.35s
813:	learn: 1655.0391445	total: 23.3s	remaining: 5.32s
814:	learn: 1654.9420823	total: 23.3s	remaining: 5.29s
815:	learn: 1654.7717133	total: 23.3s	remaining: 5.26s
816:	learn: 1654.6516492	total: 23.4s	remaining: 5.24s
817:	learn: 1654.5292150	total: 23.4s	remaining: 5.21s
818:	learn: 1654.3562520	total: 23.4s	remaining: 5.18s
819:	learn: 1654.2280109	total: 23.5s	remaining: 5.15s
820:	learn: 1654.0289015	total: 23.5s	remaining: 5.12s
821:	learn: 1653.9738142	total: 23.5s	remaining: 5.09s
822:	learn: 1653.8215737	total: 23.5s	remaining: 5.06s
823:	learn: 1653.6768895	total: 23.6s	remaining: 5.03s
824:	learn: 1653.5871523	total: 23.6s	remaining: 5.01s
825:	learn: 1653.4787846	total: 23.6s	remaining: 4.98s
826:	learn: 1653.3452465	total: 23.7s	remaining: 4.95s
827:	learn: 1653.2525868	total: 23.7s	remaining: 4.92s
828:	learn: 1653.1923936	total: 23.7s	remaining: 4.89s
829:	learn: 1653.0449818	total: 23.7s	remaining: 4.86s
830:	learn: 1652.9810095	total: 23.8s	remaining: 4.83s
831:	learn: 1652.8423291	total: 23.8s	remaining: 4.8s
832:	learn: 1652.7379262	total: 23.8s	remaining: 4.77s
833:	learn: 1652.6850798	total: 23.8s	remaining: 4.74s
834:	learn: 1652.4922296	total: 23.9s	remaining: 4.71s
835:	learn: 1652.3671133	total: 23.9s	remaining: 4.69s
836:	learn: 1652.2230224	total: 23.9s	remaining: 4.66s
837:	learn: 1652.1282818	total: 23.9s	remaining: 4.63s
838:	learn: 1652.0192074	total: 24s	remaining: 4.6s
839:	learn: 1651.9212143	total: 24s	remaining: 4.58s
840:	learn: 1651.8046949	total: 24.1s	remaining: 4.55s
841:	learn: 1651.6124318	total: 24.1s	remaining: 4.52s
842:	learn: 1651.3732941	total: 24.1s	remaining: 4.49s
843:	learn: 1651.1958455	total: 24.1s	remaining: 4.46s
844:	learn: 1651.0054488	total: 24.2s	remaining: 4.43s
845:	learn: 1650.8539152	total: 24.2s	remaining: 4.41s
846:	learn: 1650.8095401	total: 24.2s	remaining: 4.38s
847:	learn: 1650.7470724	total: 24.3s	remaining: 4.35s
848:	learn: 1650.6077480	total: 24.3s	remaining: 4.32s
849:	learn: 1650.4595230	total: 24.3s	remaining: 4.29s
850:	learn: 1650.3111246	total: 24.3s	remaining: 4.26s
851:	learn: 1650.1873670	total: 24.4s	remaining: 4.23s
852:	learn: 1650.1517335	total: 24.4s	remaining: 4.21s
853:	learn: 1649.9912513	total: 24.4s	remaining: 4.18s
854:	learn: 1649.8504413	total: 24.5s	remaining: 4.15s
855:	learn: 1649.7403460	total: 24.5s	remaining: 4.12s
856:	learn: 1649.6081364	total: 24.5s	remaining: 4.09s
857:	learn: 1649.5319515	total: 24.5s	remaining: 4.06s
858:	learn: 1649.4020825	total: 24.6s	remaining: 4.03s
859:	learn: 1649.2566188	total: 24.6s	remaining: 4s
860:	learn: 1649.1639681	total: 24.6s	remaining: 3.98s
861:	learn: 1649.0251452	total: 24.7s	remaining: 3.95s
862:	learn: 1648.9422879	total: 24.7s	remaining: 3.92s
863:	learn: 1648.8211021	total: 24.7s	remaining: 3.89s
864:	learn: 1648.8046717	total: 24.7s	remaining: 3.86s
865:	learn: 1648.7135175	total: 24.8s	remaining: 3.83s
866:	learn: 1648.6443067	total: 24.8s	remaining: 3.8s
867:	learn: 1648.5101293	total: 24.8s	remaining: 3.77s
868:	learn: 1648.3983292	total: 24.9s	remaining: 3.75s
869:	learn: 1648.2317388	total: 24.9s	remaining: 3.72s
870:	learn: 1648.1411887	total: 24.9s	remaining: 3.69s
871:	learn: 1648.0433010	total: 24.9s	remaining: 3.66s
872:	learn: 1647.8772892	total: 25s	remaining: 3.63s
873:	learn: 1647.7283231	total: 25s	remaining: 3.6s
874:	learn: 1647.5961144	total: 25s	remaining: 3.57s
875:	learn: 1647.5271704	total: 25s	remaining: 3.54s
876:	learn: 1647.3542984	total: 25.1s	remaining: 3.52s
877:	learn: 1647.2702359	total: 25.1s	remaining: 3.49s
878:	learn: 1647.1238586	total: 25.1s	remaining: 3.46s
879:	learn: 1647.0061259	total: 25.2s	remaining: 3.43s
880:	learn: 1646.8935452	total: 25.2s	remaining: 3.4s
881:	learn: 1646.7690537	total: 25.2s	remaining: 3.37s
882:	learn: 1646.6273755	total: 25.2s	remaining: 3.34s
883:	learn: 1646.5229796	total: 25.3s	remaining: 3.31s
884:	learn: 1646.4186191	total: 25.3s	remaining: 3.29s
885:	learn: 1646.3182380	total: 25.3s	remaining: 3.26s
886:	learn: 1646.1824141	total: 25.4s	remaining: 3.23s
887:	learn: 1646.0448873	total: 25.4s	remaining: 3.2s
888:	learn: 1645.9380891	total: 25.4s	remaining: 3.17s
889:	learn: 1645.8339487	total: 25.4s	remaining: 3.14s
890:	learn: 1645.7308235	total: 25.5s	remaining: 3.11s
891:	learn: 1645.6432993	total: 25.5s	remaining: 3.08s
892:	learn: 1645.5120988	total: 25.5s	remaining: 3.06s
893:	learn: 1645.3985019	total: 25.5s	remaining: 3.03s
894:	learn: 1645.2658314	total: 25.6s	remaining: 3s
895:	learn: 1645.2081591	total: 25.6s	remaining: 2.97s
896:	learn: 1645.1313628	total: 25.6s	remaining: 2.94s
897:	learn: 1645.0632011	total: 25.7s	remaining: 2.91s
898:	learn: 1644.9224691	total: 25.7s	remaining: 2.88s
899:	learn: 1644.9100702	total: 25.7s	remaining: 2.85s
900:	learn: 1644.7971720	total: 25.7s	remaining: 2.83s
901:	learn: 1644.7180427	total: 25.8s	remaining: 2.8s
902:	learn: 1644.5388766	total: 25.8s	remaining: 2.77s
903:	learn: 1644.5272925	total: 25.8s	remaining: 2.74s
904:	learn: 1644.4639469	total: 25.8s	remaining: 2.71s
905:	learn: 1644.3768114	total: 25.9s	remaining: 2.68s
906:	learn: 1644.2328611	total: 25.9s	remaining: 2.65s
907:	learn: 1644.1653848	total: 25.9s	remaining: 2.63s
908:	learn: 1644.0272680	total: 26s	remaining: 2.6s
909:	learn: 1643.8155521	total: 26s	remaining: 2.57s
910:	learn: 1643.7488935	total: 26s	remaining: 2.54s
911:	learn: 1643.6852212	total: 26s	remaining: 2.51s
912:	learn: 1643.5467300	total: 26.1s	remaining: 2.48s
913:	learn: 1643.4063965	total: 26.1s	remaining: 2.46s
914:	learn: 1643.2820454	total: 26.1s	remaining: 2.43s
915:	learn: 1643.0413616	total: 26.2s	remaining: 2.4s
916:	learn: 1642.9500976	total: 26.2s	remaining: 2.37s
917:	learn: 1642.7840687	total: 26.2s	remaining: 2.34s
918:	learn: 1642.6040391	total: 26.3s	remaining: 2.31s
919:	learn: 1642.3680842	total: 26.3s	remaining: 2.28s
920:	learn: 1642.2624851	total: 26.3s	remaining: 2.26s
921:	learn: 1642.1634153	total: 26.3s	remaining: 2.23s
922:	learn: 1642.0564702	total: 26.4s	remaining: 2.2s
923:	learn: 1641.9233020	total: 26.4s	remaining: 2.17s
924:	learn: 1641.8500592	total: 26.4s	remaining: 2.14s
925:	learn: 1641.6778145	total: 26.4s	remaining: 2.11s
926:	learn: 1641.5398697	total: 26.5s	remaining: 2.08s
927:	learn: 1641.3496087	total: 26.5s	remaining: 2.06s
928:	learn: 1641.2698067	total: 26.5s	remaining: 2.03s
929:	learn: 1641.0578916	total: 26.6s	remaining: 2s
930:	learn: 1640.9868132	total: 26.6s	remaining: 1.97s
931:	learn: 1640.8794217	total: 26.6s	remaining: 1.94s
932:	learn: 1640.6992439	total: 26.6s	remaining: 1.91s
933:	learn: 1640.5252184	total: 26.7s	remaining: 1.88s
934:	learn: 1640.3981960	total: 26.7s	remaining: 1.85s
935:	learn: 1640.3671521	total: 26.7s	remaining: 1.83s
936:	learn: 1640.2999775	total: 26.7s	remaining: 1.8s
937:	learn: 1640.1740821	total: 26.8s	remaining: 1.77s
938:	learn: 1640.0686947	total: 26.8s	remaining: 1.74s
939:	learn: 1639.9067910	total: 26.8s	remaining: 1.71s
940:	learn: 1639.8249176	total: 26.9s	remaining: 1.68s
941:	learn: 1639.7263740	total: 26.9s	remaining: 1.66s
942:	learn: 1639.6118317	total: 26.9s	remaining: 1.63s
943:	learn: 1639.5816395	total: 26.9s	remaining: 1.6s
944:	learn: 1639.4013553	total: 27s	remaining: 1.57s
945:	learn: 1639.3433323	total: 27s	remaining: 1.54s
946:	learn: 1639.2084774	total: 27s	remaining: 1.51s
947:	learn: 1639.1270864	total: 27s	remaining: 1.48s
948:	learn: 1639.0525360	total: 27.1s	remaining: 1.46s
949:	learn: 1638.8334582	total: 27.1s	remaining: 1.43s
950:	learn: 1638.7638432	total: 27.1s	remaining: 1.4s
951:	learn: 1638.7119385	total: 27.2s	remaining: 1.37s
952:	learn: 1638.5602705	total: 27.2s	remaining: 1.34s
953:	learn: 1638.4790554	total: 27.2s	remaining: 1.31s
954:	learn: 1638.3943231	total: 27.3s	remaining: 1.28s
955:	learn: 1638.2598791	total: 27.3s	remaining: 1.26s
956:	learn: 1638.1184469	total: 27.3s	remaining: 1.23s
957:	learn: 1637.9632488	total: 27.3s	remaining: 1.2s
958:	learn: 1637.8286358	total: 27.4s	remaining: 1.17s
959:	learn: 1637.7146589	total: 27.4s	remaining: 1.14s
960:	learn: 1637.6139231	total: 27.4s	remaining: 1.11s
961:	learn: 1637.4658283	total: 27.5s	remaining: 1.08s
962:	learn: 1637.4151195	total: 27.5s	remaining: 1.06s
963:	learn: 1637.2397940	total: 27.5s	remaining: 1.03s
964:	learn: 1637.1582140	total: 27.6s	remaining: 1000ms
965:	learn: 1637.0566308	total: 27.6s	remaining: 971ms
966:	learn: 1636.9301577	total: 27.6s	remaining: 942ms
967:	learn: 1636.7611066	total: 27.6s	remaining: 914ms
968:	learn: 1636.6657628	total: 27.7s	remaining: 885ms
969:	learn: 1636.5637319	total: 27.7s	remaining: 857ms
970:	learn: 1636.4414491	total: 27.7s	remaining: 828ms
971:	learn: 1636.3135391	total: 27.8s	remaining: 800ms
972:	learn: 1636.1741379	total: 27.8s	remaining: 771ms
973:	learn: 1636.0254276	total: 27.8s	remaining: 743ms
974:	learn: 1635.9520820	total: 27.8s	remaining: 714ms
975:	learn: 1635.8568370	total: 27.9s	remaining: 685ms
976:	learn: 1635.7432587	total: 27.9s	remaining: 657ms
977:	learn: 1635.5604068	total: 27.9s	remaining: 628ms
978:	learn: 1635.4700851	total: 28s	remaining: 600ms
979:	learn: 1635.3268445	total: 28s	remaining: 571ms
980:	learn: 1635.1642479	total: 28s	remaining: 543ms
981:	learn: 1635.1005484	total: 28.1s	remaining: 514ms
982:	learn: 1634.9926400	total: 28.1s	remaining: 486ms
983:	learn: 1634.8529272	total: 28.1s	remaining: 457ms
984:	learn: 1634.7574782	total: 28.1s	remaining: 428ms
985:	learn: 1634.6540572	total: 28.2s	remaining: 400ms
986:	learn: 1634.5880965	total: 28.2s	remaining: 371ms
987:	learn: 1634.5139955	total: 28.2s	remaining: 343ms
988:	learn: 1634.4101834	total: 28.2s	remaining: 314ms
989:	learn: 1634.2461516	total: 28.3s	remaining: 286ms
990:	learn: 1634.0824664	total: 28.3s	remaining: 257ms
991:	learn: 1633.9258054	total: 28.3s	remaining: 229ms
992:	learn: 1633.8385085	total: 28.4s	remaining: 200ms
993:	learn: 1633.7259623	total: 28.4s	remaining: 171ms
994:	learn: 1633.6412548	total: 28.4s	remaining: 143ms
995:	learn: 1633.5419805	total: 28.5s	remaining: 114ms
996:	learn: 1633.4029582	total: 28.5s	remaining: 85.7ms
997:	learn: 1633.2919003	total: 28.5s	remaining: 57.1ms
998:	learn: 1633.1739968	total: 28.5s	remaining: 28.6ms
999:	learn: 1633.0554469	total: 28.6s	remaining: 0us
Learning rate set to 0.093467
0:	learn: 4286.2963472	total: 36.8ms	remaining: 36.8s
1:	learn: 4036.6503016	total: 65.1ms	remaining: 32.5s
2:	learn: 3812.9422202	total: 103ms	remaining: 34.1s
3:	learn: 3620.0030793	total: 132ms	remaining: 32.9s
4:	learn: 3442.6177429	total: 162ms	remaining: 32.3s
5:	learn: 3283.4369511	total: 200ms	remaining: 33.1s
6:	learn: 3143.3229318	total: 227ms	remaining: 32.2s
7:	learn: 3021.7701886	total: 256ms	remaining: 31.7s
8:	learn: 2908.5116606	total: 294ms	remaining: 32.3s
9:	learn: 2812.3626973	total: 322ms	remaining: 31.9s
10:	learn: 2728.0689491	total: 348ms	remaining: 31.3s
11:	learn: 2650.6120219	total: 381ms	remaining: 31.4s
12:	learn: 2582.4712481	total: 414ms	remaining: 31.4s
13:	learn: 2526.2007732	total: 441ms	remaining: 31s
14:	learn: 2470.5534876	total: 469ms	remaining: 30.8s
15:	learn: 2422.8220813	total: 503ms	remaining: 30.9s
16:	learn: 2380.0190634	total: 530ms	remaining: 30.6s
17:	learn: 2341.0719449	total: 555ms	remaining: 30.3s
18:	learn: 2305.3245378	total: 595ms	remaining: 30.7s
19:	learn: 2275.0455954	total: 622ms	remaining: 30.5s
20:	learn: 2248.8313999	total: 657ms	remaining: 30.6s
21:	learn: 2225.0686803	total: 695ms	remaining: 30.9s
22:	learn: 2203.4480799	total: 731ms	remaining: 31s
23:	learn: 2184.4473249	total: 763ms	remaining: 31s
24:	learn: 2166.1706142	total: 796ms	remaining: 31s
25:	learn: 2149.5577846	total: 823ms	remaining: 30.8s
26:	learn: 2135.0000015	total: 849ms	remaining: 30.6s
27:	learn: 2120.9283617	total: 885ms	remaining: 30.7s
28:	learn: 2109.2313610	total: 912ms	remaining: 30.5s
29:	learn: 2098.3210376	total: 938ms	remaining: 30.3s
30:	learn: 2088.6916358	total: 975ms	remaining: 30.5s
31:	learn: 2079.5111483	total: 1s	remaining: 30.3s
32:	learn: 2072.0270422	total: 1.03s	remaining: 30.2s
33:	learn: 2063.8298057	total: 1.06s	remaining: 30.2s
34:	learn: 2057.8169220	total: 1.09s	remaining: 30.1s
35:	learn: 2051.0643488	total: 1.12s	remaining: 29.9s
36:	learn: 2044.6288999	total: 1.14s	remaining: 29.8s
37:	learn: 2036.9139392	total: 1.18s	remaining: 29.8s
38:	learn: 2030.7820980	total: 1.21s	remaining: 29.7s
39:	learn: 2026.4671567	total: 1.23s	remaining: 29.5s
40:	learn: 2019.7882736	total: 1.27s	remaining: 29.6s
41:	learn: 2012.5930679	total: 1.29s	remaining: 29.6s
42:	learn: 2005.9061379	total: 1.32s	remaining: 29.5s
43:	learn: 2001.5569581	total: 1.36s	remaining: 29.5s
44:	learn: 1997.6537665	total: 1.38s	remaining: 29.4s
45:	learn: 1992.8079354	total: 1.41s	remaining: 29.3s
46:	learn: 1989.2616813	total: 1.44s	remaining: 29.2s
47:	learn: 1986.0175741	total: 1.47s	remaining: 29.2s
48:	learn: 1983.1188189	total: 1.5s	remaining: 29s
49:	learn: 1978.8497332	total: 1.52s	remaining: 28.9s
50:	learn: 1975.8992157	total: 1.56s	remaining: 29s
51:	learn: 1972.4918473	total: 1.58s	remaining: 28.9s
52:	learn: 1969.3280105	total: 1.61s	remaining: 28.8s
53:	learn: 1964.9972642	total: 1.64s	remaining: 28.7s
54:	learn: 1962.5343329	total: 1.67s	remaining: 28.7s
55:	learn: 1960.2508175	total: 1.7s	remaining: 28.6s
56:	learn: 1957.9752386	total: 1.72s	remaining: 28.5s
57:	learn: 1954.9175751	total: 1.75s	remaining: 28.5s
58:	learn: 1953.0660339	total: 1.78s	remaining: 28.4s
59:	learn: 1949.3706673	total: 1.8s	remaining: 28.3s
60:	learn: 1945.7621943	total: 1.84s	remaining: 28.3s
61:	learn: 1943.3102550	total: 1.87s	remaining: 28.3s
62:	learn: 1940.6730784	total: 1.9s	remaining: 28.2s
63:	learn: 1938.5117067	total: 1.92s	remaining: 28.1s
64:	learn: 1936.4581791	total: 1.96s	remaining: 28.2s
65:	learn: 1934.4846191	total: 1.98s	remaining: 28.1s
66:	learn: 1931.3364431	total: 2.01s	remaining: 28s
67:	learn: 1928.8436494	total: 2.04s	remaining: 28s
68:	learn: 1927.3813821	total: 2.07s	remaining: 27.9s
69:	learn: 1924.7993987	total: 2.09s	remaining: 27.8s
70:	learn: 1923.2840601	total: 2.12s	remaining: 27.7s
71:	learn: 1921.6393696	total: 2.15s	remaining: 27.7s
72:	learn: 1919.8228560	total: 2.18s	remaining: 27.7s
73:	learn: 1918.9547041	total: 2.2s	remaining: 27.6s
74:	learn: 1915.7386754	total: 2.24s	remaining: 27.6s
75:	learn: 1914.3318590	total: 2.26s	remaining: 27.5s
76:	learn: 1912.1855433	total: 2.29s	remaining: 27.4s
77:	learn: 1910.8454424	total: 2.32s	remaining: 27.4s
78:	learn: 1909.0825581	total: 2.35s	remaining: 27.4s
79:	learn: 1907.6983012	total: 2.37s	remaining: 27.3s
80:	learn: 1906.4969324	total: 2.4s	remaining: 27.3s
81:	learn: 1904.6674020	total: 2.43s	remaining: 27.2s
82:	learn: 1902.6025324	total: 2.46s	remaining: 27.2s
83:	learn: 1900.5717529	total: 2.49s	remaining: 27.1s
84:	learn: 1898.6397322	total: 2.52s	remaining: 27.2s
85:	learn: 1896.7767149	total: 2.55s	remaining: 27.1s
86:	learn: 1895.7309070	total: 2.57s	remaining: 27s
87:	learn: 1893.6437469	total: 2.61s	remaining: 27s
88:	learn: 1892.2661441	total: 2.64s	remaining: 27s
89:	learn: 1890.6562989	total: 2.66s	remaining: 26.9s
90:	learn: 1889.2272362	total: 2.7s	remaining: 26.9s
91:	learn: 1888.0824101	total: 2.73s	remaining: 26.9s
92:	learn: 1886.0864054	total: 2.75s	remaining: 26.9s
93:	learn: 1884.8562493	total: 2.78s	remaining: 26.8s
94:	learn: 1883.0069150	total: 2.81s	remaining: 26.8s
95:	learn: 1881.5567136	total: 2.84s	remaining: 26.7s
96:	learn: 1880.4709431	total: 2.86s	remaining: 26.7s
97:	learn: 1879.0818030	total: 2.89s	remaining: 26.6s
98:	learn: 1878.0348438	total: 2.92s	remaining: 26.6s
99:	learn: 1876.5936624	total: 2.94s	remaining: 26.5s
100:	learn: 1875.5458102	total: 2.97s	remaining: 26.4s
101:	learn: 1874.2392974	total: 3s	remaining: 26.4s
102:	learn: 1872.7273947	total: 3.03s	remaining: 26.4s
103:	learn: 1871.9796423	total: 3.06s	remaining: 26.3s
104:	learn: 1871.0081144	total: 3.09s	remaining: 26.3s
105:	learn: 1869.5694116	total: 3.13s	remaining: 26.4s
106:	learn: 1868.5905878	total: 3.15s	remaining: 26.3s
107:	learn: 1867.4230775	total: 3.19s	remaining: 26.3s
108:	learn: 1866.5483960	total: 3.22s	remaining: 26.3s
109:	learn: 1865.2736743	total: 3.24s	remaining: 26.2s
110:	learn: 1864.0806387	total: 3.27s	remaining: 26.2s
111:	learn: 1862.9032439	total: 3.3s	remaining: 26.2s
112:	learn: 1861.4910665	total: 3.33s	remaining: 26.1s
113:	learn: 1860.8658290	total: 3.35s	remaining: 26.1s
114:	learn: 1860.0463301	total: 3.38s	remaining: 26s
115:	learn: 1859.0809276	total: 3.41s	remaining: 26s
116:	learn: 1858.1041255	total: 3.44s	remaining: 25.9s
117:	learn: 1856.7278265	total: 3.46s	remaining: 25.9s
118:	learn: 1855.7331864	total: 3.5s	remaining: 25.9s
119:	learn: 1855.2109016	total: 3.52s	remaining: 25.8s
120:	learn: 1854.0463783	total: 3.54s	remaining: 25.8s
121:	learn: 1853.2561436	total: 3.57s	remaining: 25.7s
122:	learn: 1852.2705275	total: 3.6s	remaining: 25.7s
123:	learn: 1851.3212325	total: 3.63s	remaining: 25.6s
124:	learn: 1850.7828433	total: 3.65s	remaining: 25.6s
125:	learn: 1850.0044134	total: 3.69s	remaining: 25.6s
126:	learn: 1848.8851863	total: 3.72s	remaining: 25.5s
127:	learn: 1847.9850439	total: 3.74s	remaining: 25.5s
128:	learn: 1847.2405298	total: 3.77s	remaining: 25.5s
129:	learn: 1846.5997035	total: 3.8s	remaining: 25.5s
130:	learn: 1845.6858745	total: 3.83s	remaining: 25.4s
131:	learn: 1845.0205734	total: 3.85s	remaining: 25.4s
132:	learn: 1844.4283663	total: 3.88s	remaining: 25.3s
133:	learn: 1843.3508277	total: 3.91s	remaining: 25.3s
134:	learn: 1842.4917760	total: 3.93s	remaining: 25.2s
135:	learn: 1841.7205410	total: 3.96s	remaining: 25.2s
136:	learn: 1841.2671239	total: 3.99s	remaining: 25.1s
137:	learn: 1840.4912436	total: 4.01s	remaining: 25.1s
138:	learn: 1839.4392474	total: 4.04s	remaining: 25s
139:	learn: 1838.7598800	total: 4.07s	remaining: 25s
140:	learn: 1837.6275281	total: 4.1s	remaining: 25s
141:	learn: 1837.0300515	total: 4.12s	remaining: 24.9s
142:	learn: 1835.7506448	total: 4.16s	remaining: 24.9s
143:	learn: 1835.1490623	total: 4.18s	remaining: 24.9s
144:	learn: 1834.7767387	total: 4.21s	remaining: 24.8s
145:	learn: 1834.2017591	total: 4.23s	remaining: 24.8s
146:	learn: 1833.1115486	total: 4.27s	remaining: 24.8s
147:	learn: 1832.5274497	total: 4.29s	remaining: 24.7s
148:	learn: 1831.9104444	total: 4.32s	remaining: 24.7s
149:	learn: 1830.9384275	total: 4.35s	remaining: 24.7s
150:	learn: 1830.3468491	total: 4.38s	remaining: 24.6s
151:	learn: 1829.6852741	total: 4.4s	remaining: 24.6s
152:	learn: 1829.2867880	total: 4.43s	remaining: 24.5s
153:	learn: 1828.3951800	total: 4.46s	remaining: 24.5s
154:	learn: 1827.9332471	total: 4.49s	remaining: 24.5s
155:	learn: 1827.4409551	total: 4.51s	remaining: 24.4s
156:	learn: 1826.6181288	total: 4.55s	remaining: 24.4s
157:	learn: 1826.0683988	total: 4.57s	remaining: 24.4s
158:	learn: 1825.4405372	total: 4.6s	remaining: 24.3s
159:	learn: 1824.8747043	total: 4.63s	remaining: 24.3s
160:	learn: 1823.8438630	total: 4.66s	remaining: 24.3s
161:	learn: 1822.7986957	total: 4.69s	remaining: 24.3s
162:	learn: 1821.8371293	total: 4.72s	remaining: 24.2s
163:	learn: 1821.1604359	total: 4.75s	remaining: 24.2s
164:	learn: 1820.3884148	total: 4.78s	remaining: 24.2s
165:	learn: 1819.7372595	total: 4.8s	remaining: 24.1s
166:	learn: 1818.9114948	total: 4.83s	remaining: 24.1s
167:	learn: 1818.2721048	total: 4.86s	remaining: 24.1s
168:	learn: 1817.6493526	total: 4.89s	remaining: 24s
169:	learn: 1817.1327550	total: 4.92s	remaining: 24s
170:	learn: 1816.2850882	total: 4.95s	remaining: 24s
171:	learn: 1815.9746774	total: 4.97s	remaining: 23.9s
172:	learn: 1815.2704079	total: 5s	remaining: 23.9s
173:	learn: 1814.5983519	total: 5.03s	remaining: 23.9s
174:	learn: 1813.9587513	total: 5.06s	remaining: 23.9s
175:	learn: 1813.4714686	total: 5.08s	remaining: 23.8s
176:	learn: 1812.5540059	total: 5.11s	remaining: 23.8s
177:	learn: 1811.8692735	total: 5.14s	remaining: 23.7s
178:	learn: 1811.2310537	total: 5.17s	remaining: 23.7s
179:	learn: 1810.6224038	total: 5.19s	remaining: 23.7s
180:	learn: 1810.1706762	total: 5.22s	remaining: 23.6s
181:	learn: 1809.4580820	total: 5.25s	remaining: 23.6s
182:	learn: 1809.0431776	total: 5.28s	remaining: 23.6s
183:	learn: 1808.4229498	total: 5.31s	remaining: 23.5s
184:	learn: 1808.0511439	total: 5.34s	remaining: 23.5s
185:	learn: 1807.5310724	total: 5.36s	remaining: 23.5s
186:	learn: 1806.9048726	total: 5.38s	remaining: 23.4s
187:	learn: 1806.2448865	total: 5.42s	remaining: 23.4s
188:	learn: 1805.7372077	total: 5.45s	remaining: 23.4s
189:	learn: 1805.0334859	total: 5.47s	remaining: 23.3s
190:	learn: 1804.6288079	total: 5.5s	remaining: 23.3s
191:	learn: 1804.0443957	total: 5.53s	remaining: 23.3s
192:	learn: 1803.5516212	total: 5.56s	remaining: 23.2s
193:	learn: 1803.1706971	total: 5.58s	remaining: 23.2s
194:	learn: 1802.7091580	total: 5.62s	remaining: 23.2s
195:	learn: 1802.1294562	total: 5.65s	remaining: 23.2s
196:	learn: 1801.5709895	total: 5.67s	remaining: 23.1s
197:	learn: 1801.0411929	total: 5.7s	remaining: 23.1s
198:	learn: 1800.5304245	total: 5.73s	remaining: 23.1s
199:	learn: 1800.1347367	total: 5.76s	remaining: 23s
200:	learn: 1799.6507108	total: 5.78s	remaining: 23s
201:	learn: 1799.2618285	total: 5.82s	remaining: 23s
202:	learn: 1798.7405363	total: 5.84s	remaining: 22.9s
203:	learn: 1798.3284198	total: 5.87s	remaining: 22.9s
204:	learn: 1797.9387129	total: 5.9s	remaining: 22.9s
205:	learn: 1797.3296131	total: 5.93s	remaining: 22.9s
206:	learn: 1796.8962363	total: 5.95s	remaining: 22.8s
207:	learn: 1796.2272868	total: 5.98s	remaining: 22.8s
208:	learn: 1795.8542847	total: 6.01s	remaining: 22.8s
209:	learn: 1795.5041699	total: 6.04s	remaining: 22.7s
210:	learn: 1795.0762391	total: 6.06s	remaining: 22.7s
211:	learn: 1794.6860514	total: 6.09s	remaining: 22.7s
212:	learn: 1794.4220489	total: 6.12s	remaining: 22.6s
213:	learn: 1793.9559086	total: 6.15s	remaining: 22.6s
214:	learn: 1793.4779905	total: 6.18s	remaining: 22.6s
215:	learn: 1792.9177417	total: 6.21s	remaining: 22.5s
216:	learn: 1792.5742492	total: 6.23s	remaining: 22.5s
217:	learn: 1792.1934178	total: 6.25s	remaining: 22.4s
218:	learn: 1791.6152712	total: 6.29s	remaining: 22.4s
219:	learn: 1790.9485466	total: 6.32s	remaining: 22.4s
220:	learn: 1790.4421135	total: 6.34s	remaining: 22.4s
221:	learn: 1789.9651209	total: 6.38s	remaining: 22.4s
222:	learn: 1789.4653951	total: 6.41s	remaining: 22.3s
223:	learn: 1789.0254102	total: 6.44s	remaining: 22.3s
224:	learn: 1788.5207419	total: 6.47s	remaining: 22.3s
225:	learn: 1787.9935923	total: 6.5s	remaining: 22.3s
226:	learn: 1787.6053252	total: 6.53s	remaining: 22.2s
227:	learn: 1787.0069077	total: 6.57s	remaining: 22.2s
228:	learn: 1786.6498792	total: 6.59s	remaining: 22.2s
229:	learn: 1786.2885922	total: 6.62s	remaining: 22.2s
230:	learn: 1785.9739746	total: 6.65s	remaining: 22.1s
231:	learn: 1785.5867575	total: 6.68s	remaining: 22.1s
232:	learn: 1785.2110025	total: 6.71s	remaining: 22.1s
233:	learn: 1784.8957227	total: 6.73s	remaining: 22s
234:	learn: 1784.2937894	total: 6.76s	remaining: 22s
235:	learn: 1783.8458219	total: 6.79s	remaining: 22s
236:	learn: 1783.5137269	total: 6.81s	remaining: 21.9s
237:	learn: 1783.1772805	total: 6.85s	remaining: 21.9s
238:	learn: 1782.8928263	total: 6.87s	remaining: 21.9s
239:	learn: 1782.4346708	total: 6.9s	remaining: 21.9s
240:	learn: 1782.1075159	total: 6.94s	remaining: 21.9s
241:	learn: 1781.8655481	total: 6.97s	remaining: 21.8s
242:	learn: 1781.4764102	total: 7s	remaining: 21.8s
243:	learn: 1780.9708223	total: 7.03s	remaining: 21.8s
244:	learn: 1780.5936453	total: 7.06s	remaining: 21.8s
245:	learn: 1780.1576541	total: 7.08s	remaining: 21.7s
246:	learn: 1779.8953892	total: 7.12s	remaining: 21.7s
247:	learn: 1779.6128438	total: 7.14s	remaining: 21.7s
248:	learn: 1779.2206256	total: 7.17s	remaining: 21.6s
249:	learn: 1778.6783712	total: 7.21s	remaining: 21.6s
250:	learn: 1778.3048553	total: 7.23s	remaining: 21.6s
251:	learn: 1778.0201684	total: 7.26s	remaining: 21.5s
252:	learn: 1777.6047387	total: 7.28s	remaining: 21.5s
253:	learn: 1777.4089061	total: 7.32s	remaining: 21.5s
254:	learn: 1776.9298193	total: 7.35s	remaining: 21.5s
255:	learn: 1776.1595910	total: 7.38s	remaining: 21.4s
256:	learn: 1775.8020090	total: 7.42s	remaining: 21.4s
257:	learn: 1775.4337353	total: 7.44s	remaining: 21.4s
258:	learn: 1775.1437970	total: 7.47s	remaining: 21.4s
259:	learn: 1774.7645655	total: 7.51s	remaining: 21.4s
260:	learn: 1774.6007786	total: 7.53s	remaining: 21.3s
261:	learn: 1774.2270517	total: 7.56s	remaining: 21.3s
262:	learn: 1773.8980233	total: 7.59s	remaining: 21.3s
263:	learn: 1773.6019606	total: 7.62s	remaining: 21.2s
264:	learn: 1773.2541590	total: 7.64s	remaining: 21.2s
265:	learn: 1772.9098394	total: 7.67s	remaining: 21.2s
266:	learn: 1772.6480298	total: 7.71s	remaining: 21.2s
267:	learn: 1772.2916892	total: 7.74s	remaining: 21.1s
268:	learn: 1772.0256273	total: 7.76s	remaining: 21.1s
269:	learn: 1771.6764198	total: 7.8s	remaining: 21.1s
270:	learn: 1771.4138150	total: 7.83s	remaining: 21.1s
271:	learn: 1771.1726733	total: 7.85s	remaining: 21s
272:	learn: 1770.9139525	total: 7.89s	remaining: 21s
273:	learn: 1770.6310319	total: 7.92s	remaining: 21s
274:	learn: 1770.2217129	total: 7.94s	remaining: 20.9s
275:	learn: 1769.8580994	total: 7.98s	remaining: 20.9s
276:	learn: 1769.3327386	total: 8.01s	remaining: 20.9s
277:	learn: 1768.9842687	total: 8.03s	remaining: 20.9s
278:	learn: 1768.7405948	total: 8.07s	remaining: 20.9s
279:	learn: 1768.5476045	total: 8.09s	remaining: 20.8s
280:	learn: 1768.2350517	total: 8.12s	remaining: 20.8s
281:	learn: 1767.8639743	total: 8.15s	remaining: 20.7s
282:	learn: 1767.5420279	total: 8.18s	remaining: 20.7s
283:	learn: 1767.1342623	total: 8.2s	remaining: 20.7s
284:	learn: 1766.6719452	total: 8.23s	remaining: 20.6s
285:	learn: 1766.4257154	total: 8.26s	remaining: 20.6s
286:	learn: 1766.2606695	total: 8.28s	remaining: 20.6s
287:	learn: 1766.0128594	total: 8.3s	remaining: 20.5s
288:	learn: 1765.7747439	total: 8.34s	remaining: 20.5s
289:	learn: 1765.5564676	total: 8.37s	remaining: 20.5s
290:	learn: 1765.1584395	total: 8.4s	remaining: 20.5s
291:	learn: 1764.8616412	total: 8.43s	remaining: 20.4s
292:	learn: 1764.4635015	total: 8.46s	remaining: 20.4s
293:	learn: 1764.2829156	total: 8.48s	remaining: 20.4s
294:	learn: 1764.0783554	total: 8.52s	remaining: 20.3s
295:	learn: 1763.7073877	total: 8.54s	remaining: 20.3s
296:	learn: 1763.3546237	total: 8.58s	remaining: 20.3s
297:	learn: 1762.7518869	total: 8.62s	remaining: 20.3s
298:	learn: 1762.3325089	total: 8.64s	remaining: 20.3s
299:	learn: 1762.0082812	total: 8.67s	remaining: 20.2s
300:	learn: 1761.7399828	total: 8.71s	remaining: 20.2s
301:	learn: 1761.2515317	total: 8.75s	remaining: 20.2s
302:	learn: 1760.9695494	total: 8.78s	remaining: 20.2s
303:	learn: 1760.6656191	total: 8.81s	remaining: 20.2s
304:	learn: 1760.3112267	total: 8.84s	remaining: 20.1s
305:	learn: 1759.9555512	total: 8.87s	remaining: 20.1s
306:	learn: 1759.7164160	total: 8.89s	remaining: 20.1s
307:	learn: 1759.3171538	total: 8.93s	remaining: 20.1s
308:	learn: 1759.1493001	total: 8.96s	remaining: 20s
309:	learn: 1758.8299468	total: 9s	remaining: 20s
310:	learn: 1758.5077660	total: 9.02s	remaining: 20s
311:	learn: 1758.2953289	total: 9.05s	remaining: 20s
312:	learn: 1757.9497341	total: 9.08s	remaining: 19.9s
313:	learn: 1757.6497321	total: 9.11s	remaining: 19.9s
314:	learn: 1757.3205897	total: 9.13s	remaining: 19.9s
315:	learn: 1757.0144613	total: 9.16s	remaining: 19.8s
316:	learn: 1756.8174851	total: 9.2s	remaining: 19.8s
317:	learn: 1756.3494753	total: 9.22s	remaining: 19.8s
318:	learn: 1755.9999551	total: 9.26s	remaining: 19.8s
319:	learn: 1755.6536886	total: 9.29s	remaining: 19.7s
320:	learn: 1755.5118399	total: 9.31s	remaining: 19.7s
321:	learn: 1755.1910498	total: 9.34s	remaining: 19.7s
322:	learn: 1754.7174160	total: 9.37s	remaining: 19.6s
323:	learn: 1754.4094036	total: 9.39s	remaining: 19.6s
324:	learn: 1754.1873703	total: 9.42s	remaining: 19.6s
325:	learn: 1753.9226120	total: 9.46s	remaining: 19.6s
326:	learn: 1753.7415048	total: 9.49s	remaining: 19.5s
327:	learn: 1753.4078161	total: 9.53s	remaining: 19.5s
328:	learn: 1753.2049382	total: 9.55s	remaining: 19.5s
329:	learn: 1752.7452257	total: 9.58s	remaining: 19.5s
330:	learn: 1752.3937809	total: 9.62s	remaining: 19.4s
331:	learn: 1752.2062002	total: 9.64s	remaining: 19.4s
332:	learn: 1752.0074876	total: 9.67s	remaining: 19.4s
333:	learn: 1751.4108772	total: 9.7s	remaining: 19.3s
334:	learn: 1751.1701228	total: 9.73s	remaining: 19.3s
335:	learn: 1750.9396319	total: 9.76s	remaining: 19.3s
336:	learn: 1750.6568867	total: 9.78s	remaining: 19.2s
337:	learn: 1750.4179003	total: 9.82s	remaining: 19.2s
338:	learn: 1750.0097971	total: 9.84s	remaining: 19.2s
339:	learn: 1749.6475614	total: 9.87s	remaining: 19.2s
340:	learn: 1749.1541904	total: 9.91s	remaining: 19.1s
341:	learn: 1748.8336805	total: 9.94s	remaining: 19.1s
342:	learn: 1748.4754793	total: 9.96s	remaining: 19.1s
343:	learn: 1748.1004592	total: 10s	remaining: 19.1s
344:	learn: 1747.6660105	total: 10s	remaining: 19s
345:	learn: 1747.4178667	total: 10.1s	remaining: 19s
346:	learn: 1746.9875839	total: 10.1s	remaining: 19s
347:	learn: 1746.7546934	total: 10.1s	remaining: 19s
348:	learn: 1746.5964561	total: 10.1s	remaining: 18.9s
349:	learn: 1746.2346799	total: 10.2s	remaining: 18.9s
350:	learn: 1745.9461021	total: 10.2s	remaining: 18.9s
351:	learn: 1745.5091171	total: 10.2s	remaining: 18.8s
352:	learn: 1745.2208874	total: 10.3s	remaining: 18.8s
353:	learn: 1745.0528405	total: 10.3s	remaining: 18.8s
354:	learn: 1744.6236781	total: 10.3s	remaining: 18.7s
355:	learn: 1744.2941014	total: 10.3s	remaining: 18.7s
356:	learn: 1744.0349586	total: 10.4s	remaining: 18.7s
357:	learn: 1743.8517934	total: 10.4s	remaining: 18.6s
358:	learn: 1743.7022430	total: 10.4s	remaining: 18.6s
359:	learn: 1743.3636334	total: 10.4s	remaining: 18.6s
360:	learn: 1743.1381863	total: 10.5s	remaining: 18.6s
361:	learn: 1742.9026507	total: 10.5s	remaining: 18.5s
362:	learn: 1742.6416858	total: 10.5s	remaining: 18.5s
363:	learn: 1742.3137898	total: 10.6s	remaining: 18.4s
364:	learn: 1742.0076015	total: 10.6s	remaining: 18.4s
365:	learn: 1741.5668054	total: 10.6s	remaining: 18.4s
366:	learn: 1741.3258716	total: 10.6s	remaining: 18.4s
367:	learn: 1740.9401614	total: 10.7s	remaining: 18.3s
368:	learn: 1740.5763483	total: 10.7s	remaining: 18.3s
369:	learn: 1740.3584201	total: 10.7s	remaining: 18.3s
370:	learn: 1740.0191395	total: 10.8s	remaining: 18.2s
371:	learn: 1739.7396172	total: 10.8s	remaining: 18.2s
372:	learn: 1739.6329241	total: 10.8s	remaining: 18.2s
373:	learn: 1739.3091788	total: 10.8s	remaining: 18.1s
374:	learn: 1738.9915444	total: 10.9s	remaining: 18.1s
375:	learn: 1738.7986899	total: 10.9s	remaining: 18.1s
376:	learn: 1738.4893310	total: 10.9s	remaining: 18.1s
377:	learn: 1738.2941249	total: 11s	remaining: 18s
378:	learn: 1737.9795285	total: 11s	remaining: 18s
379:	learn: 1737.7886246	total: 11s	remaining: 18s
380:	learn: 1737.5438758	total: 11.1s	remaining: 18s
381:	learn: 1737.3271277	total: 11.1s	remaining: 17.9s
382:	learn: 1737.0665561	total: 11.1s	remaining: 17.9s
383:	learn: 1736.7825618	total: 11.2s	remaining: 17.9s
384:	learn: 1736.5778118	total: 11.2s	remaining: 17.9s
385:	learn: 1736.3626503	total: 11.2s	remaining: 17.8s
386:	learn: 1736.1689439	total: 11.2s	remaining: 17.8s
387:	learn: 1735.9733064	total: 11.3s	remaining: 17.8s
388:	learn: 1735.7655063	total: 11.3s	remaining: 17.7s
389:	learn: 1735.5452499	total: 11.3s	remaining: 17.7s
390:	learn: 1735.4435032	total: 11.3s	remaining: 17.7s
391:	learn: 1735.1709882	total: 11.4s	remaining: 17.6s
392:	learn: 1734.8905756	total: 11.4s	remaining: 17.6s
393:	learn: 1734.7153519	total: 11.4s	remaining: 17.6s
394:	learn: 1734.5482984	total: 11.5s	remaining: 17.6s
395:	learn: 1734.2773683	total: 11.5s	remaining: 17.5s
396:	learn: 1734.0339547	total: 11.5s	remaining: 17.5s
397:	learn: 1733.7334770	total: 11.6s	remaining: 17.5s
398:	learn: 1733.6165978	total: 11.6s	remaining: 17.5s
399:	learn: 1733.4355510	total: 11.6s	remaining: 17.4s
400:	learn: 1733.2195958	total: 11.7s	remaining: 17.4s
401:	learn: 1732.9910585	total: 11.7s	remaining: 17.4s
402:	learn: 1732.6998996	total: 11.7s	remaining: 17.4s
403:	learn: 1732.5161930	total: 11.7s	remaining: 17.3s
404:	learn: 1732.4039272	total: 11.8s	remaining: 17.3s
405:	learn: 1732.1314718	total: 11.8s	remaining: 17.3s
406:	learn: 1731.8206171	total: 11.8s	remaining: 17.2s
407:	learn: 1731.5837522	total: 11.9s	remaining: 17.2s
408:	learn: 1731.3991370	total: 11.9s	remaining: 17.2s
409:	learn: 1731.1328168	total: 11.9s	remaining: 17.1s
410:	learn: 1730.8627833	total: 11.9s	remaining: 17.1s
411:	learn: 1730.5340505	total: 12s	remaining: 17.1s
412:	learn: 1730.1803678	total: 12s	remaining: 17.1s
413:	learn: 1729.8776971	total: 12s	remaining: 17s
414:	learn: 1729.6195381	total: 12.1s	remaining: 17s
415:	learn: 1729.4298405	total: 12.1s	remaining: 17s
416:	learn: 1729.1739420	total: 12.1s	remaining: 17s
417:	learn: 1728.9305203	total: 12.2s	remaining: 16.9s
418:	learn: 1728.6383850	total: 12.2s	remaining: 16.9s
419:	learn: 1728.4469112	total: 12.2s	remaining: 16.9s
420:	learn: 1728.2158184	total: 12.3s	remaining: 16.9s
421:	learn: 1727.9335444	total: 12.3s	remaining: 16.8s
422:	learn: 1727.7785383	total: 12.3s	remaining: 16.8s
423:	learn: 1727.5444656	total: 12.3s	remaining: 16.8s
424:	learn: 1727.2769857	total: 12.4s	remaining: 16.7s
425:	learn: 1726.9813373	total: 12.4s	remaining: 16.7s
426:	learn: 1726.8598744	total: 12.4s	remaining: 16.7s
427:	learn: 1726.7346575	total: 12.5s	remaining: 16.6s
428:	learn: 1726.5392616	total: 12.5s	remaining: 16.6s
429:	learn: 1726.3817938	total: 12.5s	remaining: 16.6s
430:	learn: 1726.0348019	total: 12.5s	remaining: 16.6s
431:	learn: 1725.8087449	total: 12.6s	remaining: 16.5s
432:	learn: 1725.6846929	total: 12.6s	remaining: 16.5s
433:	learn: 1725.5206374	total: 12.6s	remaining: 16.5s
434:	learn: 1725.3820979	total: 12.7s	remaining: 16.4s
435:	learn: 1725.2435974	total: 12.7s	remaining: 16.4s
436:	learn: 1724.9632216	total: 12.7s	remaining: 16.4s
437:	learn: 1724.7165672	total: 12.7s	remaining: 16.4s
438:	learn: 1724.4670403	total: 12.8s	remaining: 16.3s
439:	learn: 1724.2500844	total: 12.8s	remaining: 16.3s
440:	learn: 1723.9170860	total: 12.8s	remaining: 16.3s
441:	learn: 1723.7376523	total: 12.9s	remaining: 16.2s
442:	learn: 1723.4036373	total: 12.9s	remaining: 16.2s
443:	learn: 1723.1781246	total: 12.9s	remaining: 16.2s
444:	learn: 1723.0807261	total: 12.9s	remaining: 16.1s
445:	learn: 1722.9437668	total: 13s	remaining: 16.1s
446:	learn: 1722.4544707	total: 13s	remaining: 16.1s
447:	learn: 1722.1753818	total: 13s	remaining: 16.1s
448:	learn: 1721.9279510	total: 13.1s	remaining: 16s
449:	learn: 1721.7780541	total: 13.1s	remaining: 16s
450:	learn: 1721.4904615	total: 13.1s	remaining: 16s
451:	learn: 1721.2126180	total: 13.1s	remaining: 15.9s
452:	learn: 1720.9482555	total: 13.2s	remaining: 15.9s
453:	learn: 1720.7418743	total: 13.2s	remaining: 15.9s
454:	learn: 1720.4835756	total: 13.2s	remaining: 15.8s
455:	learn: 1720.3043340	total: 13.3s	remaining: 15.8s
456:	learn: 1720.0809386	total: 13.3s	remaining: 15.8s
457:	learn: 1719.8472872	total: 13.3s	remaining: 15.8s
458:	learn: 1719.6231741	total: 13.3s	remaining: 15.7s
459:	learn: 1719.4339620	total: 13.4s	remaining: 15.7s
460:	learn: 1719.1113345	total: 13.4s	remaining: 15.7s
461:	learn: 1718.9216506	total: 13.4s	remaining: 15.6s
462:	learn: 1718.6449253	total: 13.5s	remaining: 15.6s
463:	learn: 1718.4516070	total: 13.5s	remaining: 15.6s
464:	learn: 1718.2735275	total: 13.5s	remaining: 15.5s
465:	learn: 1718.0934941	total: 13.5s	remaining: 15.5s
466:	learn: 1717.9509938	total: 13.6s	remaining: 15.5s
467:	learn: 1717.7404142	total: 13.6s	remaining: 15.5s
468:	learn: 1717.5899907	total: 13.6s	remaining: 15.4s
469:	learn: 1717.2656310	total: 13.7s	remaining: 15.4s
470:	learn: 1717.0293722	total: 13.7s	remaining: 15.4s
471:	learn: 1716.8992923	total: 13.7s	remaining: 15.3s
472:	learn: 1716.7882595	total: 13.7s	remaining: 15.3s
473:	learn: 1716.6295782	total: 13.8s	remaining: 15.3s
474:	learn: 1716.3993161	total: 13.8s	remaining: 15.3s
475:	learn: 1716.2190218	total: 13.8s	remaining: 15.2s
476:	learn: 1716.0543229	total: 13.9s	remaining: 15.2s
477:	learn: 1715.8827104	total: 13.9s	remaining: 15.2s
478:	learn: 1715.6810915	total: 13.9s	remaining: 15.1s
479:	learn: 1715.4811111	total: 13.9s	remaining: 15.1s
480:	learn: 1715.2444496	total: 14s	remaining: 15.1s
481:	learn: 1715.1047805	total: 14s	remaining: 15s
482:	learn: 1714.9458361	total: 14s	remaining: 15s
483:	learn: 1714.6442558	total: 14.1s	remaining: 15s
484:	learn: 1714.4807301	total: 14.1s	remaining: 14.9s
485:	learn: 1714.0284871	total: 14.1s	remaining: 14.9s
486:	learn: 1713.9137887	total: 14.1s	remaining: 14.9s
487:	learn: 1713.5953399	total: 14.2s	remaining: 14.9s
488:	learn: 1713.4435645	total: 14.2s	remaining: 14.8s
489:	learn: 1713.3278370	total: 14.2s	remaining: 14.8s
490:	learn: 1713.0345379	total: 14.3s	remaining: 14.8s
491:	learn: 1712.9003829	total: 14.3s	remaining: 14.7s
492:	learn: 1712.5733320	total: 14.3s	remaining: 14.7s
493:	learn: 1712.2626354	total: 14.3s	remaining: 14.7s
494:	learn: 1712.1375486	total: 14.4s	remaining: 14.7s
495:	learn: 1712.0029651	total: 14.4s	remaining: 14.6s
496:	learn: 1711.8783687	total: 14.4s	remaining: 14.6s
497:	learn: 1711.6887611	total: 14.4s	remaining: 14.6s
498:	learn: 1711.4937066	total: 14.5s	remaining: 14.5s
499:	learn: 1711.2500774	total: 14.5s	remaining: 14.5s
500:	learn: 1711.0106250	total: 14.5s	remaining: 14.5s
501:	learn: 1710.6622824	total: 14.6s	remaining: 14.4s
502:	learn: 1710.3967019	total: 14.6s	remaining: 14.4s
503:	learn: 1710.2537493	total: 14.6s	remaining: 14.4s
504:	learn: 1710.0657868	total: 14.7s	remaining: 14.4s
505:	learn: 1709.7803881	total: 14.7s	remaining: 14.3s
506:	learn: 1709.6085689	total: 14.7s	remaining: 14.3s
507:	learn: 1709.2917713	total: 14.7s	remaining: 14.3s
508:	learn: 1709.1483985	total: 14.8s	remaining: 14.2s
509:	learn: 1708.8921052	total: 14.8s	remaining: 14.2s
510:	learn: 1708.7281057	total: 14.8s	remaining: 14.2s
511:	learn: 1708.5226515	total: 14.9s	remaining: 14.2s
512:	learn: 1708.3334075	total: 14.9s	remaining: 14.1s
513:	learn: 1708.0701365	total: 14.9s	remaining: 14.1s
514:	learn: 1707.9969639	total: 14.9s	remaining: 14.1s
515:	learn: 1707.8728807	total: 15s	remaining: 14s
516:	learn: 1707.7011738	total: 15s	remaining: 14s
517:	learn: 1707.5569911	total: 15s	remaining: 14s
518:	learn: 1707.4211051	total: 15s	remaining: 13.9s
519:	learn: 1707.2573006	total: 15.1s	remaining: 13.9s
520:	learn: 1706.9806542	total: 15.1s	remaining: 13.9s
521:	learn: 1706.6764198	total: 15.1s	remaining: 13.9s
522:	learn: 1706.4665352	total: 15.2s	remaining: 13.8s
523:	learn: 1706.2040300	total: 15.2s	remaining: 13.8s
524:	learn: 1706.0187729	total: 15.2s	remaining: 13.8s
525:	learn: 1705.7935691	total: 15.3s	remaining: 13.7s
526:	learn: 1705.6377950	total: 15.3s	remaining: 13.7s
527:	learn: 1705.4742646	total: 15.3s	remaining: 13.7s
528:	learn: 1705.2907309	total: 15.3s	remaining: 13.6s
529:	learn: 1705.0938245	total: 15.4s	remaining: 13.6s
530:	learn: 1704.8330837	total: 15.4s	remaining: 13.6s
531:	learn: 1704.6816496	total: 15.4s	remaining: 13.6s
532:	learn: 1704.5582979	total: 15.5s	remaining: 13.5s
533:	learn: 1704.4800691	total: 15.5s	remaining: 13.5s
534:	learn: 1704.2715661	total: 15.5s	remaining: 13.5s
535:	learn: 1704.0043432	total: 15.5s	remaining: 13.4s
536:	learn: 1703.9079548	total: 15.6s	remaining: 13.4s
537:	learn: 1703.7005154	total: 15.6s	remaining: 13.4s
538:	learn: 1703.5137220	total: 15.6s	remaining: 13.4s
539:	learn: 1703.2519848	total: 15.7s	remaining: 13.3s
540:	learn: 1702.9926342	total: 15.7s	remaining: 13.3s
541:	learn: 1702.8273283	total: 15.7s	remaining: 13.3s
542:	learn: 1702.6653077	total: 15.7s	remaining: 13.2s
543:	learn: 1702.4734346	total: 15.8s	remaining: 13.2s
544:	learn: 1702.2042526	total: 15.8s	remaining: 13.2s
545:	learn: 1701.9865799	total: 15.8s	remaining: 13.2s
546:	learn: 1701.8604844	total: 15.9s	remaining: 13.1s
547:	learn: 1701.5597698	total: 15.9s	remaining: 13.1s
548:	learn: 1701.3083877	total: 15.9s	remaining: 13.1s
549:	learn: 1701.1930557	total: 15.9s	remaining: 13s
550:	learn: 1701.0015065	total: 16s	remaining: 13s
551:	learn: 1700.7992349	total: 16s	remaining: 13s
552:	learn: 1700.5855194	total: 16s	remaining: 13s
553:	learn: 1700.3806090	total: 16.1s	remaining: 12.9s
554:	learn: 1700.1798941	total: 16.1s	remaining: 12.9s
555:	learn: 1699.9670715	total: 16.1s	remaining: 12.9s
556:	learn: 1699.7511425	total: 16.1s	remaining: 12.8s
557:	learn: 1699.4064817	total: 16.2s	remaining: 12.8s
558:	learn: 1699.1381388	total: 16.2s	remaining: 12.8s
559:	learn: 1699.0094828	total: 16.2s	remaining: 12.8s
560:	learn: 1698.7925045	total: 16.3s	remaining: 12.7s
561:	learn: 1698.5678056	total: 16.3s	remaining: 12.7s
562:	learn: 1698.3937034	total: 16.3s	remaining: 12.7s
563:	learn: 1698.1925711	total: 16.4s	remaining: 12.6s
564:	learn: 1698.1145474	total: 16.4s	remaining: 12.6s
565:	learn: 1697.9519039	total: 16.4s	remaining: 12.6s
566:	learn: 1697.8247139	total: 16.4s	remaining: 12.6s
567:	learn: 1697.5858935	total: 16.5s	remaining: 12.5s
568:	learn: 1697.3396739	total: 16.5s	remaining: 12.5s
569:	learn: 1697.1720167	total: 16.5s	remaining: 12.5s
570:	learn: 1697.0033719	total: 16.6s	remaining: 12.4s
571:	learn: 1696.8005481	total: 16.6s	remaining: 12.4s
572:	learn: 1696.6541201	total: 16.6s	remaining: 12.4s
573:	learn: 1696.5818148	total: 16.6s	remaining: 12.3s
574:	learn: 1696.3754714	total: 16.7s	remaining: 12.3s
575:	learn: 1696.3000425	total: 16.7s	remaining: 12.3s
576:	learn: 1696.2020097	total: 16.7s	remaining: 12.3s
577:	learn: 1696.0019590	total: 16.7s	remaining: 12.2s
578:	learn: 1695.8180916	total: 16.8s	remaining: 12.2s
579:	learn: 1695.7177021	total: 16.8s	remaining: 12.2s
580:	learn: 1695.4794168	total: 16.8s	remaining: 12.1s
581:	learn: 1695.3164338	total: 16.9s	remaining: 12.1s
582:	learn: 1695.1899425	total: 16.9s	remaining: 12.1s
583:	learn: 1695.0896771	total: 16.9s	remaining: 12.1s
584:	learn: 1694.9246077	total: 16.9s	remaining: 12s
585:	learn: 1694.7634986	total: 17s	remaining: 12s
586:	learn: 1694.6179629	total: 17s	remaining: 12s
587:	learn: 1694.4259391	total: 17s	remaining: 11.9s
588:	learn: 1694.2371809	total: 17.1s	remaining: 11.9s
589:	learn: 1694.0692386	total: 17.1s	remaining: 11.9s
590:	learn: 1693.9757323	total: 17.1s	remaining: 11.8s
591:	learn: 1693.8130888	total: 17.1s	remaining: 11.8s
592:	learn: 1693.6191992	total: 17.2s	remaining: 11.8s
593:	learn: 1693.2655907	total: 17.2s	remaining: 11.8s
594:	learn: 1692.9366062	total: 17.2s	remaining: 11.7s
595:	learn: 1692.7794646	total: 17.3s	remaining: 11.7s
596:	learn: 1692.6158411	total: 17.3s	remaining: 11.7s
597:	learn: 1692.4724049	total: 17.3s	remaining: 11.6s
598:	learn: 1692.3002893	total: 17.4s	remaining: 11.6s
599:	learn: 1692.1372872	total: 17.4s	remaining: 11.6s
600:	learn: 1692.0256380	total: 17.4s	remaining: 11.6s
601:	learn: 1691.8746400	total: 17.4s	remaining: 11.5s
602:	learn: 1691.7756650	total: 17.5s	remaining: 11.5s
603:	learn: 1691.4399085	total: 17.5s	remaining: 11.5s
604:	learn: 1691.2439539	total: 17.5s	remaining: 11.4s
605:	learn: 1691.0219009	total: 17.6s	remaining: 11.4s
606:	learn: 1690.9145686	total: 17.6s	remaining: 11.4s
607:	learn: 1690.6484252	total: 17.6s	remaining: 11.4s
608:	learn: 1690.3131511	total: 17.7s	remaining: 11.3s
609:	learn: 1690.1579202	total: 17.7s	remaining: 11.3s
610:	learn: 1690.0204808	total: 17.7s	remaining: 11.3s
611:	learn: 1689.8857567	total: 17.7s	remaining: 11.2s
612:	learn: 1689.6444494	total: 17.8s	remaining: 11.2s
613:	learn: 1689.5624202	total: 17.8s	remaining: 11.2s
614:	learn: 1689.4156172	total: 17.8s	remaining: 11.2s
615:	learn: 1689.2260816	total: 17.9s	remaining: 11.1s
616:	learn: 1689.1589531	total: 17.9s	remaining: 11.1s
617:	learn: 1688.9118089	total: 17.9s	remaining: 11.1s
618:	learn: 1688.8320749	total: 17.9s	remaining: 11s
619:	learn: 1688.7433289	total: 18s	remaining: 11s
620:	learn: 1688.6580625	total: 18s	remaining: 11s
621:	learn: 1688.4420783	total: 18s	remaining: 10.9s
622:	learn: 1688.2603251	total: 18.1s	remaining: 10.9s
623:	learn: 1688.0310150	total: 18.1s	remaining: 10.9s
624:	learn: 1687.9591354	total: 18.1s	remaining: 10.9s
625:	learn: 1687.6776050	total: 18.1s	remaining: 10.8s
626:	learn: 1687.5873123	total: 18.2s	remaining: 10.8s
627:	learn: 1687.4408763	total: 18.2s	remaining: 10.8s
628:	learn: 1687.3139996	total: 18.2s	remaining: 10.7s
629:	learn: 1687.1914045	total: 18.3s	remaining: 10.7s
630:	learn: 1687.0315190	total: 18.3s	remaining: 10.7s
631:	learn: 1686.9108952	total: 18.3s	remaining: 10.7s
632:	learn: 1686.8037896	total: 18.3s	remaining: 10.6s
633:	learn: 1686.5412798	total: 18.4s	remaining: 10.6s
634:	learn: 1686.4454363	total: 18.4s	remaining: 10.6s
635:	learn: 1686.2951521	total: 18.4s	remaining: 10.6s
636:	learn: 1686.0414855	total: 18.5s	remaining: 10.5s
637:	learn: 1685.9143298	total: 18.5s	remaining: 10.5s
638:	learn: 1685.7450530	total: 18.5s	remaining: 10.5s
639:	learn: 1685.5805690	total: 18.6s	remaining: 10.4s
640:	learn: 1685.4675049	total: 18.6s	remaining: 10.4s
641:	learn: 1685.2484957	total: 18.6s	remaining: 10.4s
642:	learn: 1685.0274568	total: 18.7s	remaining: 10.4s
643:	learn: 1684.9189012	total: 18.7s	remaining: 10.3s
644:	learn: 1684.7879960	total: 18.7s	remaining: 10.3s
645:	learn: 1684.5981096	total: 18.8s	remaining: 10.3s
646:	learn: 1684.4790667	total: 18.8s	remaining: 10.2s
647:	learn: 1684.3821611	total: 18.8s	remaining: 10.2s
648:	learn: 1684.2330485	total: 18.8s	remaining: 10.2s
649:	learn: 1684.1533294	total: 18.9s	remaining: 10.2s
650:	learn: 1684.0391716	total: 18.9s	remaining: 10.1s
651:	learn: 1683.9076379	total: 18.9s	remaining: 10.1s
652:	learn: 1683.7766493	total: 19s	remaining: 10.1s
653:	learn: 1683.7201671	total: 19s	remaining: 10s
654:	learn: 1683.5800809	total: 19s	remaining: 10s
655:	learn: 1683.3888538	total: 19s	remaining: 9.99s
656:	learn: 1683.2583378	total: 19.1s	remaining: 9.96s
657:	learn: 1683.2229123	total: 19.1s	remaining: 9.93s
658:	learn: 1683.0169045	total: 19.1s	remaining: 9.9s
659:	learn: 1682.8291523	total: 19.2s	remaining: 9.87s
660:	learn: 1682.6705302	total: 19.2s	remaining: 9.85s
661:	learn: 1682.5221901	total: 19.2s	remaining: 9.81s
662:	learn: 1682.2991305	total: 19.2s	remaining: 9.78s
663:	learn: 1682.1167998	total: 19.3s	remaining: 9.76s
664:	learn: 1681.8474062	total: 19.3s	remaining: 9.73s
665:	learn: 1681.7176894	total: 19.3s	remaining: 9.7s
666:	learn: 1681.5881109	total: 19.4s	remaining: 9.66s
667:	learn: 1681.4356576	total: 19.4s	remaining: 9.64s
668:	learn: 1681.3358104	total: 19.4s	remaining: 9.61s
669:	learn: 1681.1179703	total: 19.4s	remaining: 9.58s
670:	learn: 1681.0324453	total: 19.5s	remaining: 9.55s
671:	learn: 1680.9086651	total: 19.5s	remaining: 9.52s
672:	learn: 1680.7300019	total: 19.5s	remaining: 9.49s
673:	learn: 1680.5868102	total: 19.6s	remaining: 9.46s
674:	learn: 1680.3812620	total: 19.6s	remaining: 9.43s
675:	learn: 1680.2664712	total: 19.6s	remaining: 9.4s
676:	learn: 1680.1012385	total: 19.6s	remaining: 9.37s
677:	learn: 1679.9858134	total: 19.7s	remaining: 9.34s
678:	learn: 1679.8315992	total: 19.7s	remaining: 9.31s
679:	learn: 1679.5689406	total: 19.7s	remaining: 9.28s
680:	learn: 1679.4530340	total: 19.7s	remaining: 9.25s
681:	learn: 1679.3040812	total: 19.8s	remaining: 9.22s
682:	learn: 1679.0986971	total: 19.8s	remaining: 9.19s
683:	learn: 1678.9035850	total: 19.8s	remaining: 9.16s
684:	learn: 1678.6862356	total: 19.9s	remaining: 9.13s
685:	learn: 1678.5839143	total: 19.9s	remaining: 9.1s
686:	learn: 1678.3733202	total: 19.9s	remaining: 9.07s
687:	learn: 1678.1995130	total: 19.9s	remaining: 9.04s
688:	learn: 1678.0689777	total: 20s	remaining: 9.01s
689:	learn: 1677.9066625	total: 20s	remaining: 8.98s
690:	learn: 1677.8512997	total: 20s	remaining: 8.95s
691:	learn: 1677.6670885	total: 20s	remaining: 8.92s
692:	learn: 1677.4732526	total: 20.1s	remaining: 8.9s
693:	learn: 1677.3645319	total: 20.1s	remaining: 8.86s
694:	learn: 1677.1916012	total: 20.1s	remaining: 8.83s
695:	learn: 1677.0773492	total: 20.2s	remaining: 8.8s
696:	learn: 1676.9287784	total: 20.2s	remaining: 8.78s
697:	learn: 1676.7781923	total: 20.2s	remaining: 8.74s
698:	learn: 1676.6902779	total: 20.2s	remaining: 8.71s
699:	learn: 1676.4836240	total: 20.3s	remaining: 8.69s
700:	learn: 1676.2995831	total: 20.3s	remaining: 8.66s
701:	learn: 1676.0988945	total: 20.3s	remaining: 8.63s
702:	learn: 1676.0187427	total: 20.4s	remaining: 8.6s
703:	learn: 1675.8175145	total: 20.4s	remaining: 8.57s
704:	learn: 1675.6694229	total: 20.4s	remaining: 8.54s
705:	learn: 1675.5081644	total: 20.4s	remaining: 8.51s
706:	learn: 1675.3859556	total: 20.5s	remaining: 8.48s
707:	learn: 1675.1959602	total: 20.5s	remaining: 8.45s
708:	learn: 1675.0582819	total: 20.5s	remaining: 8.42s
709:	learn: 1674.8635034	total: 20.5s	remaining: 8.39s
710:	learn: 1674.7649809	total: 20.6s	remaining: 8.36s
711:	learn: 1674.5611209	total: 20.6s	remaining: 8.33s
712:	learn: 1674.3526369	total: 20.6s	remaining: 8.3s
713:	learn: 1674.2017076	total: 20.7s	remaining: 8.28s
714:	learn: 1674.0751615	total: 20.7s	remaining: 8.24s
715:	learn: 1674.0040425	total: 20.7s	remaining: 8.21s
716:	learn: 1673.8726861	total: 20.7s	remaining: 8.19s
717:	learn: 1673.7856527	total: 20.8s	remaining: 8.16s
718:	learn: 1673.6861518	total: 20.8s	remaining: 8.13s
719:	learn: 1673.5070964	total: 20.8s	remaining: 8.1s
720:	learn: 1673.3317298	total: 20.9s	remaining: 8.07s
721:	learn: 1673.0872549	total: 20.9s	remaining: 8.04s
722:	learn: 1672.8934869	total: 20.9s	remaining: 8.01s
723:	learn: 1672.7676725	total: 20.9s	remaining: 7.98s
724:	learn: 1672.6224146	total: 21s	remaining: 7.95s
725:	learn: 1672.5349812	total: 21s	remaining: 7.92s
726:	learn: 1672.3601479	total: 21s	remaining: 7.89s
727:	learn: 1672.2204424	total: 21.1s	remaining: 7.86s
728:	learn: 1672.0913483	total: 21.1s	remaining: 7.83s
729:	learn: 1671.9234616	total: 21.1s	remaining: 7.8s
730:	learn: 1671.7930279	total: 21.1s	remaining: 7.78s
731:	learn: 1671.6154734	total: 21.2s	remaining: 7.75s
732:	learn: 1671.4691018	total: 21.2s	remaining: 7.72s
733:	learn: 1671.3662726	total: 21.2s	remaining: 7.69s
734:	learn: 1671.2150508	total: 21.2s	remaining: 7.66s
735:	learn: 1671.1002763	total: 21.3s	remaining: 7.63s
736:	learn: 1670.8448925	total: 21.3s	remaining: 7.6s
737:	learn: 1670.6583812	total: 21.3s	remaining: 7.57s
738:	learn: 1670.5701926	total: 21.4s	remaining: 7.54s
739:	learn: 1670.4800599	total: 21.4s	remaining: 7.51s
740:	learn: 1670.3295738	total: 21.4s	remaining: 7.48s
741:	learn: 1670.2280426	total: 21.4s	remaining: 7.46s
742:	learn: 1670.0964092	total: 21.5s	remaining: 7.42s
743:	learn: 1669.8474310	total: 21.5s	remaining: 7.4s
744:	learn: 1669.7142909	total: 21.5s	remaining: 7.37s
745:	learn: 1669.5630984	total: 21.6s	remaining: 7.34s
746:	learn: 1669.4164866	total: 21.6s	remaining: 7.31s
747:	learn: 1669.2865245	total: 21.6s	remaining: 7.28s
748:	learn: 1669.1752961	total: 21.6s	remaining: 7.25s
749:	learn: 1669.0615318	total: 21.7s	remaining: 7.22s
750:	learn: 1668.9497851	total: 21.7s	remaining: 7.2s
751:	learn: 1668.8257854	total: 21.7s	remaining: 7.17s
752:	learn: 1668.7195730	total: 21.8s	remaining: 7.14s
753:	learn: 1668.5707645	total: 21.8s	remaining: 7.11s
754:	learn: 1668.4641886	total: 21.8s	remaining: 7.08s
755:	learn: 1668.3445392	total: 21.8s	remaining: 7.05s
756:	learn: 1668.1272034	total: 21.9s	remaining: 7.02s
757:	learn: 1667.9582165	total: 21.9s	remaining: 6.99s
758:	learn: 1667.7877307	total: 21.9s	remaining: 6.96s
759:	learn: 1667.6778062	total: 22s	remaining: 6.93s
760:	learn: 1667.5350408	total: 22s	remaining: 6.91s
761:	learn: 1667.4527593	total: 22s	remaining: 6.88s
762:	learn: 1667.3570469	total: 22s	remaining: 6.85s
763:	learn: 1667.1210374	total: 22.1s	remaining: 6.82s
764:	learn: 1667.0292105	total: 22.1s	remaining: 6.79s
765:	learn: 1666.9051585	total: 22.1s	remaining: 6.76s
766:	learn: 1666.8039778	total: 22.2s	remaining: 6.73s
767:	learn: 1666.6727736	total: 22.2s	remaining: 6.7s
768:	learn: 1666.4808442	total: 22.2s	remaining: 6.67s
769:	learn: 1666.3561079	total: 22.2s	remaining: 6.64s
770:	learn: 1666.2341394	total: 22.3s	remaining: 6.61s
771:	learn: 1666.0896086	total: 22.3s	remaining: 6.59s
772:	learn: 1665.9620108	total: 22.3s	remaining: 6.56s
773:	learn: 1665.8675275	total: 22.4s	remaining: 6.53s
774:	learn: 1665.7587699	total: 22.4s	remaining: 6.5s
775:	learn: 1665.6476485	total: 22.4s	remaining: 6.47s
776:	learn: 1665.5291410	total: 22.5s	remaining: 6.44s
777:	learn: 1665.4501318	total: 22.5s	remaining: 6.41s
778:	learn: 1665.3761515	total: 22.5s	remaining: 6.38s
779:	learn: 1665.1300343	total: 22.5s	remaining: 6.35s
780:	learn: 1665.0619605	total: 22.6s	remaining: 6.32s
781:	learn: 1664.9496679	total: 22.6s	remaining: 6.29s
782:	learn: 1664.8428700	total: 22.6s	remaining: 6.27s
783:	learn: 1664.7491260	total: 22.6s	remaining: 6.24s
784:	learn: 1664.6830851	total: 22.7s	remaining: 6.21s
785:	learn: 1664.5236514	total: 22.7s	remaining: 6.18s
786:	learn: 1664.3916344	total: 22.7s	remaining: 6.15s
787:	learn: 1664.2107439	total: 22.7s	remaining: 6.12s
788:	learn: 1664.1254361	total: 22.8s	remaining: 6.09s
789:	learn: 1663.9937272	total: 22.8s	remaining: 6.06s
790:	learn: 1663.8209254	total: 22.8s	remaining: 6.03s
791:	learn: 1663.6816721	total: 22.9s	remaining: 6s
792:	learn: 1663.5673292	total: 22.9s	remaining: 5.97s
793:	learn: 1663.5085174	total: 22.9s	remaining: 5.94s
794:	learn: 1663.3827289	total: 22.9s	remaining: 5.92s
795:	learn: 1663.2421003	total: 23s	remaining: 5.89s
796:	learn: 1663.0792440	total: 23s	remaining: 5.86s
797:	learn: 1662.9455287	total: 23s	remaining: 5.83s
798:	learn: 1662.7913864	total: 23.1s	remaining: 5.8s
799:	learn: 1662.6788038	total: 23.1s	remaining: 5.77s
800:	learn: 1662.5607705	total: 23.1s	remaining: 5.74s
801:	learn: 1662.4001225	total: 23.1s	remaining: 5.71s
802:	learn: 1662.2822540	total: 23.2s	remaining: 5.68s
803:	learn: 1662.1258718	total: 23.2s	remaining: 5.65s
804:	learn: 1661.9932272	total: 23.2s	remaining: 5.62s
805:	learn: 1661.9243198	total: 23.3s	remaining: 5.6s
806:	learn: 1661.7848443	total: 23.3s	remaining: 5.57s
807:	learn: 1661.6503176	total: 23.3s	remaining: 5.54s
808:	learn: 1661.5898107	total: 23.3s	remaining: 5.51s
809:	learn: 1661.5330276	total: 23.4s	remaining: 5.48s
810:	learn: 1661.4483516	total: 23.4s	remaining: 5.45s
811:	learn: 1661.2739080	total: 23.4s	remaining: 5.42s
812:	learn: 1661.2190711	total: 23.4s	remaining: 5.39s
813:	learn: 1661.1377256	total: 23.5s	remaining: 5.36s
814:	learn: 1661.0237122	total: 23.5s	remaining: 5.33s
815:	learn: 1660.8871389	total: 23.5s	remaining: 5.3s
816:	learn: 1660.6833198	total: 23.6s	remaining: 5.28s
817:	learn: 1660.5878167	total: 23.6s	remaining: 5.25s
818:	learn: 1660.3726520	total: 23.6s	remaining: 5.22s
819:	learn: 1660.3071654	total: 23.6s	remaining: 5.19s
820:	learn: 1660.2001347	total: 23.7s	remaining: 5.16s
821:	learn: 1660.0809523	total: 23.7s	remaining: 5.13s
822:	learn: 1659.9498793	total: 23.7s	remaining: 5.11s
823:	learn: 1659.8118476	total: 23.8s	remaining: 5.08s
824:	learn: 1659.6328859	total: 23.8s	remaining: 5.05s
825:	learn: 1659.4716252	total: 23.8s	remaining: 5.02s
826:	learn: 1659.3017389	total: 23.9s	remaining: 4.99s
827:	learn: 1659.0573124	total: 23.9s	remaining: 4.96s
828:	learn: 1658.9463223	total: 23.9s	remaining: 4.93s
829:	learn: 1658.8366046	total: 24s	remaining: 4.91s
830:	learn: 1658.7604503	total: 24s	remaining: 4.88s
831:	learn: 1658.6496855	total: 24s	remaining: 4.85s
832:	learn: 1658.5220822	total: 24s	remaining: 4.82s
833:	learn: 1658.3623051	total: 24.1s	remaining: 4.79s
834:	learn: 1658.2442258	total: 24.1s	remaining: 4.76s
835:	learn: 1658.1196974	total: 24.1s	remaining: 4.73s
836:	learn: 1657.9922985	total: 24.1s	remaining: 4.7s
837:	learn: 1657.8366839	total: 24.2s	remaining: 4.67s
838:	learn: 1657.7242300	total: 24.2s	remaining: 4.64s
839:	learn: 1657.6628246	total: 24.2s	remaining: 4.61s
840:	learn: 1657.5905210	total: 24.3s	remaining: 4.58s
841:	learn: 1657.5480274	total: 24.3s	remaining: 4.55s
842:	learn: 1657.3715236	total: 24.3s	remaining: 4.53s
843:	learn: 1657.2321130	total: 24.3s	remaining: 4.5s
844:	learn: 1657.1296136	total: 24.4s	remaining: 4.47s
845:	learn: 1657.0549657	total: 24.4s	remaining: 4.44s
846:	learn: 1657.0167465	total: 24.4s	remaining: 4.41s
847:	learn: 1656.9106329	total: 24.4s	remaining: 4.38s
848:	learn: 1656.7895851	total: 24.5s	remaining: 4.35s
849:	learn: 1656.6744955	total: 24.5s	remaining: 4.32s
850:	learn: 1656.5888923	total: 24.5s	remaining: 4.29s
851:	learn: 1656.4708069	total: 24.6s	remaining: 4.26s
852:	learn: 1656.4000303	total: 24.6s	remaining: 4.24s
853:	learn: 1656.1768470	total: 24.6s	remaining: 4.21s
854:	learn: 1656.0725921	total: 24.6s	remaining: 4.18s
855:	learn: 1655.9833523	total: 24.7s	remaining: 4.15s
856:	learn: 1655.8487313	total: 24.7s	remaining: 4.12s
857:	learn: 1655.7368577	total: 24.7s	remaining: 4.09s
858:	learn: 1655.6495851	total: 24.7s	remaining: 4.06s
859:	learn: 1655.5878920	total: 24.8s	remaining: 4.03s
860:	learn: 1655.4179533	total: 24.8s	remaining: 4s
861:	learn: 1655.3617645	total: 24.8s	remaining: 3.97s
862:	learn: 1655.2468889	total: 24.9s	remaining: 3.94s
863:	learn: 1655.1083255	total: 24.9s	remaining: 3.92s
864:	learn: 1655.0595047	total: 24.9s	remaining: 3.89s
865:	learn: 1654.9295046	total: 24.9s	remaining: 3.86s
866:	learn: 1654.8844405	total: 25s	remaining: 3.83s
867:	learn: 1654.8152827	total: 25s	remaining: 3.8s
868:	learn: 1654.6478396	total: 25s	remaining: 3.77s
869:	learn: 1654.5005613	total: 25s	remaining: 3.74s
870:	learn: 1654.3654531	total: 25.1s	remaining: 3.71s
871:	learn: 1654.2922958	total: 25.1s	remaining: 3.68s
872:	learn: 1654.1981775	total: 25.1s	remaining: 3.65s
873:	learn: 1654.0876011	total: 25.2s	remaining: 3.63s
874:	learn: 1653.9845085	total: 25.2s	remaining: 3.6s
875:	learn: 1653.8938821	total: 25.2s	remaining: 3.57s
876:	learn: 1653.7808033	total: 25.2s	remaining: 3.54s
877:	learn: 1653.7065966	total: 25.3s	remaining: 3.51s
878:	learn: 1653.4625831	total: 25.3s	remaining: 3.48s
879:	learn: 1653.3633960	total: 25.3s	remaining: 3.45s
880:	learn: 1653.1822885	total: 25.4s	remaining: 3.42s
881:	learn: 1652.9490708	total: 25.4s	remaining: 3.4s
882:	learn: 1652.7863577	total: 25.4s	remaining: 3.37s
883:	learn: 1652.6681490	total: 25.4s	remaining: 3.34s
884:	learn: 1652.5087044	total: 25.5s	remaining: 3.31s
885:	learn: 1652.2956893	total: 25.5s	remaining: 3.28s
886:	learn: 1652.1354590	total: 25.5s	remaining: 3.25s
887:	learn: 1652.0320247	total: 25.6s	remaining: 3.22s
888:	learn: 1651.9443978	total: 25.6s	remaining: 3.19s
889:	learn: 1651.8774312	total: 25.6s	remaining: 3.17s
890:	learn: 1651.7946304	total: 25.6s	remaining: 3.14s
891:	learn: 1651.7379241	total: 25.7s	remaining: 3.11s
892:	learn: 1651.6381041	total: 25.7s	remaining: 3.08s
893:	learn: 1651.5543011	total: 25.7s	remaining: 3.05s
894:	learn: 1651.4082380	total: 25.7s	remaining: 3.02s
895:	learn: 1651.3018491	total: 25.8s	remaining: 2.99s
896:	learn: 1651.2184181	total: 25.8s	remaining: 2.96s
897:	learn: 1651.0670820	total: 25.8s	remaining: 2.93s
898:	learn: 1650.9969602	total: 25.9s	remaining: 2.9s
899:	learn: 1650.9044331	total: 25.9s	remaining: 2.88s
900:	learn: 1650.7686450	total: 25.9s	remaining: 2.85s
901:	learn: 1650.5813984	total: 25.9s	remaining: 2.82s
902:	learn: 1650.4234869	total: 26s	remaining: 2.79s
903:	learn: 1650.2506417	total: 26s	remaining: 2.76s
904:	learn: 1650.1633183	total: 26s	remaining: 2.73s
905:	learn: 1650.0997594	total: 26.1s	remaining: 2.7s
906:	learn: 1650.0682819	total: 26.1s	remaining: 2.67s
907:	learn: 1650.0153588	total: 26.1s	remaining: 2.65s
908:	learn: 1649.9059947	total: 26.1s	remaining: 2.62s
909:	learn: 1649.8264968	total: 26.2s	remaining: 2.59s
910:	learn: 1649.5978754	total: 26.2s	remaining: 2.56s
911:	learn: 1649.4707303	total: 26.2s	remaining: 2.53s
912:	learn: 1649.3017910	total: 26.2s	remaining: 2.5s
913:	learn: 1649.1546768	total: 26.3s	remaining: 2.47s
914:	learn: 1649.0198965	total: 26.3s	remaining: 2.44s
915:	learn: 1648.8213746	total: 26.3s	remaining: 2.42s
916:	learn: 1648.7323331	total: 26.4s	remaining: 2.39s
917:	learn: 1648.7000211	total: 26.4s	remaining: 2.36s
918:	learn: 1648.5962317	total: 26.4s	remaining: 2.33s
919:	learn: 1648.5675976	total: 26.4s	remaining: 2.3s
920:	learn: 1648.3914200	total: 26.5s	remaining: 2.27s
921:	learn: 1648.2456114	total: 26.5s	remaining: 2.24s
922:	learn: 1648.1507975	total: 26.5s	remaining: 2.21s
923:	learn: 1647.9270674	total: 26.6s	remaining: 2.19s
924:	learn: 1647.7921343	total: 26.6s	remaining: 2.16s
925:	learn: 1647.7342470	total: 26.6s	remaining: 2.13s
926:	learn: 1647.5947141	total: 26.7s	remaining: 2.1s
927:	learn: 1647.4999340	total: 26.7s	remaining: 2.07s
928:	learn: 1647.3358331	total: 26.7s	remaining: 2.04s
929:	learn: 1647.2533900	total: 26.7s	remaining: 2.01s
930:	learn: 1647.1800533	total: 26.8s	remaining: 1.98s
931:	learn: 1647.0683846	total: 26.8s	remaining: 1.96s
932:	learn: 1646.9319154	total: 26.8s	remaining: 1.93s
933:	learn: 1646.8427202	total: 26.9s	remaining: 1.9s
934:	learn: 1646.7236418	total: 26.9s	remaining: 1.87s
935:	learn: 1646.6296139	total: 26.9s	remaining: 1.84s
936:	learn: 1646.5572947	total: 26.9s	remaining: 1.81s
937:	learn: 1646.5414844	total: 27s	remaining: 1.78s
938:	learn: 1646.4624080	total: 27s	remaining: 1.75s
939:	learn: 1646.3520906	total: 27s	remaining: 1.72s
940:	learn: 1646.2979395	total: 27.1s	remaining: 1.7s
941:	learn: 1646.2224518	total: 27.1s	remaining: 1.67s
942:	learn: 1646.0997241	total: 27.1s	remaining: 1.64s
943:	learn: 1645.9730185	total: 27.1s	remaining: 1.61s
944:	learn: 1645.8456699	total: 27.2s	remaining: 1.58s
945:	learn: 1645.6114672	total: 27.2s	remaining: 1.55s
946:	learn: 1645.5570005	total: 27.2s	remaining: 1.52s
947:	learn: 1645.4576812	total: 27.2s	remaining: 1.49s
948:	learn: 1645.3524206	total: 27.3s	remaining: 1.47s
949:	learn: 1645.2696627	total: 27.3s	remaining: 1.44s
950:	learn: 1645.1601083	total: 27.3s	remaining: 1.41s
951:	learn: 1645.0802337	total: 27.4s	remaining: 1.38s
952:	learn: 1645.0085435	total: 27.4s	remaining: 1.35s
953:	learn: 1644.9219069	total: 27.4s	remaining: 1.32s
954:	learn: 1644.8637222	total: 27.4s	remaining: 1.29s
955:	learn: 1644.7615230	total: 27.5s	remaining: 1.26s
956:	learn: 1644.6152962	total: 27.5s	remaining: 1.24s
957:	learn: 1644.5100186	total: 27.5s	remaining: 1.21s
958:	learn: 1644.3974935	total: 27.6s	remaining: 1.18s
959:	learn: 1644.2480453	total: 27.6s	remaining: 1.15s
960:	learn: 1644.1351769	total: 27.6s	remaining: 1.12s
961:	learn: 1643.9675842	total: 27.6s	remaining: 1.09s
962:	learn: 1643.8908607	total: 27.7s	remaining: 1.06s
963:	learn: 1643.6991177	total: 27.7s	remaining: 1.03s
964:	learn: 1643.6216391	total: 27.7s	remaining: 1s
965:	learn: 1643.5495112	total: 27.8s	remaining: 977ms
966:	learn: 1643.3698123	total: 27.8s	remaining: 948ms
967:	learn: 1643.2837147	total: 27.8s	remaining: 920ms
968:	learn: 1643.1484740	total: 27.9s	remaining: 891ms
969:	learn: 1642.9877653	total: 27.9s	remaining: 862ms
970:	learn: 1642.8795028	total: 27.9s	remaining: 834ms
971:	learn: 1642.7402413	total: 28s	remaining: 805ms
972:	learn: 1642.6577768	total: 28s	remaining: 777ms
973:	learn: 1642.5645912	total: 28s	remaining: 748ms
974:	learn: 1642.5039556	total: 28.1s	remaining: 720ms
975:	learn: 1642.3351531	total: 28.1s	remaining: 691ms
976:	learn: 1642.2661876	total: 28.1s	remaining: 662ms
977:	learn: 1642.1966485	total: 28.2s	remaining: 633ms
978:	learn: 1642.1281658	total: 28.2s	remaining: 604ms
979:	learn: 1642.0215993	total: 28.2s	remaining: 576ms
980:	learn: 1641.9210359	total: 28.2s	remaining: 547ms
981:	learn: 1641.7892111	total: 28.3s	remaining: 518ms
982:	learn: 1641.7338329	total: 28.3s	remaining: 490ms
983:	learn: 1641.5914488	total: 28.3s	remaining: 461ms
984:	learn: 1641.4539548	total: 28.4s	remaining: 432ms
985:	learn: 1641.3412646	total: 28.4s	remaining: 403ms
986:	learn: 1641.2610449	total: 28.4s	remaining: 374ms
987:	learn: 1641.1744721	total: 28.4s	remaining: 345ms
988:	learn: 1641.0399788	total: 28.5s	remaining: 317ms
989:	learn: 1640.9447331	total: 28.5s	remaining: 288ms
990:	learn: 1640.8411033	total: 28.5s	remaining: 259ms
991:	learn: 1640.7228468	total: 28.6s	remaining: 230ms
992:	learn: 1640.5104114	total: 28.6s	remaining: 201ms
993:	learn: 1640.4154939	total: 28.6s	remaining: 173ms
994:	learn: 1640.3175923	total: 28.6s	remaining: 144ms
995:	learn: 1640.1868466	total: 28.7s	remaining: 115ms
996:	learn: 1640.1208851	total: 28.7s	remaining: 86.3ms
997:	learn: 1639.9796372	total: 28.7s	remaining: 57.6ms
998:	learn: 1639.8431859	total: 28.8s	remaining: 28.8ms
999:	learn: 1639.7609384	total: 28.8s	remaining: 0us
Learning rate set to 0.093467
0:	learn: 4282.9492307	total: 25.7ms	remaining: 25.7s
1:	learn: 4037.8482936	total: 51.8ms	remaining: 25.9s
2:	learn: 3812.6176441	total: 79.1ms	remaining: 26.3s
3:	learn: 3620.5832422	total: 113ms	remaining: 28.1s
4:	learn: 3444.3988734	total: 138ms	remaining: 27.5s
5:	learn: 3287.6892789	total: 165ms	remaining: 27.4s
6:	learn: 3149.2705150	total: 203ms	remaining: 28.9s
7:	learn: 3024.1237783	total: 230ms	remaining: 28.5s
8:	learn: 2914.4504801	total: 257ms	remaining: 28.3s
9:	learn: 2815.3718302	total: 294ms	remaining: 29.1s
10:	learn: 2727.5566696	total: 323ms	remaining: 29.1s
11:	learn: 2648.8250796	total: 348ms	remaining: 28.7s
12:	learn: 2582.5540133	total: 379ms	remaining: 28.8s
13:	learn: 2522.1273439	total: 408ms	remaining: 28.8s
14:	learn: 2468.8227921	total: 434ms	remaining: 28.5s
15:	learn: 2424.2179529	total: 458ms	remaining: 28.2s
16:	learn: 2379.6977773	total: 493ms	remaining: 28.5s
17:	learn: 2341.3677909	total: 519ms	remaining: 28.3s
18:	learn: 2308.0389440	total: 543ms	remaining: 28.1s
19:	learn: 2279.2045896	total: 572ms	remaining: 28s
20:	learn: 2251.7404320	total: 600ms	remaining: 28s
21:	learn: 2227.8079454	total: 625ms	remaining: 27.8s
22:	learn: 2205.3679978	total: 652ms	remaining: 27.7s
23:	learn: 2184.6380512	total: 689ms	remaining: 28s
24:	learn: 2167.1413102	total: 715ms	remaining: 27.9s
25:	learn: 2149.0952136	total: 739ms	remaining: 27.7s
26:	learn: 2133.3746699	total: 772ms	remaining: 27.8s
27:	learn: 2120.6066184	total: 803ms	remaining: 27.9s
28:	learn: 2108.2313007	total: 827ms	remaining: 27.7s
29:	learn: 2097.4523683	total: 852ms	remaining: 27.6s
30:	learn: 2087.0778032	total: 886ms	remaining: 27.7s
31:	learn: 2078.0966150	total: 911ms	remaining: 27.6s
32:	learn: 2070.2326752	total: 936ms	remaining: 27.4s
33:	learn: 2062.4560597	total: 968ms	remaining: 27.5s
34:	learn: 2055.3654152	total: 997ms	remaining: 27.5s
35:	learn: 2049.0914207	total: 1.02s	remaining: 27.4s
36:	learn: 2042.3399457	total: 1.05s	remaining: 27.4s
37:	learn: 2035.2543233	total: 1.09s	remaining: 27.5s
38:	learn: 2029.1398579	total: 1.11s	remaining: 27.4s
39:	learn: 2023.8159225	total: 1.14s	remaining: 27.4s
40:	learn: 2017.1205370	total: 1.17s	remaining: 27.5s
41:	learn: 2012.1049585	total: 1.2s	remaining: 27.3s
42:	learn: 2007.6780345	total: 1.22s	remaining: 27.3s
43:	learn: 2003.9266793	total: 1.25s	remaining: 27.3s
44:	learn: 1998.6581062	total: 1.29s	remaining: 27.3s
45:	learn: 1993.5340250	total: 1.31s	remaining: 27.2s
46:	learn: 1989.7703018	total: 1.34s	remaining: 27.2s
47:	learn: 1986.4483472	total: 1.37s	remaining: 27.2s
48:	learn: 1983.7345150	total: 1.4s	remaining: 27.1s
49:	learn: 1980.7740621	total: 1.42s	remaining: 27s
50:	learn: 1977.2271100	total: 1.45s	remaining: 27.1s
51:	learn: 1973.3246744	total: 1.48s	remaining: 27s
52:	learn: 1970.8185936	total: 1.5s	remaining: 26.9s
53:	learn: 1968.3359956	total: 1.53s	remaining: 26.8s
54:	learn: 1964.1409336	total: 1.56s	remaining: 26.8s
55:	learn: 1960.2175255	total: 1.59s	remaining: 26.8s
56:	learn: 1958.2491326	total: 1.61s	remaining: 26.7s
57:	learn: 1956.1711996	total: 1.64s	remaining: 26.7s
58:	learn: 1954.5443822	total: 1.67s	remaining: 26.6s
59:	learn: 1949.7480279	total: 1.69s	remaining: 26.5s
60:	learn: 1945.9687194	total: 1.72s	remaining: 26.4s
61:	learn: 1943.6866736	total: 1.75s	remaining: 26.5s
62:	learn: 1940.2999042	total: 1.78s	remaining: 26.4s
63:	learn: 1938.6826442	total: 1.8s	remaining: 26.3s
64:	learn: 1937.0476886	total: 1.83s	remaining: 26.3s
65:	learn: 1935.4056837	total: 1.86s	remaining: 26.3s
66:	learn: 1933.7006351	total: 1.88s	remaining: 26.2s
67:	learn: 1931.8845036	total: 1.9s	remaining: 26.1s
68:	learn: 1929.5118349	total: 1.94s	remaining: 26.2s
69:	learn: 1927.7565178	total: 1.97s	remaining: 26.1s
70:	learn: 1924.0771990	total: 1.99s	remaining: 26.1s
71:	learn: 1921.6224265	total: 2.02s	remaining: 26.1s
72:	learn: 1919.9103274	total: 2.05s	remaining: 26s
73:	learn: 1917.5701110	total: 2.07s	remaining: 26s
74:	learn: 1915.6766396	total: 2.1s	remaining: 25.9s
75:	learn: 1914.0275496	total: 2.14s	remaining: 26s
76:	learn: 1911.4751345	total: 2.16s	remaining: 25.9s
77:	learn: 1910.4993908	total: 2.18s	remaining: 25.8s
78:	learn: 1908.9887519	total: 2.21s	remaining: 25.8s
79:	learn: 1907.0080901	total: 2.24s	remaining: 25.7s
80:	learn: 1905.1414714	total: 2.26s	remaining: 25.7s
81:	learn: 1903.4216483	total: 2.29s	remaining: 25.6s
82:	learn: 1901.0869488	total: 2.33s	remaining: 25.7s
83:	learn: 1898.4072913	total: 2.35s	remaining: 25.7s
84:	learn: 1896.7893324	total: 2.38s	remaining: 25.6s
85:	learn: 1895.7525685	total: 2.41s	remaining: 25.6s
86:	learn: 1894.4420679	total: 2.43s	remaining: 25.5s
87:	learn: 1892.5427785	total: 2.46s	remaining: 25.5s
88:	learn: 1891.2607146	total: 2.48s	remaining: 25.4s
89:	learn: 1889.7245399	total: 2.52s	remaining: 25.4s
90:	learn: 1887.8295453	total: 2.54s	remaining: 25.4s
91:	learn: 1887.0189579	total: 2.56s	remaining: 25.3s
92:	learn: 1886.0819412	total: 2.59s	remaining: 25.3s
93:	learn: 1884.7883595	total: 2.62s	remaining: 25.3s
94:	learn: 1883.7052444	total: 2.65s	remaining: 25.2s
95:	learn: 1882.3708223	total: 2.67s	remaining: 25.2s
96:	learn: 1881.5368103	total: 2.71s	remaining: 25.2s
97:	learn: 1880.7402856	total: 2.73s	remaining: 25.1s
98:	learn: 1879.3663249	total: 2.76s	remaining: 25.1s
99:	learn: 1877.9862328	total: 2.79s	remaining: 25.2s
100:	learn: 1876.4766445	total: 2.82s	remaining: 25.1s
101:	learn: 1875.3010950	total: 2.85s	remaining: 25.1s
102:	learn: 1874.5189999	total: 2.87s	remaining: 25s
103:	learn: 1873.0737277	total: 2.9s	remaining: 25s
104:	learn: 1871.0835708	total: 2.93s	remaining: 25s
105:	learn: 1870.4235872	total: 2.95s	remaining: 24.9s
106:	learn: 1869.1242222	total: 2.99s	remaining: 25s
107:	learn: 1867.8385222	total: 3.02s	remaining: 24.9s
108:	learn: 1866.7979293	total: 3.04s	remaining: 24.8s
109:	learn: 1865.7111149	total: 3.07s	remaining: 24.9s
110:	learn: 1864.4976533	total: 3.1s	remaining: 24.9s
111:	learn: 1863.5048890	total: 3.13s	remaining: 24.8s
112:	learn: 1862.0700180	total: 3.16s	remaining: 24.8s
113:	learn: 1861.0290414	total: 3.19s	remaining: 24.8s
114:	learn: 1860.4285048	total: 3.22s	remaining: 24.8s
115:	learn: 1859.3929543	total: 3.24s	remaining: 24.7s
116:	learn: 1858.6064304	total: 3.27s	remaining: 24.7s
117:	learn: 1857.4365070	total: 3.3s	remaining: 24.7s
118:	learn: 1856.2225656	total: 3.32s	remaining: 24.6s
119:	learn: 1855.4000919	total: 3.35s	remaining: 24.5s
120:	learn: 1854.7534103	total: 3.38s	remaining: 24.5s
121:	learn: 1853.9929664	total: 3.4s	remaining: 24.5s
122:	learn: 1852.6684254	total: 3.43s	remaining: 24.4s
123:	learn: 1851.8222836	total: 3.46s	remaining: 24.4s
124:	learn: 1851.2519849	total: 3.48s	remaining: 24.4s
125:	learn: 1850.2853774	total: 3.51s	remaining: 24.3s
126:	learn: 1849.6968795	total: 3.53s	remaining: 24.3s
127:	learn: 1849.1208581	total: 3.57s	remaining: 24.3s
128:	learn: 1847.8908737	total: 3.59s	remaining: 24.3s
129:	learn: 1847.1416009	total: 3.62s	remaining: 24.2s
130:	learn: 1846.0172831	total: 3.65s	remaining: 24.2s
131:	learn: 1845.4145415	total: 3.67s	remaining: 24.2s
132:	learn: 1844.6967352	total: 3.7s	remaining: 24.1s
133:	learn: 1843.5002376	total: 3.73s	remaining: 24.1s
134:	learn: 1842.2851067	total: 3.76s	remaining: 24.1s
135:	learn: 1841.7587258	total: 3.78s	remaining: 24s
136:	learn: 1841.1246120	total: 3.81s	remaining: 24s
137:	learn: 1840.5250265	total: 3.83s	remaining: 23.9s
138:	learn: 1840.1041298	total: 3.86s	remaining: 23.9s
139:	learn: 1839.1149943	total: 3.89s	remaining: 23.9s
140:	learn: 1838.1306163	total: 3.91s	remaining: 23.8s
141:	learn: 1837.5679478	total: 3.95s	remaining: 23.9s
142:	learn: 1836.5901097	total: 3.98s	remaining: 23.8s
143:	learn: 1835.8054508	total: 4s	remaining: 23.8s
144:	learn: 1835.0600775	total: 4.03s	remaining: 23.8s
145:	learn: 1834.0306817	total: 4.06s	remaining: 23.8s
146:	learn: 1833.4113796	total: 4.09s	remaining: 23.7s
147:	learn: 1832.6287063	total: 4.11s	remaining: 23.7s
148:	learn: 1832.1961176	total: 4.14s	remaining: 23.7s
149:	learn: 1831.6029309	total: 4.17s	remaining: 23.6s
150:	learn: 1830.7185196	total: 4.2s	remaining: 23.6s
151:	learn: 1829.8034132	total: 4.22s	remaining: 23.6s
152:	learn: 1829.3243745	total: 4.25s	remaining: 23.6s
153:	learn: 1827.9692209	total: 4.28s	remaining: 23.5s
154:	learn: 1827.3008351	total: 4.3s	remaining: 23.5s
155:	learn: 1826.2318173	total: 4.34s	remaining: 23.5s
156:	learn: 1825.6840640	total: 4.37s	remaining: 23.4s
157:	learn: 1825.0874346	total: 4.39s	remaining: 23.4s
158:	learn: 1824.5984914	total: 4.42s	remaining: 23.4s
159:	learn: 1823.9083937	total: 4.44s	remaining: 23.3s
160:	learn: 1823.1856171	total: 4.47s	remaining: 23.3s
161:	learn: 1822.7653690	total: 4.49s	remaining: 23.2s
162:	learn: 1822.0870253	total: 4.52s	remaining: 23.2s
163:	learn: 1821.4882303	total: 4.55s	remaining: 23.2s
164:	learn: 1820.3610661	total: 4.58s	remaining: 23.2s
165:	learn: 1819.8016593	total: 4.6s	remaining: 23.1s
166:	learn: 1819.1002075	total: 4.64s	remaining: 23.1s
167:	learn: 1818.3334014	total: 4.66s	remaining: 23.1s
168:	learn: 1817.2853736	total: 4.69s	remaining: 23s
169:	learn: 1816.8276125	total: 4.72s	remaining: 23s
170:	learn: 1815.9109038	total: 4.75s	remaining: 23s
171:	learn: 1815.4504422	total: 4.77s	remaining: 23s
172:	learn: 1814.6231119	total: 4.8s	remaining: 22.9s
173:	learn: 1814.3053671	total: 4.83s	remaining: 22.9s
174:	learn: 1813.8331893	total: 4.85s	remaining: 22.9s
175:	learn: 1812.6997531	total: 4.88s	remaining: 22.8s
176:	learn: 1812.0022859	total: 4.91s	remaining: 22.8s
177:	learn: 1811.2716173	total: 4.93s	remaining: 22.8s
178:	learn: 1810.7431976	total: 4.96s	remaining: 22.7s
179:	learn: 1810.1522438	total: 4.98s	remaining: 22.7s
180:	learn: 1809.3601292	total: 5.02s	remaining: 22.7s
181:	learn: 1808.8991431	total: 5.04s	remaining: 22.7s
182:	learn: 1808.3372812	total: 5.07s	remaining: 22.6s
183:	learn: 1807.5713197	total: 5.1s	remaining: 22.6s
184:	learn: 1807.1572558	total: 5.13s	remaining: 22.6s
185:	learn: 1806.6754271	total: 5.15s	remaining: 22.5s
186:	learn: 1805.8923991	total: 5.18s	remaining: 22.5s
187:	learn: 1805.1936020	total: 5.21s	remaining: 22.5s
188:	learn: 1804.8713285	total: 5.24s	remaining: 22.5s
189:	learn: 1804.1868011	total: 5.26s	remaining: 22.4s
190:	learn: 1803.8686040	total: 5.29s	remaining: 22.4s
191:	learn: 1803.0046225	total: 5.31s	remaining: 22.4s
192:	learn: 1802.4563049	total: 5.34s	remaining: 22.3s
193:	learn: 1802.0407852	total: 5.36s	remaining: 22.3s
194:	learn: 1801.3666526	total: 5.4s	remaining: 22.3s
195:	learn: 1800.9489950	total: 5.42s	remaining: 22.2s
196:	learn: 1800.2623932	total: 5.45s	remaining: 22.2s
197:	learn: 1799.6822213	total: 5.48s	remaining: 22.2s
198:	learn: 1799.2411532	total: 5.51s	remaining: 22.2s
199:	learn: 1798.6838820	total: 5.53s	remaining: 22.1s
200:	learn: 1798.3085481	total: 5.56s	remaining: 22.1s
201:	learn: 1797.8592960	total: 5.59s	remaining: 22.1s
202:	learn: 1797.2333505	total: 5.62s	remaining: 22.1s
203:	learn: 1796.7948060	total: 5.64s	remaining: 22s
204:	learn: 1796.2169892	total: 5.66s	remaining: 22s
205:	learn: 1795.5262734	total: 5.7s	remaining: 22s
206:	learn: 1794.9331048	total: 5.73s	remaining: 21.9s
207:	learn: 1794.4337976	total: 5.75s	remaining: 21.9s
208:	learn: 1794.0644945	total: 5.78s	remaining: 21.9s
209:	learn: 1793.2867453	total: 5.81s	remaining: 21.9s
210:	learn: 1792.8892847	total: 5.83s	remaining: 21.8s
211:	learn: 1792.0766933	total: 5.86s	remaining: 21.8s
212:	learn: 1791.7437017	total: 5.89s	remaining: 21.8s
213:	learn: 1791.3993162	total: 5.92s	remaining: 21.7s
214:	learn: 1790.9304081	total: 5.94s	remaining: 21.7s
215:	learn: 1790.4859097	total: 5.98s	remaining: 21.7s
216:	learn: 1790.0678677	total: 6s	remaining: 21.7s
217:	learn: 1789.7304470	total: 6.03s	remaining: 21.6s
218:	learn: 1789.2976599	total: 6.06s	remaining: 21.6s
219:	learn: 1788.6308659	total: 6.09s	remaining: 21.6s
220:	learn: 1788.2698633	total: 6.12s	remaining: 21.6s
221:	learn: 1787.9033616	total: 6.14s	remaining: 21.5s
222:	learn: 1787.5035410	total: 6.17s	remaining: 21.5s
223:	learn: 1786.8635343	total: 6.21s	remaining: 21.5s
224:	learn: 1786.3455545	total: 6.23s	remaining: 21.5s
225:	learn: 1785.9098402	total: 6.26s	remaining: 21.4s
226:	learn: 1785.6854240	total: 6.29s	remaining: 21.4s
227:	learn: 1785.2745977	total: 6.31s	remaining: 21.4s
228:	learn: 1784.7953540	total: 6.34s	remaining: 21.3s
229:	learn: 1784.4379239	total: 6.37s	remaining: 21.3s
230:	learn: 1783.6009750	total: 6.39s	remaining: 21.3s
231:	learn: 1783.3292475	total: 6.42s	remaining: 21.2s
232:	learn: 1782.7320356	total: 6.44s	remaining: 21.2s
233:	learn: 1782.1025310	total: 6.47s	remaining: 21.2s
234:	learn: 1781.4405915	total: 6.5s	remaining: 21.2s
235:	learn: 1780.9843767	total: 6.53s	remaining: 21.1s
236:	learn: 1780.5731624	total: 6.55s	remaining: 21.1s
237:	learn: 1780.0191647	total: 6.58s	remaining: 21.1s
238:	learn: 1779.3620790	total: 6.61s	remaining: 21s
239:	learn: 1778.9800087	total: 6.63s	remaining: 21s
240:	learn: 1778.5255396	total: 6.67s	remaining: 21s
241:	learn: 1777.9634795	total: 6.69s	remaining: 21s
242:	learn: 1777.5835059	total: 6.71s	remaining: 20.9s
243:	learn: 1777.1596292	total: 6.74s	remaining: 20.9s
244:	learn: 1776.7976563	total: 6.77s	remaining: 20.9s
245:	learn: 1776.3479597	total: 6.79s	remaining: 20.8s
246:	learn: 1775.9115537	total: 6.82s	remaining: 20.8s
247:	learn: 1775.4388874	total: 6.85s	remaining: 20.8s
248:	learn: 1775.0228709	total: 6.87s	remaining: 20.7s
249:	learn: 1774.6464504	total: 6.9s	remaining: 20.7s
250:	learn: 1774.3154518	total: 6.92s	remaining: 20.7s
251:	learn: 1773.9476890	total: 6.95s	remaining: 20.6s
252:	learn: 1773.6462742	total: 6.98s	remaining: 20.6s
253:	learn: 1773.2479389	total: 7s	remaining: 20.6s
254:	learn: 1772.8650657	total: 7.02s	remaining: 20.5s
255:	learn: 1772.5674666	total: 7.06s	remaining: 20.5s
256:	learn: 1772.2338069	total: 7.08s	remaining: 20.5s
257:	learn: 1771.9012816	total: 7.11s	remaining: 20.4s
258:	learn: 1771.4734818	total: 7.13s	remaining: 20.4s
259:	learn: 1770.8970008	total: 7.16s	remaining: 20.4s
260:	learn: 1770.2556799	total: 7.18s	remaining: 20.3s
261:	learn: 1769.7584170	total: 7.21s	remaining: 20.3s
262:	learn: 1769.3680975	total: 7.24s	remaining: 20.3s
263:	learn: 1768.9075991	total: 7.28s	remaining: 20.3s
264:	learn: 1768.6080317	total: 7.31s	remaining: 20.3s
265:	learn: 1768.3378164	total: 7.34s	remaining: 20.3s
266:	learn: 1767.9806405	total: 7.37s	remaining: 20.2s
267:	learn: 1767.7095376	total: 7.39s	remaining: 20.2s
268:	learn: 1767.2393662	total: 7.42s	remaining: 20.2s
269:	learn: 1766.8669346	total: 7.45s	remaining: 20.1s
270:	learn: 1766.3543330	total: 7.47s	remaining: 20.1s
271:	learn: 1766.0684958	total: 7.5s	remaining: 20.1s
272:	learn: 1765.7424881	total: 7.52s	remaining: 20s
273:	learn: 1765.2965361	total: 7.55s	remaining: 20s
274:	learn: 1764.8385916	total: 7.57s	remaining: 20s
275:	learn: 1764.6104131	total: 7.59s	remaining: 19.9s
276:	learn: 1764.2147988	total: 7.63s	remaining: 19.9s
277:	learn: 1763.8849534	total: 7.65s	remaining: 19.9s
278:	learn: 1763.6093157	total: 7.68s	remaining: 19.8s
279:	learn: 1763.3217212	total: 7.71s	remaining: 19.8s
280:	learn: 1763.0541650	total: 7.75s	remaining: 19.8s
281:	learn: 1762.7089347	total: 7.77s	remaining: 19.8s
282:	learn: 1762.2759285	total: 7.81s	remaining: 19.8s
283:	learn: 1761.9672630	total: 7.84s	remaining: 19.8s
284:	learn: 1761.6514470	total: 7.87s	remaining: 19.8s
285:	learn: 1761.3743073	total: 7.9s	remaining: 19.7s
286:	learn: 1761.0459916	total: 7.93s	remaining: 19.7s
287:	learn: 1760.5625524	total: 7.96s	remaining: 19.7s
288:	learn: 1760.2742725	total: 7.99s	remaining: 19.7s
289:	learn: 1759.9626341	total: 8.02s	remaining: 19.6s
290:	learn: 1759.7559271	total: 8.05s	remaining: 19.6s
291:	learn: 1759.4960092	total: 8.08s	remaining: 19.6s
292:	learn: 1758.9642799	total: 8.11s	remaining: 19.6s
293:	learn: 1758.7006228	total: 8.14s	remaining: 19.5s
294:	learn: 1758.4881178	total: 8.16s	remaining: 19.5s
295:	learn: 1758.2277685	total: 8.2s	remaining: 19.5s
296:	learn: 1757.8294939	total: 8.23s	remaining: 19.5s
297:	learn: 1757.3568416	total: 8.25s	remaining: 19.4s
298:	learn: 1757.0153342	total: 8.28s	remaining: 19.4s
299:	learn: 1756.8772711	total: 8.31s	remaining: 19.4s
300:	learn: 1756.6136064	total: 8.33s	remaining: 19.4s
301:	learn: 1756.4068961	total: 8.36s	remaining: 19.3s
302:	learn: 1756.1395872	total: 8.39s	remaining: 19.3s
303:	learn: 1755.9052854	total: 8.41s	remaining: 19.3s
304:	learn: 1755.5612668	total: 8.44s	remaining: 19.2s
305:	learn: 1755.3670391	total: 8.47s	remaining: 19.2s
306:	learn: 1755.1512014	total: 8.5s	remaining: 19.2s
307:	learn: 1754.9033623	total: 8.53s	remaining: 19.2s
308:	learn: 1754.5511231	total: 8.55s	remaining: 19.1s
309:	learn: 1754.2037555	total: 8.58s	remaining: 19.1s
310:	learn: 1753.7069245	total: 8.61s	remaining: 19.1s
311:	learn: 1753.5299129	total: 8.63s	remaining: 19s
312:	learn: 1753.3049508	total: 8.66s	remaining: 19s
313:	learn: 1752.9848942	total: 8.69s	remaining: 19s
314:	learn: 1752.5752357	total: 8.71s	remaining: 18.9s
315:	learn: 1752.3617696	total: 8.73s	remaining: 18.9s
316:	learn: 1752.1624868	total: 8.76s	remaining: 18.9s
317:	learn: 1751.9985216	total: 8.79s	remaining: 18.9s
318:	learn: 1751.7211885	total: 8.81s	remaining: 18.8s
319:	learn: 1751.4509047	total: 8.84s	remaining: 18.8s
320:	learn: 1751.0279461	total: 8.87s	remaining: 18.8s
321:	learn: 1750.9292577	total: 8.9s	remaining: 18.7s
322:	learn: 1750.4836956	total: 8.92s	remaining: 18.7s
323:	learn: 1750.2786102	total: 8.94s	remaining: 18.7s
324:	learn: 1750.0253426	total: 8.97s	remaining: 18.6s
325:	learn: 1749.7014227	total: 9s	remaining: 18.6s
326:	learn: 1749.3579980	total: 9.02s	remaining: 18.6s
327:	learn: 1749.1449241	total: 9.05s	remaining: 18.5s
328:	learn: 1748.9527583	total: 9.09s	remaining: 18.5s
329:	learn: 1748.5723199	total: 9.11s	remaining: 18.5s
330:	learn: 1748.2552727	total: 9.14s	remaining: 18.5s
331:	learn: 1748.0239272	total: 9.17s	remaining: 18.4s
332:	learn: 1747.7783560	total: 9.19s	remaining: 18.4s
333:	learn: 1747.5235725	total: 9.21s	remaining: 18.4s
334:	learn: 1747.0924591	total: 9.24s	remaining: 18.3s
335:	learn: 1746.8859588	total: 9.27s	remaining: 18.3s
336:	learn: 1746.7162236	total: 9.29s	remaining: 18.3s
337:	learn: 1746.4385357	total: 9.32s	remaining: 18.3s
338:	learn: 1746.0759789	total: 9.35s	remaining: 18.2s
339:	learn: 1745.7795302	total: 9.38s	remaining: 18.2s
340:	learn: 1745.4609833	total: 9.4s	remaining: 18.2s
341:	learn: 1745.3033143	total: 9.43s	remaining: 18.1s
342:	learn: 1744.9151421	total: 9.46s	remaining: 18.1s
343:	learn: 1744.7039257	total: 9.49s	remaining: 18.1s
344:	learn: 1744.1836669	total: 9.51s	remaining: 18.1s
345:	learn: 1743.8780078	total: 9.54s	remaining: 18s
346:	learn: 1743.5843683	total: 9.57s	remaining: 18s
347:	learn: 1743.2884758	total: 9.59s	remaining: 18s
348:	learn: 1743.0767962	total: 9.62s	remaining: 17.9s
349:	learn: 1742.7512049	total: 9.66s	remaining: 17.9s
350:	learn: 1742.6366156	total: 9.68s	remaining: 17.9s
351:	learn: 1742.5068919	total: 9.7s	remaining: 17.9s
352:	learn: 1742.1471300	total: 9.73s	remaining: 17.8s
353:	learn: 1741.6437819	total: 9.76s	remaining: 17.8s
354:	learn: 1741.4263857	total: 9.79s	remaining: 17.8s
355:	learn: 1741.0281640	total: 9.81s	remaining: 17.8s
356:	learn: 1740.5947251	total: 9.85s	remaining: 17.7s
357:	learn: 1740.1266566	total: 9.88s	remaining: 17.7s
358:	learn: 1739.8108850	total: 9.9s	remaining: 17.7s
359:	learn: 1739.4114025	total: 9.93s	remaining: 17.7s
360:	learn: 1739.0978682	total: 9.96s	remaining: 17.6s
361:	learn: 1738.7615356	total: 9.98s	remaining: 17.6s
362:	learn: 1738.5360360	total: 10s	remaining: 17.6s
363:	learn: 1738.2583301	total: 10.1s	remaining: 17.6s
364:	learn: 1738.0745429	total: 10.1s	remaining: 17.5s
365:	learn: 1737.7328847	total: 10.1s	remaining: 17.5s
366:	learn: 1737.6112975	total: 10.1s	remaining: 17.5s
367:	learn: 1737.4764393	total: 10.2s	remaining: 17.4s
368:	learn: 1737.3503701	total: 10.2s	remaining: 17.4s
369:	learn: 1737.1171423	total: 10.2s	remaining: 17.4s
370:	learn: 1736.8809626	total: 10.2s	remaining: 17.3s
371:	learn: 1736.7180739	total: 10.3s	remaining: 17.3s
372:	learn: 1736.4859108	total: 10.3s	remaining: 17.3s
373:	learn: 1736.3103022	total: 10.3s	remaining: 17.2s
374:	learn: 1736.1366438	total: 10.3s	remaining: 17.2s
375:	learn: 1735.8144530	total: 10.4s	remaining: 17.2s
376:	learn: 1735.3876077	total: 10.4s	remaining: 17.1s
377:	learn: 1735.2836293	total: 10.4s	remaining: 17.1s
378:	learn: 1734.9374181	total: 10.4s	remaining: 17.1s
379:	learn: 1734.4593080	total: 10.5s	remaining: 17.1s
380:	learn: 1734.2978355	total: 10.5s	remaining: 17s
381:	learn: 1733.9003675	total: 10.5s	remaining: 17s
382:	learn: 1733.7659461	total: 10.5s	remaining: 17s
383:	learn: 1733.4965610	total: 10.6s	remaining: 16.9s
384:	learn: 1733.3631396	total: 10.6s	remaining: 16.9s
385:	learn: 1733.2221146	total: 10.6s	remaining: 16.9s
386:	learn: 1733.0819943	total: 10.6s	remaining: 16.9s
387:	learn: 1732.9293179	total: 10.7s	remaining: 16.8s
388:	learn: 1732.6974738	total: 10.7s	remaining: 16.8s
389:	learn: 1732.3199615	total: 10.7s	remaining: 16.8s
390:	learn: 1731.9007469	total: 10.8s	remaining: 16.8s
391:	learn: 1731.6555670	total: 10.8s	remaining: 16.7s
392:	learn: 1731.4969308	total: 10.8s	remaining: 16.7s
393:	learn: 1731.3130533	total: 10.8s	remaining: 16.7s
394:	learn: 1731.1478241	total: 10.9s	remaining: 16.6s
395:	learn: 1730.7968243	total: 10.9s	remaining: 16.6s
396:	learn: 1730.5189885	total: 10.9s	remaining: 16.6s
397:	learn: 1730.1432094	total: 10.9s	remaining: 16.6s
398:	learn: 1729.9289134	total: 11s	remaining: 16.5s
399:	learn: 1729.6498096	total: 11s	remaining: 16.5s
400:	learn: 1729.4930944	total: 11s	remaining: 16.5s
401:	learn: 1728.9555937	total: 11.1s	remaining: 16.5s
402:	learn: 1728.7575793	total: 11.1s	remaining: 16.4s
403:	learn: 1728.5043729	total: 11.1s	remaining: 16.4s
404:	learn: 1728.2045535	total: 11.1s	remaining: 16.4s
405:	learn: 1727.8305892	total: 11.2s	remaining: 16.3s
406:	learn: 1727.5135617	total: 11.2s	remaining: 16.3s
407:	learn: 1727.3962769	total: 11.2s	remaining: 16.3s
408:	learn: 1727.1988336	total: 11.3s	remaining: 16.3s
409:	learn: 1726.9806291	total: 11.3s	remaining: 16.2s
410:	learn: 1726.8093032	total: 11.3s	remaining: 16.2s
411:	learn: 1726.4934766	total: 11.3s	remaining: 16.2s
412:	learn: 1726.1975866	total: 11.4s	remaining: 16.2s
413:	learn: 1725.9244857	total: 11.4s	remaining: 16.1s
414:	learn: 1725.7148752	total: 11.4s	remaining: 16.1s
415:	learn: 1725.4719681	total: 11.5s	remaining: 16.1s
416:	learn: 1725.2299478	total: 11.5s	remaining: 16s
417:	learn: 1724.7925840	total: 11.5s	remaining: 16s
418:	learn: 1724.6719934	total: 11.5s	remaining: 16s
419:	learn: 1724.4931970	total: 11.6s	remaining: 16s
420:	learn: 1724.2689577	total: 11.6s	remaining: 15.9s
421:	learn: 1724.0641019	total: 11.6s	remaining: 15.9s
422:	learn: 1723.8415852	total: 11.6s	remaining: 15.9s
423:	learn: 1723.6341401	total: 11.7s	remaining: 15.9s
424:	learn: 1723.4085544	total: 11.7s	remaining: 15.8s
425:	learn: 1722.9245918	total: 11.7s	remaining: 15.8s
426:	learn: 1722.6787413	total: 11.8s	remaining: 15.8s
427:	learn: 1722.3815308	total: 11.8s	remaining: 15.8s
428:	learn: 1722.1348411	total: 11.8s	remaining: 15.7s
429:	learn: 1721.8787159	total: 11.8s	remaining: 15.7s
430:	learn: 1721.6300346	total: 11.9s	remaining: 15.7s
431:	learn: 1721.4944354	total: 11.9s	remaining: 15.6s
432:	learn: 1721.3482085	total: 11.9s	remaining: 15.6s
433:	learn: 1721.1085743	total: 11.9s	remaining: 15.6s
434:	learn: 1720.9446670	total: 12s	remaining: 15.5s
435:	learn: 1720.7707373	total: 12s	remaining: 15.5s
436:	learn: 1720.5052117	total: 12s	remaining: 15.5s
437:	learn: 1720.3804160	total: 12.1s	remaining: 15.5s
438:	learn: 1720.1800582	total: 12.1s	remaining: 15.4s
439:	learn: 1719.8706799	total: 12.1s	remaining: 15.4s
440:	learn: 1719.7073979	total: 12.1s	remaining: 15.4s
441:	learn: 1719.5420037	total: 12.2s	remaining: 15.3s
442:	learn: 1719.4227825	total: 12.2s	remaining: 15.3s
443:	learn: 1719.2770143	total: 12.2s	remaining: 15.3s
444:	learn: 1719.1109865	total: 12.2s	remaining: 15.3s
445:	learn: 1718.8693794	total: 12.3s	remaining: 15.2s
446:	learn: 1718.5897098	total: 12.3s	remaining: 15.2s
447:	learn: 1718.3365152	total: 12.3s	remaining: 15.2s
448:	learn: 1718.0371003	total: 12.3s	remaining: 15.1s
449:	learn: 1717.8990796	total: 12.4s	remaining: 15.1s
450:	learn: 1717.6102658	total: 12.4s	remaining: 15.1s
451:	learn: 1717.4586360	total: 12.4s	remaining: 15.1s
452:	learn: 1717.1594577	total: 12.5s	remaining: 15s
453:	learn: 1716.9329324	total: 12.5s	remaining: 15s
454:	learn: 1716.5932167	total: 12.5s	remaining: 15s
455:	learn: 1716.3710059	total: 12.5s	remaining: 14.9s
456:	learn: 1716.2233376	total: 12.6s	remaining: 14.9s
457:	learn: 1716.0142021	total: 12.6s	remaining: 14.9s
458:	learn: 1715.6628533	total: 12.6s	remaining: 14.9s
459:	learn: 1715.4418455	total: 12.6s	remaining: 14.8s
460:	learn: 1715.0968546	total: 12.7s	remaining: 14.8s
461:	learn: 1714.9546329	total: 12.7s	remaining: 14.8s
462:	learn: 1714.6543210	total: 12.7s	remaining: 14.8s
463:	learn: 1714.3815694	total: 12.8s	remaining: 14.7s
464:	learn: 1714.0692509	total: 12.8s	remaining: 14.7s
465:	learn: 1713.8464687	total: 12.8s	remaining: 14.7s
466:	learn: 1713.6821941	total: 12.8s	remaining: 14.6s
467:	learn: 1713.3773955	total: 12.9s	remaining: 14.6s
468:	learn: 1713.0548536	total: 12.9s	remaining: 14.6s
469:	learn: 1712.8247296	total: 12.9s	remaining: 14.6s
470:	learn: 1712.6117092	total: 12.9s	remaining: 14.5s
471:	learn: 1712.4307772	total: 13s	remaining: 14.5s
472:	learn: 1712.1721127	total: 13s	remaining: 14.5s
473:	learn: 1712.0492464	total: 13s	remaining: 14.4s
474:	learn: 1711.8558267	total: 13s	remaining: 14.4s
475:	learn: 1711.6646075	total: 13.1s	remaining: 14.4s
476:	learn: 1711.5512330	total: 13.1s	remaining: 14.3s
477:	learn: 1711.3264689	total: 13.1s	remaining: 14.3s
478:	learn: 1711.0536369	total: 13.1s	remaining: 14.3s
479:	learn: 1710.8733505	total: 13.2s	remaining: 14.3s
480:	learn: 1710.7170419	total: 13.2s	remaining: 14.2s
481:	learn: 1710.5616064	total: 13.2s	remaining: 14.2s
482:	learn: 1710.2800168	total: 13.3s	remaining: 14.2s
483:	learn: 1710.2192052	total: 13.3s	remaining: 14.2s
484:	learn: 1710.1138949	total: 13.3s	remaining: 14.1s
485:	learn: 1709.9354447	total: 13.3s	remaining: 14.1s
486:	learn: 1709.5957803	total: 13.4s	remaining: 14.1s
487:	learn: 1709.5334700	total: 13.4s	remaining: 14s
488:	learn: 1709.2605878	total: 13.4s	remaining: 14s
489:	learn: 1709.0422134	total: 13.4s	remaining: 14s
490:	learn: 1708.8949075	total: 13.5s	remaining: 14s
491:	learn: 1708.7711117	total: 13.5s	remaining: 13.9s
492:	learn: 1708.5042672	total: 13.5s	remaining: 13.9s
493:	learn: 1708.2595509	total: 13.5s	remaining: 13.9s
494:	learn: 1707.8056889	total: 13.6s	remaining: 13.8s
495:	learn: 1707.6423206	total: 13.6s	remaining: 13.8s
496:	learn: 1707.4865548	total: 13.6s	remaining: 13.8s
497:	learn: 1707.1568491	total: 13.7s	remaining: 13.8s
498:	learn: 1707.0241469	total: 13.7s	remaining: 13.7s
499:	learn: 1706.6301897	total: 13.7s	remaining: 13.7s
500:	learn: 1706.2981094	total: 13.7s	remaining: 13.7s
501:	learn: 1706.0470225	total: 13.8s	remaining: 13.7s
502:	learn: 1705.8845399	total: 13.8s	remaining: 13.6s
503:	learn: 1705.7685534	total: 13.8s	remaining: 13.6s
504:	learn: 1705.4282936	total: 13.8s	remaining: 13.6s
505:	learn: 1705.2222597	total: 13.9s	remaining: 13.5s
506:	learn: 1705.0372368	total: 13.9s	remaining: 13.5s
507:	learn: 1704.7586434	total: 13.9s	remaining: 13.5s
508:	learn: 1704.5042917	total: 14s	remaining: 13.5s
509:	learn: 1704.2855451	total: 14s	remaining: 13.4s
510:	learn: 1704.1428920	total: 14s	remaining: 13.4s
511:	learn: 1703.8975433	total: 14s	remaining: 13.4s
512:	learn: 1703.7575624	total: 14.1s	remaining: 13.3s
513:	learn: 1703.7036885	total: 14.1s	remaining: 13.3s
514:	learn: 1703.4694841	total: 14.1s	remaining: 13.3s
515:	learn: 1703.2374962	total: 14.1s	remaining: 13.3s
516:	learn: 1703.0022965	total: 14.2s	remaining: 13.2s
517:	learn: 1702.7031413	total: 14.2s	remaining: 13.2s
518:	learn: 1702.5767344	total: 14.2s	remaining: 13.2s
519:	learn: 1702.3903004	total: 14.2s	remaining: 13.1s
520:	learn: 1702.2317931	total: 14.3s	remaining: 13.1s
521:	learn: 1702.1184789	total: 14.3s	remaining: 13.1s
522:	learn: 1701.8932253	total: 14.3s	remaining: 13.1s
523:	learn: 1701.7559379	total: 14.3s	remaining: 13s
524:	learn: 1701.6124239	total: 14.4s	remaining: 13s
525:	learn: 1701.4700053	total: 14.4s	remaining: 13s
526:	learn: 1701.2880035	total: 14.4s	remaining: 12.9s
527:	learn: 1701.0779707	total: 14.5s	remaining: 12.9s
528:	learn: 1700.8311593	total: 14.5s	remaining: 12.9s
529:	learn: 1700.6975137	total: 14.5s	remaining: 12.9s
530:	learn: 1700.4380575	total: 14.5s	remaining: 12.8s
531:	learn: 1700.1624460	total: 14.6s	remaining: 12.8s
532:	learn: 1699.9509639	total: 14.6s	remaining: 12.8s
533:	learn: 1699.7413970	total: 14.6s	remaining: 12.8s
534:	learn: 1699.4896568	total: 14.6s	remaining: 12.7s
535:	learn: 1699.2330325	total: 14.7s	remaining: 12.7s
536:	learn: 1699.0548749	total: 14.7s	remaining: 12.7s
537:	learn: 1698.8959378	total: 14.7s	remaining: 12.7s
538:	learn: 1698.5298020	total: 14.8s	remaining: 12.6s
539:	learn: 1698.3905353	total: 14.8s	remaining: 12.6s
540:	learn: 1698.2555344	total: 14.8s	remaining: 12.6s
541:	learn: 1698.0311315	total: 14.9s	remaining: 12.6s
542:	learn: 1697.8976375	total: 14.9s	remaining: 12.5s
543:	learn: 1697.6364774	total: 14.9s	remaining: 12.5s
544:	learn: 1697.4588254	total: 14.9s	remaining: 12.5s
545:	learn: 1697.2500396	total: 15s	remaining: 12.4s
546:	learn: 1697.1403952	total: 15s	remaining: 12.4s
547:	learn: 1696.9579471	total: 15s	remaining: 12.4s
548:	learn: 1696.7916390	total: 15s	remaining: 12.4s
549:	learn: 1696.6695560	total: 15.1s	remaining: 12.3s
550:	learn: 1696.5002606	total: 15.1s	remaining: 12.3s
551:	learn: 1696.2396394	total: 15.1s	remaining: 12.3s
552:	learn: 1696.1276240	total: 15.1s	remaining: 12.2s
553:	learn: 1695.8474132	total: 15.2s	remaining: 12.2s
554:	learn: 1695.6476378	total: 15.2s	remaining: 12.2s
555:	learn: 1695.4936487	total: 15.2s	remaining: 12.2s
556:	learn: 1695.3517891	total: 15.3s	remaining: 12.1s
557:	learn: 1695.1084149	total: 15.3s	remaining: 12.1s
558:	learn: 1694.8433818	total: 15.3s	remaining: 12.1s
559:	learn: 1694.7256051	total: 15.3s	remaining: 12.1s
560:	learn: 1694.5787690	total: 15.4s	remaining: 12s
561:	learn: 1694.3435862	total: 15.4s	remaining: 12s
562:	learn: 1694.1094830	total: 15.4s	remaining: 12s
563:	learn: 1693.9540094	total: 15.4s	remaining: 11.9s
564:	learn: 1693.8760746	total: 15.5s	remaining: 11.9s
565:	learn: 1693.6528482	total: 15.5s	remaining: 11.9s
566:	learn: 1693.5373027	total: 15.5s	remaining: 11.9s
567:	learn: 1693.2885963	total: 15.6s	remaining: 11.8s
568:	learn: 1693.1515071	total: 15.6s	remaining: 11.8s
569:	learn: 1693.0181247	total: 15.6s	remaining: 11.8s
570:	learn: 1692.7950900	total: 15.7s	remaining: 11.8s
571:	learn: 1692.5733039	total: 15.7s	remaining: 11.7s
572:	learn: 1692.4804245	total: 15.7s	remaining: 11.7s
573:	learn: 1692.4119178	total: 15.7s	remaining: 11.7s
574:	learn: 1692.2669883	total: 15.8s	remaining: 11.7s
575:	learn: 1692.1416000	total: 15.8s	remaining: 11.6s
576:	learn: 1692.0240794	total: 15.8s	remaining: 11.6s
577:	learn: 1691.8315820	total: 15.8s	remaining: 11.6s
578:	learn: 1691.6072952	total: 15.9s	remaining: 11.5s
579:	learn: 1691.4590553	total: 15.9s	remaining: 11.5s
580:	learn: 1691.1516759	total: 15.9s	remaining: 11.5s
581:	learn: 1690.8737645	total: 16s	remaining: 11.5s
582:	learn: 1690.7560860	total: 16s	remaining: 11.4s
583:	learn: 1690.5966426	total: 16s	remaining: 11.4s
584:	learn: 1690.4559600	total: 16.1s	remaining: 11.4s
585:	learn: 1690.3298858	total: 16.1s	remaining: 11.4s
586:	learn: 1690.1355632	total: 16.1s	remaining: 11.3s
587:	learn: 1689.8833008	total: 16.1s	remaining: 11.3s
588:	learn: 1689.7284970	total: 16.2s	remaining: 11.3s
589:	learn: 1689.5454680	total: 16.2s	remaining: 11.2s
590:	learn: 1689.2156067	total: 16.2s	remaining: 11.2s
591:	learn: 1689.0134942	total: 16.2s	remaining: 11.2s
592:	learn: 1688.8437005	total: 16.3s	remaining: 11.2s
593:	learn: 1688.7195367	total: 16.3s	remaining: 11.1s
594:	learn: 1688.5573363	total: 16.3s	remaining: 11.1s
595:	learn: 1688.4107246	total: 16.3s	remaining: 11.1s
596:	learn: 1688.3281715	total: 16.4s	remaining: 11s
597:	learn: 1688.1009582	total: 16.4s	remaining: 11s
598:	learn: 1687.9608896	total: 16.4s	remaining: 11s
599:	learn: 1687.8577688	total: 16.4s	remaining: 11s
600:	learn: 1687.5200751	total: 16.5s	remaining: 10.9s
601:	learn: 1687.2822329	total: 16.5s	remaining: 10.9s
602:	learn: 1687.0960702	total: 16.5s	remaining: 10.9s
603:	learn: 1686.7856218	total: 16.6s	remaining: 10.9s
604:	learn: 1686.6349506	total: 16.6s	remaining: 10.8s
605:	learn: 1686.4410318	total: 16.6s	remaining: 10.8s
606:	learn: 1686.2935853	total: 16.6s	remaining: 10.8s
607:	learn: 1686.2228289	total: 16.7s	remaining: 10.7s
608:	learn: 1686.0449881	total: 16.7s	remaining: 10.7s
609:	learn: 1685.7982189	total: 16.7s	remaining: 10.7s
610:	learn: 1685.7091904	total: 16.7s	remaining: 10.7s
611:	learn: 1685.5520996	total: 16.8s	remaining: 10.6s
612:	learn: 1685.3777480	total: 16.8s	remaining: 10.6s
613:	learn: 1685.2366875	total: 16.8s	remaining: 10.6s
614:	learn: 1685.1310719	total: 16.9s	remaining: 10.5s
615:	learn: 1685.0988721	total: 16.9s	remaining: 10.5s
616:	learn: 1684.9226714	total: 16.9s	remaining: 10.5s
617:	learn: 1684.8289087	total: 16.9s	remaining: 10.5s
618:	learn: 1684.6689438	total: 17s	remaining: 10.4s
619:	learn: 1684.5508836	total: 17s	remaining: 10.4s
620:	learn: 1684.3876378	total: 17s	remaining: 10.4s
621:	learn: 1684.3305164	total: 17s	remaining: 10.4s
622:	learn: 1684.2468376	total: 17.1s	remaining: 10.3s
623:	learn: 1684.1157999	total: 17.1s	remaining: 10.3s
624:	learn: 1683.9919746	total: 17.1s	remaining: 10.3s
625:	learn: 1683.7561116	total: 17.2s	remaining: 10.3s
626:	learn: 1683.5713000	total: 17.2s	remaining: 10.2s
627:	learn: 1683.4830475	total: 17.2s	remaining: 10.2s
628:	learn: 1683.3615383	total: 17.3s	remaining: 10.2s
629:	learn: 1683.1307478	total: 17.3s	remaining: 10.2s
630:	learn: 1682.9412419	total: 17.3s	remaining: 10.1s
631:	learn: 1682.8849423	total: 17.4s	remaining: 10.1s
632:	learn: 1682.6802639	total: 17.4s	remaining: 10.1s
633:	learn: 1682.5842850	total: 17.4s	remaining: 10.1s
634:	learn: 1682.3742238	total: 17.4s	remaining: 10s
635:	learn: 1682.2336521	total: 17.5s	remaining: 9.99s
636:	learn: 1682.0595777	total: 17.5s	remaining: 9.97s
637:	learn: 1681.8801109	total: 17.5s	remaining: 9.94s
638:	learn: 1681.7568804	total: 17.6s	remaining: 9.91s
639:	learn: 1681.5689587	total: 17.6s	remaining: 9.89s
640:	learn: 1681.4705107	total: 17.6s	remaining: 9.87s
641:	learn: 1681.2873998	total: 17.6s	remaining: 9.84s
642:	learn: 1681.1822551	total: 17.7s	remaining: 9.81s
643:	learn: 1680.9026289	total: 17.7s	remaining: 9.79s
644:	learn: 1680.6493307	total: 17.7s	remaining: 9.76s
645:	learn: 1680.4461423	total: 17.8s	remaining: 9.73s
646:	learn: 1680.2984576	total: 17.8s	remaining: 9.7s
647:	learn: 1680.1198124	total: 17.8s	remaining: 9.68s
648:	learn: 1679.8931810	total: 17.9s	remaining: 9.66s
649:	learn: 1679.7707394	total: 17.9s	remaining: 9.63s
650:	learn: 1679.6267549	total: 17.9s	remaining: 9.61s
651:	learn: 1679.3254850	total: 18s	remaining: 9.58s
652:	learn: 1679.1974012	total: 18s	remaining: 9.56s
653:	learn: 1679.0239951	total: 18s	remaining: 9.54s
654:	learn: 1678.9140096	total: 18.1s	remaining: 9.51s
655:	learn: 1678.8305686	total: 18.1s	remaining: 9.48s
656:	learn: 1678.7207316	total: 18.1s	remaining: 9.46s
657:	learn: 1678.5385758	total: 18.1s	remaining: 9.43s
658:	learn: 1678.3041871	total: 18.2s	remaining: 9.4s
659:	learn: 1678.1954577	total: 18.2s	remaining: 9.38s
660:	learn: 1677.9739292	total: 18.2s	remaining: 9.35s
661:	learn: 1677.7965822	total: 18.3s	remaining: 9.32s
662:	learn: 1677.5953691	total: 18.3s	remaining: 9.29s
663:	learn: 1677.3802865	total: 18.3s	remaining: 9.27s
664:	learn: 1677.2096908	total: 18.3s	remaining: 9.24s
665:	learn: 1677.0288698	total: 18.4s	remaining: 9.21s
666:	learn: 1676.8431646	total: 18.4s	remaining: 9.19s
667:	learn: 1676.6366753	total: 18.4s	remaining: 9.16s
668:	learn: 1676.5794085	total: 18.5s	remaining: 9.14s
669:	learn: 1676.3746594	total: 18.5s	remaining: 9.12s
670:	learn: 1676.0512965	total: 18.5s	remaining: 9.09s
671:	learn: 1675.7797137	total: 18.6s	remaining: 9.06s
672:	learn: 1675.6533602	total: 18.6s	remaining: 9.04s
673:	learn: 1675.5334040	total: 18.6s	remaining: 9.01s
674:	learn: 1675.2710052	total: 18.6s	remaining: 8.98s
675:	learn: 1675.0194513	total: 18.7s	remaining: 8.96s
676:	learn: 1674.8800185	total: 18.7s	remaining: 8.93s
677:	learn: 1674.8142915	total: 18.7s	remaining: 8.9s
678:	learn: 1674.6339854	total: 18.8s	remaining: 8.87s
679:	learn: 1674.4911402	total: 18.8s	remaining: 8.84s
680:	learn: 1674.3170866	total: 18.8s	remaining: 8.81s
681:	learn: 1674.0560732	total: 18.8s	remaining: 8.79s
682:	learn: 1673.9112251	total: 18.9s	remaining: 8.76s
683:	learn: 1673.7637032	total: 18.9s	remaining: 8.73s
684:	learn: 1673.6548363	total: 18.9s	remaining: 8.71s
685:	learn: 1673.4307968	total: 19s	remaining: 8.68s
686:	learn: 1673.2063655	total: 19s	remaining: 8.65s
687:	learn: 1673.0367300	total: 19s	remaining: 8.62s
688:	learn: 1672.8713723	total: 19s	remaining: 8.6s
689:	learn: 1672.6300316	total: 19.1s	remaining: 8.57s
690:	learn: 1672.4909263	total: 19.1s	remaining: 8.54s
691:	learn: 1672.3133694	total: 19.1s	remaining: 8.52s
692:	learn: 1672.1313333	total: 19.2s	remaining: 8.49s
693:	learn: 1671.9207119	total: 19.2s	remaining: 8.46s
694:	learn: 1671.7249301	total: 19.2s	remaining: 8.43s
695:	learn: 1671.5971571	total: 19.2s	remaining: 8.4s
696:	learn: 1671.3775701	total: 19.3s	remaining: 8.38s
697:	learn: 1671.2901656	total: 19.3s	remaining: 8.35s
698:	learn: 1671.1367672	total: 19.3s	remaining: 8.32s
699:	learn: 1670.9656461	total: 19.3s	remaining: 8.29s
700:	learn: 1670.8891016	total: 19.4s	remaining: 8.26s
701:	learn: 1670.6723672	total: 19.4s	remaining: 8.23s
702:	learn: 1670.5779713	total: 19.4s	remaining: 8.2s
703:	learn: 1670.4322729	total: 19.4s	remaining: 8.18s
704:	learn: 1670.1806673	total: 19.5s	remaining: 8.15s
705:	learn: 1670.0627684	total: 19.5s	remaining: 8.12s
706:	learn: 1669.8170364	total: 19.5s	remaining: 8.09s
707:	learn: 1669.6580650	total: 19.6s	remaining: 8.07s
708:	learn: 1669.4437637	total: 19.6s	remaining: 8.04s
709:	learn: 1669.2550748	total: 19.6s	remaining: 8.01s
710:	learn: 1669.1728310	total: 19.6s	remaining: 7.99s
711:	learn: 1669.0205887	total: 19.7s	remaining: 7.96s
712:	learn: 1668.9201375	total: 19.7s	remaining: 7.93s
713:	learn: 1668.7013401	total: 19.7s	remaining: 7.9s
714:	learn: 1668.5867672	total: 19.8s	remaining: 7.88s
715:	learn: 1668.3688200	total: 19.8s	remaining: 7.85s
716:	learn: 1668.2524646	total: 19.8s	remaining: 7.82s
717:	learn: 1668.0729096	total: 19.9s	remaining: 7.8s
718:	learn: 1667.9625170	total: 19.9s	remaining: 7.77s
719:	learn: 1667.8595053	total: 19.9s	remaining: 7.74s
720:	learn: 1667.7738299	total: 19.9s	remaining: 7.71s
721:	learn: 1667.6051917	total: 20s	remaining: 7.68s
722:	learn: 1667.4650996	total: 20s	remaining: 7.65s
723:	learn: 1667.3163328	total: 20s	remaining: 7.63s
724:	learn: 1667.2470813	total: 20s	remaining: 7.6s
725:	learn: 1667.0914945	total: 20.1s	remaining: 7.57s
726:	learn: 1666.9199299	total: 20.1s	remaining: 7.54s
727:	learn: 1666.8176568	total: 20.1s	remaining: 7.51s
728:	learn: 1666.6276974	total: 20.1s	remaining: 7.49s
729:	learn: 1666.5015264	total: 20.2s	remaining: 7.46s
730:	learn: 1666.4187297	total: 20.2s	remaining: 7.43s
731:	learn: 1666.2654086	total: 20.2s	remaining: 7.41s
732:	learn: 1666.1420780	total: 20.3s	remaining: 7.38s
733:	learn: 1666.0062577	total: 20.3s	remaining: 7.35s
734:	learn: 1665.8469857	total: 20.3s	remaining: 7.32s
735:	learn: 1665.6593665	total: 20.3s	remaining: 7.3s
736:	learn: 1665.5970291	total: 20.4s	remaining: 7.27s
737:	learn: 1665.3744995	total: 20.4s	remaining: 7.24s
738:	learn: 1665.2416914	total: 20.4s	remaining: 7.21s
739:	learn: 1665.1109316	total: 20.4s	remaining: 7.18s
740:	learn: 1664.9651241	total: 20.5s	remaining: 7.16s
741:	learn: 1664.7929602	total: 20.5s	remaining: 7.13s
742:	learn: 1664.5325161	total: 20.5s	remaining: 7.1s
743:	learn: 1664.3917870	total: 20.6s	remaining: 7.07s
744:	learn: 1664.2829849	total: 20.6s	remaining: 7.04s
745:	learn: 1664.1171401	total: 20.6s	remaining: 7.02s
746:	learn: 1664.0476245	total: 20.6s	remaining: 6.99s
747:	learn: 1663.9245268	total: 20.7s	remaining: 6.96s
748:	learn: 1663.8297673	total: 20.7s	remaining: 6.93s
749:	learn: 1663.6976534	total: 20.7s	remaining: 6.91s
750:	learn: 1663.6375726	total: 20.7s	remaining: 6.88s
751:	learn: 1663.4301196	total: 20.8s	remaining: 6.85s
752:	learn: 1663.2345638	total: 20.8s	remaining: 6.83s
753:	learn: 1662.9302072	total: 20.8s	remaining: 6.8s
754:	learn: 1662.7882007	total: 20.9s	remaining: 6.77s
755:	learn: 1662.6955447	total: 20.9s	remaining: 6.74s
756:	learn: 1662.6065034	total: 20.9s	remaining: 6.71s
757:	learn: 1662.4424117	total: 20.9s	remaining: 6.69s
758:	learn: 1662.2531657	total: 21s	remaining: 6.66s
759:	learn: 1662.1316634	total: 21s	remaining: 6.63s
760:	learn: 1661.9127944	total: 21s	remaining: 6.61s
761:	learn: 1661.8231598	total: 21.1s	remaining: 6.58s
762:	learn: 1661.7084338	total: 21.1s	remaining: 6.55s
763:	learn: 1661.6068094	total: 21.1s	remaining: 6.52s
764:	learn: 1661.4914161	total: 21.1s	remaining: 6.49s
765:	learn: 1661.3200825	total: 21.2s	remaining: 6.47s
766:	learn: 1661.2047310	total: 21.2s	remaining: 6.44s
767:	learn: 1661.1332760	total: 21.2s	remaining: 6.41s
768:	learn: 1660.9643193	total: 21.3s	remaining: 6.38s
769:	learn: 1660.8191334	total: 21.3s	remaining: 6.36s
770:	learn: 1660.6737604	total: 21.3s	remaining: 6.33s
771:	learn: 1660.5246274	total: 21.3s	remaining: 6.3s
772:	learn: 1660.2982657	total: 21.4s	remaining: 6.28s
773:	learn: 1660.1943779	total: 21.4s	remaining: 6.25s
774:	learn: 1660.0816458	total: 21.4s	remaining: 6.22s
775:	learn: 1659.9430763	total: 21.5s	remaining: 6.19s
776:	learn: 1659.9039053	total: 21.5s	remaining: 6.17s
777:	learn: 1659.7968199	total: 21.5s	remaining: 6.14s
778:	learn: 1659.6647130	total: 21.5s	remaining: 6.11s
779:	learn: 1659.5540529	total: 21.6s	remaining: 6.09s
780:	learn: 1659.3996019	total: 21.6s	remaining: 6.06s
781:	learn: 1659.2496673	total: 21.6s	remaining: 6.03s
782:	learn: 1659.1224272	total: 21.7s	remaining: 6s
783:	learn: 1659.0496452	total: 21.7s	remaining: 5.97s
784:	learn: 1658.9036520	total: 21.7s	remaining: 5.95s
785:	learn: 1658.7804719	total: 21.7s	remaining: 5.92s
786:	learn: 1658.7188365	total: 21.8s	remaining: 5.89s
787:	learn: 1658.6161822	total: 21.8s	remaining: 5.86s
788:	learn: 1658.4895529	total: 21.8s	remaining: 5.83s
789:	learn: 1658.3560260	total: 21.9s	remaining: 5.81s
790:	learn: 1658.1837874	total: 21.9s	remaining: 5.78s
791:	learn: 1658.1047749	total: 21.9s	remaining: 5.75s
792:	learn: 1658.0000359	total: 21.9s	remaining: 5.72s
793:	learn: 1657.8923309	total: 22s	remaining: 5.7s
794:	learn: 1657.8038734	total: 22s	remaining: 5.67s
795:	learn: 1657.5932615	total: 22s	remaining: 5.64s
796:	learn: 1657.4300421	total: 22s	remaining: 5.61s
797:	learn: 1657.3371918	total: 22.1s	remaining: 5.59s
798:	learn: 1657.2069993	total: 22.1s	remaining: 5.56s
799:	learn: 1657.0881001	total: 22.1s	remaining: 5.53s
800:	learn: 1657.0296140	total: 22.1s	remaining: 5.5s
801:	learn: 1656.9381946	total: 22.2s	remaining: 5.47s
802:	learn: 1656.7567154	total: 22.2s	remaining: 5.45s
803:	learn: 1656.6198554	total: 22.2s	remaining: 5.42s
804:	learn: 1656.3997435	total: 22.3s	remaining: 5.39s
805:	learn: 1656.1468982	total: 22.3s	remaining: 5.36s
806:	learn: 1656.0449258	total: 22.3s	remaining: 5.33s
807:	learn: 1655.8998343	total: 22.3s	remaining: 5.31s
808:	learn: 1655.7148400	total: 22.4s	remaining: 5.28s
809:	learn: 1655.6289753	total: 22.4s	remaining: 5.25s
810:	learn: 1655.4964626	total: 22.4s	remaining: 5.23s
811:	learn: 1655.3495571	total: 22.5s	remaining: 5.2s
812:	learn: 1655.1950292	total: 22.5s	remaining: 5.17s
813:	learn: 1654.9980766	total: 22.5s	remaining: 5.15s
814:	learn: 1654.8514268	total: 22.6s	remaining: 5.12s
815:	learn: 1654.7548152	total: 22.6s	remaining: 5.09s
816:	learn: 1654.6174823	total: 22.6s	remaining: 5.06s
817:	learn: 1654.5331550	total: 22.6s	remaining: 5.04s
818:	learn: 1654.4696978	total: 22.7s	remaining: 5.01s
819:	learn: 1654.3845788	total: 22.7s	remaining: 4.98s
820:	learn: 1654.2434294	total: 22.7s	remaining: 4.96s
821:	learn: 1654.1933862	total: 22.8s	remaining: 4.93s
822:	learn: 1654.0814052	total: 22.8s	remaining: 4.9s
823:	learn: 1653.9664808	total: 22.8s	remaining: 4.87s
824:	learn: 1653.8682966	total: 22.8s	remaining: 4.84s
825:	learn: 1653.7491340	total: 22.9s	remaining: 4.82s
826:	learn: 1653.6012842	total: 22.9s	remaining: 4.79s
827:	learn: 1653.4905959	total: 22.9s	remaining: 4.76s
828:	learn: 1653.3594391	total: 23s	remaining: 4.73s
829:	learn: 1653.3144348	total: 23s	remaining: 4.71s
830:	learn: 1653.1509595	total: 23s	remaining: 4.68s
831:	learn: 1653.0735575	total: 23s	remaining: 4.65s
832:	learn: 1652.9276219	total: 23.1s	remaining: 4.62s
833:	learn: 1652.7223869	total: 23.1s	remaining: 4.6s
834:	learn: 1652.5774761	total: 23.1s	remaining: 4.57s
835:	learn: 1652.4616236	total: 23.2s	remaining: 4.54s
836:	learn: 1652.3764327	total: 23.2s	remaining: 4.51s
837:	learn: 1652.3068810	total: 23.2s	remaining: 4.49s
838:	learn: 1652.1851521	total: 23.2s	remaining: 4.46s
839:	learn: 1652.0673676	total: 23.3s	remaining: 4.43s
840:	learn: 1651.9185240	total: 23.3s	remaining: 4.4s
841:	learn: 1651.7867096	total: 23.3s	remaining: 4.38s
842:	learn: 1651.6972004	total: 23.4s	remaining: 4.35s
843:	learn: 1651.5756958	total: 23.4s	remaining: 4.32s
844:	learn: 1651.4048558	total: 23.4s	remaining: 4.29s
845:	learn: 1651.2377065	total: 23.4s	remaining: 4.26s
846:	learn: 1651.1967564	total: 23.5s	remaining: 4.24s
847:	learn: 1651.1120873	total: 23.5s	remaining: 4.21s
848:	learn: 1650.9904963	total: 23.5s	remaining: 4.18s
849:	learn: 1650.8445709	total: 23.5s	remaining: 4.15s
850:	learn: 1650.7028655	total: 23.6s	remaining: 4.13s
851:	learn: 1650.5555192	total: 23.6s	remaining: 4.1s
852:	learn: 1650.4953875	total: 23.6s	remaining: 4.07s
853:	learn: 1650.3645091	total: 23.6s	remaining: 4.04s
854:	learn: 1650.2207563	total: 23.7s	remaining: 4.01s
855:	learn: 1650.0211246	total: 23.7s	remaining: 3.99s
856:	learn: 1649.9059174	total: 23.7s	remaining: 3.96s
857:	learn: 1649.7954363	total: 23.8s	remaining: 3.93s
858:	learn: 1649.6839780	total: 23.8s	remaining: 3.9s
859:	learn: 1649.5813628	total: 23.8s	remaining: 3.88s
860:	learn: 1649.4592609	total: 23.8s	remaining: 3.85s
861:	learn: 1649.3487263	total: 23.9s	remaining: 3.82s
862:	learn: 1649.2411005	total: 23.9s	remaining: 3.79s
863:	learn: 1649.0642997	total: 23.9s	remaining: 3.77s
864:	learn: 1648.9602855	total: 23.9s	remaining: 3.74s
865:	learn: 1648.7832465	total: 24s	remaining: 3.71s
866:	learn: 1648.6824371	total: 24s	remaining: 3.68s
867:	learn: 1648.5702472	total: 24s	remaining: 3.65s
868:	learn: 1648.4464980	total: 24.1s	remaining: 3.63s
869:	learn: 1648.3673383	total: 24.1s	remaining: 3.6s
870:	learn: 1648.2566701	total: 24.1s	remaining: 3.57s
871:	learn: 1648.0447772	total: 24.1s	remaining: 3.54s
872:	learn: 1647.9539352	total: 24.2s	remaining: 3.52s
873:	learn: 1647.8747995	total: 24.2s	remaining: 3.49s
874:	learn: 1647.7535333	total: 24.2s	remaining: 3.46s
875:	learn: 1647.5982134	total: 24.2s	remaining: 3.43s
876:	learn: 1647.4565125	total: 24.3s	remaining: 3.4s
877:	learn: 1647.3620061	total: 24.3s	remaining: 3.38s
878:	learn: 1647.2474307	total: 24.3s	remaining: 3.35s
879:	learn: 1647.0649321	total: 24.4s	remaining: 3.32s
880:	learn: 1647.0025619	total: 24.4s	remaining: 3.29s
881:	learn: 1646.8819560	total: 24.4s	remaining: 3.27s
882:	learn: 1646.7740890	total: 24.4s	remaining: 3.24s
883:	learn: 1646.7232520	total: 24.5s	remaining: 3.21s
884:	learn: 1646.6137503	total: 24.5s	remaining: 3.18s
885:	learn: 1646.5218245	total: 24.5s	remaining: 3.15s
886:	learn: 1646.3686811	total: 24.5s	remaining: 3.13s
887:	learn: 1646.2534959	total: 24.6s	remaining: 3.1s
888:	learn: 1646.0942544	total: 24.6s	remaining: 3.07s
889:	learn: 1646.0092940	total: 24.6s	remaining: 3.04s
890:	learn: 1645.8963441	total: 24.7s	remaining: 3.02s
891:	learn: 1645.8181436	total: 24.7s	remaining: 2.99s
892:	learn: 1645.7441894	total: 24.7s	remaining: 2.96s
893:	learn: 1645.6768383	total: 24.7s	remaining: 2.93s
894:	learn: 1645.5531437	total: 24.8s	remaining: 2.9s
895:	learn: 1645.4198543	total: 24.8s	remaining: 2.88s
896:	learn: 1645.2832337	total: 24.8s	remaining: 2.85s
897:	learn: 1645.1688441	total: 24.8s	remaining: 2.82s
898:	learn: 1645.0560755	total: 24.9s	remaining: 2.79s
899:	learn: 1644.8688373	total: 24.9s	remaining: 2.77s
900:	learn: 1644.7249891	total: 24.9s	remaining: 2.74s
901:	learn: 1644.6332902	total: 25s	remaining: 2.71s
902:	learn: 1644.5020692	total: 25s	remaining: 2.68s
903:	learn: 1644.4192215	total: 25s	remaining: 2.65s
904:	learn: 1644.2567349	total: 25s	remaining: 2.63s
905:	learn: 1644.1269045	total: 25.1s	remaining: 2.6s
906:	learn: 1644.0442046	total: 25.1s	remaining: 2.57s
907:	learn: 1643.8894275	total: 25.1s	remaining: 2.54s
908:	learn: 1643.8297119	total: 25.2s	remaining: 2.52s
909:	learn: 1643.6913590	total: 25.2s	remaining: 2.49s
910:	learn: 1643.5227169	total: 25.2s	remaining: 2.46s
911:	learn: 1643.4071328	total: 25.2s	remaining: 2.44s
912:	learn: 1643.2995627	total: 25.3s	remaining: 2.41s
913:	learn: 1643.1688928	total: 25.3s	remaining: 2.38s
914:	learn: 1642.9227816	total: 25.3s	remaining: 2.35s
915:	learn: 1642.8110497	total: 25.4s	remaining: 2.33s
916:	learn: 1642.6822289	total: 25.4s	remaining: 2.3s
917:	learn: 1642.5779032	total: 25.4s	remaining: 2.27s
918:	learn: 1642.4686556	total: 25.4s	remaining: 2.24s
919:	learn: 1642.2731516	total: 25.5s	remaining: 2.21s
920:	learn: 1642.1689847	total: 25.5s	remaining: 2.19s
921:	learn: 1642.0340051	total: 25.5s	remaining: 2.16s
922:	learn: 1641.9586741	total: 25.6s	remaining: 2.13s
923:	learn: 1641.8432721	total: 25.6s	remaining: 2.1s
924:	learn: 1641.7486246	total: 25.6s	remaining: 2.08s
925:	learn: 1641.7125286	total: 25.6s	remaining: 2.05s
926:	learn: 1641.5950423	total: 25.7s	remaining: 2.02s
927:	learn: 1641.4824923	total: 25.7s	remaining: 1.99s
928:	learn: 1641.3577161	total: 25.7s	remaining: 1.97s
929:	learn: 1641.1907321	total: 25.8s	remaining: 1.94s
930:	learn: 1641.0947514	total: 25.8s	remaining: 1.91s
931:	learn: 1640.9059267	total: 25.8s	remaining: 1.88s
932:	learn: 1640.7250869	total: 25.8s	remaining: 1.85s
933:	learn: 1640.6127859	total: 25.9s	remaining: 1.83s
934:	learn: 1640.4982666	total: 25.9s	remaining: 1.8s
935:	learn: 1640.3904213	total: 25.9s	remaining: 1.77s
936:	learn: 1640.2900579	total: 26s	remaining: 1.75s
937:	learn: 1640.0903968	total: 26s	remaining: 1.72s
938:	learn: 1639.9924277	total: 26s	remaining: 1.69s
939:	learn: 1639.8339284	total: 26s	remaining: 1.66s
940:	learn: 1639.7347681	total: 26.1s	remaining: 1.63s
941:	learn: 1639.5997686	total: 26.1s	remaining: 1.61s
942:	learn: 1639.4792733	total: 26.1s	remaining: 1.58s
943:	learn: 1639.3978780	total: 26.2s	remaining: 1.55s
944:	learn: 1639.3155623	total: 26.2s	remaining: 1.52s
945:	learn: 1639.2351247	total: 26.2s	remaining: 1.5s
946:	learn: 1639.1582369	total: 26.2s	remaining: 1.47s
947:	learn: 1638.9711013	total: 26.3s	remaining: 1.44s
948:	learn: 1638.7849980	total: 26.3s	remaining: 1.41s
949:	learn: 1638.6744101	total: 26.3s	remaining: 1.39s
950:	learn: 1638.4995723	total: 26.3s	remaining: 1.36s
951:	learn: 1638.3695943	total: 26.4s	remaining: 1.33s
952:	learn: 1638.2048436	total: 26.4s	remaining: 1.3s
953:	learn: 1638.0983857	total: 26.4s	remaining: 1.27s
954:	learn: 1637.9653838	total: 26.5s	remaining: 1.25s
955:	learn: 1637.8520881	total: 26.5s	remaining: 1.22s
956:	learn: 1637.7317360	total: 26.5s	remaining: 1.19s
957:	learn: 1637.5546457	total: 26.5s	remaining: 1.16s
958:	learn: 1637.4251170	total: 26.6s	remaining: 1.14s
959:	learn: 1637.3077433	total: 26.6s	remaining: 1.11s
960:	learn: 1637.1882530	total: 26.6s	remaining: 1.08s
961:	learn: 1637.1305962	total: 26.7s	remaining: 1.05s
962:	learn: 1637.0143659	total: 26.7s	remaining: 1.02s
963:	learn: 1636.9516064	total: 26.7s	remaining: 998ms
964:	learn: 1636.8410392	total: 26.8s	remaining: 971ms
965:	learn: 1636.7464148	total: 26.8s	remaining: 943ms
966:	learn: 1636.7083497	total: 26.8s	remaining: 915ms
967:	learn: 1636.5932615	total: 26.8s	remaining: 888ms
968:	learn: 1636.5036348	total: 26.9s	remaining: 860ms
969:	learn: 1636.4107337	total: 26.9s	remaining: 832ms
970:	learn: 1636.3366511	total: 26.9s	remaining: 805ms
971:	learn: 1636.2240660	total: 27s	remaining: 777ms
972:	learn: 1636.1251069	total: 27s	remaining: 749ms
973:	learn: 1635.9905361	total: 27s	remaining: 721ms
974:	learn: 1635.9437341	total: 27.1s	remaining: 694ms
975:	learn: 1635.8409764	total: 27.1s	remaining: 666ms
976:	learn: 1635.7162099	total: 27.1s	remaining: 638ms
977:	learn: 1635.6287511	total: 27.1s	remaining: 610ms
978:	learn: 1635.5549846	total: 27.2s	remaining: 583ms
979:	learn: 1635.4051529	total: 27.2s	remaining: 555ms
980:	learn: 1635.3406003	total: 27.2s	remaining: 527ms
981:	learn: 1635.2764127	total: 27.3s	remaining: 500ms
982:	learn: 1635.1289619	total: 27.3s	remaining: 472ms
983:	learn: 1635.0934711	total: 27.3s	remaining: 444ms
984:	learn: 1635.0317147	total: 27.3s	remaining: 416ms
985:	learn: 1634.9273438	total: 27.4s	remaining: 389ms
986:	learn: 1634.8101853	total: 27.4s	remaining: 361ms
987:	learn: 1634.6494664	total: 27.4s	remaining: 333ms
988:	learn: 1634.4437055	total: 27.5s	remaining: 305ms
989:	learn: 1634.3706506	total: 27.5s	remaining: 278ms
990:	learn: 1634.2287648	total: 27.5s	remaining: 250ms
991:	learn: 1634.0952565	total: 27.5s	remaining: 222ms
992:	learn: 1633.9584680	total: 27.6s	remaining: 194ms
993:	learn: 1633.8105420	total: 27.6s	remaining: 167ms
994:	learn: 1633.7068815	total: 27.6s	remaining: 139ms
995:	learn: 1633.6137294	total: 27.7s	remaining: 111ms
996:	learn: 1633.5306133	total: 27.7s	remaining: 83.3ms
997:	learn: 1633.4055632	total: 27.7s	remaining: 55.5ms
998:	learn: 1633.3060596	total: 27.7s	remaining: 27.8ms
999:	learn: 1633.2108707	total: 27.8s	remaining: 0us
Learning rate set to 0.097814
0:	learn: 4268.9324355	total: 47.7ms	remaining: 47.6s
1:	learn: 4010.6410460	total: 88.4ms	remaining: 44.1s
2:	learn: 3784.1669481	total: 134ms	remaining: 44.4s
3:	learn: 3579.5232253	total: 170ms	remaining: 42.3s
4:	learn: 3400.3400769	total: 222ms	remaining: 44.2s
5:	learn: 3242.2229009	total: 259ms	remaining: 42.9s
6:	learn: 3101.0951468	total: 309ms	remaining: 43.8s
7:	learn: 2976.0448761	total: 345ms	remaining: 42.8s
8:	learn: 2865.0184049	total: 388ms	remaining: 42.7s
9:	learn: 2769.5971830	total: 428ms	remaining: 42.4s
10:	learn: 2688.7300008	total: 463ms	remaining: 41.7s
11:	learn: 2611.4192577	total: 515ms	remaining: 42.4s
12:	learn: 2547.1626659	total: 553ms	remaining: 42s
13:	learn: 2486.4814447	total: 597ms	remaining: 42.1s
14:	learn: 2436.7976506	total: 631ms	remaining: 41.4s
15:	learn: 2388.2476102	total: 675ms	remaining: 41.5s
16:	learn: 2349.1750890	total: 718ms	remaining: 41.5s
17:	learn: 2311.5397711	total: 752ms	remaining: 41s
18:	learn: 2279.0709995	total: 799ms	remaining: 41.3s
19:	learn: 2249.5200641	total: 835ms	remaining: 40.9s
20:	learn: 2225.0638599	total: 875ms	remaining: 40.8s
21:	learn: 2202.5869378	total: 915ms	remaining: 40.7s
22:	learn: 2181.3138603	total: 950ms	remaining: 40.3s
23:	learn: 2162.7987170	total: 994ms	remaining: 40.4s
24:	learn: 2146.4960625	total: 1.03s	remaining: 40.1s
25:	learn: 2130.0284629	total: 1.07s	remaining: 40s
26:	learn: 2114.8175590	total: 1.1s	remaining: 39.9s
27:	learn: 2103.0645814	total: 1.14s	remaining: 39.6s
28:	learn: 2092.1972325	total: 1.18s	remaining: 39.6s
29:	learn: 2082.0707635	total: 1.22s	remaining: 39.4s
30:	learn: 2072.5199001	total: 1.26s	remaining: 39.3s
31:	learn: 2061.9256277	total: 1.3s	remaining: 39.3s
32:	learn: 2053.0818270	total: 1.34s	remaining: 39.2s
33:	learn: 2045.8192642	total: 1.38s	remaining: 39.2s
34:	learn: 2038.9822441	total: 1.41s	remaining: 39s
35:	learn: 2033.6254723	total: 1.45s	remaining: 38.8s
36:	learn: 2028.4506164	total: 1.49s	remaining: 38.8s
37:	learn: 2020.3179117	total: 1.53s	remaining: 38.7s
38:	learn: 2015.5523488	total: 1.57s	remaining: 38.7s
39:	learn: 2010.3228599	total: 1.6s	remaining: 38.5s
40:	learn: 2004.3267459	total: 1.64s	remaining: 38.4s
41:	learn: 1998.5867855	total: 1.69s	remaining: 38.4s
42:	learn: 1994.6349425	total: 1.72s	remaining: 38.2s
43:	learn: 1991.2172038	total: 1.76s	remaining: 38.2s
44:	learn: 1988.5022244	total: 1.79s	remaining: 38s
45:	learn: 1984.3815351	total: 1.83s	remaining: 38s
46:	learn: 1978.7809852	total: 1.88s	remaining: 38s
47:	learn: 1976.1214547	total: 1.91s	remaining: 37.8s
48:	learn: 1972.7190666	total: 1.95s	remaining: 37.8s
49:	learn: 1968.6779676	total: 1.98s	remaining: 37.7s
50:	learn: 1963.1907207	total: 2.02s	remaining: 37.6s
51:	learn: 1960.2988615	total: 2.07s	remaining: 37.7s
52:	learn: 1957.8772666	total: 2.1s	remaining: 37.5s
53:	learn: 1955.8311334	total: 2.14s	remaining: 37.5s
54:	learn: 1953.0846871	total: 2.18s	remaining: 37.4s
55:	learn: 1950.5234577	total: 2.21s	remaining: 37.2s
56:	learn: 1946.2558032	total: 2.25s	remaining: 37.3s
57:	learn: 1943.7185580	total: 2.29s	remaining: 37.1s
58:	learn: 1941.8747937	total: 2.32s	remaining: 37s
59:	learn: 1938.4969708	total: 2.36s	remaining: 37s
60:	learn: 1935.7775907	total: 2.4s	remaining: 36.9s
61:	learn: 1934.4810527	total: 2.43s	remaining: 36.8s
62:	learn: 1931.2776221	total: 2.47s	remaining: 36.8s
63:	learn: 1929.5589586	total: 2.5s	remaining: 36.6s
64:	learn: 1926.3388558	total: 2.55s	remaining: 36.7s
65:	learn: 1923.6750746	total: 2.58s	remaining: 36.6s
66:	learn: 1922.5330800	total: 2.62s	remaining: 36.5s
67:	learn: 1920.8281168	total: 2.66s	remaining: 36.4s
68:	learn: 1918.3175340	total: 2.69s	remaining: 36.3s
69:	learn: 1915.7574885	total: 2.73s	remaining: 36.3s
70:	learn: 1913.6291187	total: 2.77s	remaining: 36.3s
71:	learn: 1911.3063450	total: 2.81s	remaining: 36.2s
72:	learn: 1910.0146675	total: 2.85s	remaining: 36.1s
73:	learn: 1908.5637534	total: 2.88s	remaining: 36s
74:	learn: 1906.4780092	total: 2.92s	remaining: 36s
75:	learn: 1905.1691680	total: 2.96s	remaining: 36s
76:	learn: 1903.1997125	total: 2.99s	remaining: 35.8s
77:	learn: 1901.8821513	total: 3.03s	remaining: 35.9s
78:	learn: 1900.1337731	total: 3.06s	remaining: 35.7s
79:	learn: 1899.1992384	total: 3.1s	remaining: 35.6s
80:	learn: 1897.3895928	total: 3.14s	remaining: 35.6s
81:	learn: 1895.8139714	total: 3.17s	remaining: 35.5s
82:	learn: 1893.6768162	total: 3.22s	remaining: 35.5s
83:	learn: 1891.4986383	total: 3.25s	remaining: 35.5s
84:	learn: 1889.8489640	total: 3.29s	remaining: 35.4s
85:	learn: 1888.8394635	total: 3.33s	remaining: 35.4s
86:	learn: 1887.7631764	total: 3.36s	remaining: 35.3s
87:	learn: 1886.0409055	total: 3.4s	remaining: 35.2s
88:	learn: 1884.4447070	total: 3.44s	remaining: 35.2s
89:	learn: 1882.9561620	total: 3.47s	remaining: 35.1s
90:	learn: 1881.6691320	total: 3.51s	remaining: 35.1s
91:	learn: 1879.9898022	total: 3.55s	remaining: 35s
92:	learn: 1879.0661497	total: 3.58s	remaining: 34.9s
93:	learn: 1877.3728090	total: 3.62s	remaining: 34.9s
94:	learn: 1876.1311891	total: 3.66s	remaining: 34.8s
95:	learn: 1874.7015338	total: 3.7s	remaining: 34.8s
96:	learn: 1873.5465573	total: 3.73s	remaining: 34.8s
97:	learn: 1872.7624631	total: 3.77s	remaining: 34.7s
98:	learn: 1870.7938629	total: 3.81s	remaining: 34.6s
99:	learn: 1869.2673785	total: 3.84s	remaining: 34.6s
100:	learn: 1867.9182654	total: 3.89s	remaining: 34.6s
101:	learn: 1866.6275050	total: 3.92s	remaining: 34.5s
102:	learn: 1864.8356438	total: 3.96s	remaining: 34.5s
103:	learn: 1863.8575595	total: 4s	remaining: 34.5s
104:	learn: 1862.9924644	total: 4.04s	remaining: 34.4s
105:	learn: 1862.4445378	total: 4.08s	remaining: 34.4s
106:	learn: 1861.5129977	total: 4.11s	remaining: 34.3s
107:	learn: 1859.9204173	total: 4.14s	remaining: 34.2s
108:	learn: 1858.7382351	total: 4.18s	remaining: 34.2s
109:	learn: 1857.3892643	total: 4.22s	remaining: 34.1s
110:	learn: 1856.6283950	total: 4.26s	remaining: 34.1s
111:	learn: 1855.4247124	total: 4.29s	remaining: 34s
112:	learn: 1854.3838611	total: 4.33s	remaining: 34s
113:	learn: 1853.5954063	total: 4.37s	remaining: 34s
114:	learn: 1852.6219831	total: 4.4s	remaining: 33.9s
115:	learn: 1851.9004090	total: 4.44s	remaining: 33.8s
116:	learn: 1851.2159787	total: 4.47s	remaining: 33.8s
117:	learn: 1850.5915299	total: 4.5s	remaining: 33.7s
118:	learn: 1849.8976949	total: 4.54s	remaining: 33.6s
119:	learn: 1848.8652075	total: 4.58s	remaining: 33.6s
120:	learn: 1847.8232751	total: 4.62s	remaining: 33.5s
121:	learn: 1847.2230104	total: 4.66s	remaining: 33.5s
122:	learn: 1846.5371880	total: 4.7s	remaining: 33.5s
123:	learn: 1845.8262919	total: 4.73s	remaining: 33.4s
124:	learn: 1845.3827261	total: 4.77s	remaining: 33.4s
125:	learn: 1844.6014292	total: 4.8s	remaining: 33.3s
126:	learn: 1843.7578276	total: 4.84s	remaining: 33.3s
127:	learn: 1843.0869271	total: 4.88s	remaining: 33.2s
128:	learn: 1841.9619538	total: 4.91s	remaining: 33.2s
129:	learn: 1841.2991078	total: 4.95s	remaining: 33.2s
130:	learn: 1840.4296282	total: 4.99s	remaining: 33.1s
131:	learn: 1839.1957353	total: 5.03s	remaining: 33.1s
132:	learn: 1838.1950594	total: 5.06s	remaining: 33s
133:	learn: 1837.3731522	total: 5.1s	remaining: 32.9s
134:	learn: 1836.7928530	total: 5.14s	remaining: 32.9s
135:	learn: 1835.7954051	total: 5.18s	remaining: 32.9s
136:	learn: 1834.9445079	total: 5.21s	remaining: 32.8s
137:	learn: 1834.0497983	total: 5.25s	remaining: 32.8s
138:	learn: 1833.0138969	total: 5.29s	remaining: 32.8s
139:	learn: 1832.2018222	total: 5.33s	remaining: 32.7s
140:	learn: 1831.6937920	total: 5.36s	remaining: 32.7s
141:	learn: 1831.1211248	total: 5.39s	remaining: 32.6s
142:	learn: 1830.3238911	total: 5.43s	remaining: 32.6s
143:	learn: 1829.7812267	total: 5.46s	remaining: 32.5s
144:	learn: 1829.0570199	total: 5.5s	remaining: 32.4s
145:	learn: 1828.4505519	total: 5.54s	remaining: 32.4s
146:	learn: 1828.0967705	total: 5.57s	remaining: 32.3s
147:	learn: 1827.0128422	total: 5.61s	remaining: 32.3s
148:	learn: 1826.1013723	total: 5.65s	remaining: 32.3s
149:	learn: 1825.4182648	total: 5.68s	remaining: 32.2s
150:	learn: 1824.6176075	total: 5.73s	remaining: 32.2s
151:	learn: 1823.8060667	total: 5.76s	remaining: 32.1s
152:	learn: 1823.1678913	total: 5.8s	remaining: 32.1s
153:	learn: 1822.7543456	total: 5.84s	remaining: 32.1s
154:	learn: 1822.1850611	total: 5.87s	remaining: 32s
155:	learn: 1821.5692808	total: 5.92s	remaining: 32s
156:	learn: 1820.9095442	total: 5.96s	remaining: 32s
157:	learn: 1820.4462638	total: 5.99s	remaining: 31.9s
158:	learn: 1819.8860522	total: 6.03s	remaining: 31.9s
159:	learn: 1819.2441115	total: 6.06s	remaining: 31.8s
160:	learn: 1818.4616216	total: 6.11s	remaining: 31.8s
161:	learn: 1817.9427457	total: 6.14s	remaining: 31.8s
162:	learn: 1817.4403200	total: 6.18s	remaining: 31.7s
163:	learn: 1816.9666062	total: 6.22s	remaining: 31.7s
164:	learn: 1816.2439721	total: 6.25s	remaining: 31.6s
165:	learn: 1815.8090863	total: 6.29s	remaining: 31.6s
166:	learn: 1815.3273199	total: 6.33s	remaining: 31.6s
167:	learn: 1814.5158132	total: 6.36s	remaining: 31.5s
168:	learn: 1814.1986002	total: 6.4s	remaining: 31.5s
169:	learn: 1813.6626373	total: 6.44s	remaining: 31.4s
170:	learn: 1812.9822789	total: 6.47s	remaining: 31.4s
171:	learn: 1812.5582427	total: 6.51s	remaining: 31.4s
172:	learn: 1811.8354672	total: 6.54s	remaining: 31.3s
173:	learn: 1811.3725020	total: 6.58s	remaining: 31.2s
174:	learn: 1810.8608786	total: 6.62s	remaining: 31.2s
175:	learn: 1810.2449498	total: 6.65s	remaining: 31.1s
176:	learn: 1809.6423035	total: 6.7s	remaining: 31.1s
177:	learn: 1809.2480727	total: 6.73s	remaining: 31.1s
178:	learn: 1808.6675072	total: 6.76s	remaining: 31s
179:	learn: 1808.0039389	total: 6.8s	remaining: 31s
180:	learn: 1807.6046747	total: 6.83s	remaining: 30.9s
181:	learn: 1807.3519822	total: 6.87s	remaining: 30.9s
182:	learn: 1806.8990466	total: 6.91s	remaining: 30.8s
183:	learn: 1806.2886441	total: 6.94s	remaining: 30.8s
184:	learn: 1805.5854407	total: 6.99s	remaining: 30.8s
185:	learn: 1805.0783816	total: 7.03s	remaining: 30.8s
186:	learn: 1804.5242344	total: 7.07s	remaining: 30.7s
187:	learn: 1804.3149081	total: 7.1s	remaining: 30.7s
188:	learn: 1803.7452142	total: 7.14s	remaining: 30.6s
189:	learn: 1803.4434408	total: 7.18s	remaining: 30.6s
190:	learn: 1803.1064225	total: 7.22s	remaining: 30.6s
191:	learn: 1802.6029852	total: 7.27s	remaining: 30.6s
192:	learn: 1802.1195912	total: 7.3s	remaining: 30.5s
193:	learn: 1801.6937097	total: 7.35s	remaining: 30.5s
194:	learn: 1801.2427150	total: 7.39s	remaining: 30.5s
195:	learn: 1800.9240433	total: 7.43s	remaining: 30.5s
196:	learn: 1800.5739351	total: 7.47s	remaining: 30.5s
197:	learn: 1800.1988974	total: 7.51s	remaining: 30.4s
198:	learn: 1799.8508234	total: 7.55s	remaining: 30.4s
199:	learn: 1799.1884678	total: 7.59s	remaining: 30.4s
200:	learn: 1798.5902332	total: 7.63s	remaining: 30.3s
201:	learn: 1798.0212189	total: 7.67s	remaining: 30.3s
202:	learn: 1797.7243252	total: 7.71s	remaining: 30.3s
203:	learn: 1797.0336469	total: 7.75s	remaining: 30.2s
204:	learn: 1796.3191776	total: 7.79s	remaining: 30.2s
205:	learn: 1795.9519571	total: 7.82s	remaining: 30.2s
206:	learn: 1795.5950900	total: 7.87s	remaining: 30.1s
207:	learn: 1795.2389059	total: 7.91s	remaining: 30.1s
208:	learn: 1794.7079839	total: 7.95s	remaining: 30.1s
209:	learn: 1794.1596165	total: 7.99s	remaining: 30s
210:	learn: 1793.7831297	total: 8.03s	remaining: 30s
211:	learn: 1793.4907585	total: 8.06s	remaining: 30s
212:	learn: 1792.9769478	total: 8.09s	remaining: 29.9s
213:	learn: 1792.5382337	total: 8.13s	remaining: 29.9s
214:	learn: 1791.9538646	total: 8.17s	remaining: 29.8s
215:	learn: 1791.2988125	total: 8.21s	remaining: 29.8s
216:	learn: 1790.8879168	total: 8.25s	remaining: 29.8s
217:	learn: 1790.5466705	total: 8.28s	remaining: 29.7s
218:	learn: 1790.1821375	total: 8.31s	remaining: 29.6s
219:	learn: 1789.8022776	total: 8.35s	remaining: 29.6s
220:	learn: 1789.3897690	total: 8.38s	remaining: 29.5s
221:	learn: 1789.2006412	total: 8.42s	remaining: 29.5s
222:	learn: 1788.6142665	total: 8.46s	remaining: 29.5s
223:	learn: 1788.1002454	total: 8.49s	remaining: 29.4s
224:	learn: 1787.6531289	total: 8.53s	remaining: 29.4s
225:	learn: 1787.2842224	total: 8.56s	remaining: 29.3s
226:	learn: 1786.9077368	total: 8.61s	remaining: 29.3s
227:	learn: 1786.4275520	total: 8.64s	remaining: 29.3s
228:	learn: 1786.1205733	total: 8.67s	remaining: 29.2s
229:	learn: 1785.6904303	total: 8.72s	remaining: 29.2s
230:	learn: 1785.3968018	total: 8.75s	remaining: 29.1s
231:	learn: 1784.9782433	total: 8.79s	remaining: 29.1s
232:	learn: 1784.6855910	total: 8.82s	remaining: 29s
233:	learn: 1784.2238292	total: 8.86s	remaining: 29s
234:	learn: 1783.9608709	total: 8.89s	remaining: 29s
235:	learn: 1783.6271962	total: 8.93s	remaining: 28.9s
236:	learn: 1783.2335930	total: 8.96s	remaining: 28.8s
237:	learn: 1782.7868410	total: 9s	remaining: 28.8s
238:	learn: 1782.5721117	total: 9.04s	remaining: 28.8s
239:	learn: 1781.9219510	total: 9.08s	remaining: 28.8s
240:	learn: 1781.7145898	total: 9.12s	remaining: 28.7s
241:	learn: 1781.3611394	total: 9.15s	remaining: 28.7s
242:	learn: 1781.1198496	total: 9.2s	remaining: 28.7s
243:	learn: 1780.6603243	total: 9.23s	remaining: 28.6s
244:	learn: 1780.2112602	total: 9.27s	remaining: 28.6s
245:	learn: 1779.9453147	total: 9.31s	remaining: 28.5s
246:	learn: 1779.5035305	total: 9.34s	remaining: 28.5s
247:	learn: 1779.1024113	total: 9.39s	remaining: 28.5s
248:	learn: 1778.6124969	total: 9.42s	remaining: 28.4s
249:	learn: 1778.2540646	total: 9.46s	remaining: 28.4s
250:	learn: 1777.8708548	total: 9.5s	remaining: 28.3s
251:	learn: 1777.6396625	total: 9.53s	remaining: 28.3s
252:	learn: 1777.3333206	total: 9.56s	remaining: 28.2s
253:	learn: 1776.9195690	total: 9.61s	remaining: 28.2s
254:	learn: 1776.6025572	total: 9.64s	remaining: 28.2s
255:	learn: 1776.1948877	total: 9.68s	remaining: 28.1s
256:	learn: 1775.9161063	total: 9.72s	remaining: 28.1s
257:	learn: 1775.6270258	total: 9.75s	remaining: 28s
258:	learn: 1775.3998128	total: 9.79s	remaining: 28s
259:	learn: 1775.0897997	total: 9.83s	remaining: 28s
260:	learn: 1774.8271029	total: 9.87s	remaining: 27.9s
261:	learn: 1774.3388322	total: 9.9s	remaining: 27.9s
262:	learn: 1774.0200502	total: 9.94s	remaining: 27.9s
263:	learn: 1773.6887452	total: 9.98s	remaining: 27.8s
264:	learn: 1773.4059839	total: 10s	remaining: 27.8s
265:	learn: 1773.1201130	total: 10.1s	remaining: 27.8s
266:	learn: 1772.7696711	total: 10.1s	remaining: 27.7s
267:	learn: 1772.4872386	total: 10.1s	remaining: 27.7s
268:	learn: 1772.2062464	total: 10.2s	remaining: 27.6s
269:	learn: 1771.8423772	total: 10.2s	remaining: 27.6s
270:	learn: 1771.5846551	total: 10.2s	remaining: 27.6s
271:	learn: 1771.4041686	total: 10.3s	remaining: 27.5s
272:	learn: 1770.8989639	total: 10.3s	remaining: 27.5s
273:	learn: 1770.1904435	total: 10.4s	remaining: 27.5s
274:	learn: 1769.7568932	total: 10.4s	remaining: 27.4s
275:	learn: 1769.3664708	total: 10.4s	remaining: 27.4s
276:	learn: 1769.0642151	total: 10.5s	remaining: 27.4s
277:	learn: 1768.6658957	total: 10.5s	remaining: 27.3s
278:	learn: 1768.1364497	total: 10.6s	remaining: 27.3s
279:	learn: 1767.9284926	total: 10.6s	remaining: 27.2s
280:	learn: 1767.6985789	total: 10.6s	remaining: 27.2s
281:	learn: 1767.1823263	total: 10.7s	remaining: 27.2s
282:	learn: 1766.8906104	total: 10.7s	remaining: 27.1s
283:	learn: 1766.6383109	total: 10.8s	remaining: 27.1s
284:	learn: 1766.1665040	total: 10.8s	remaining: 27.1s
285:	learn: 1765.8448374	total: 10.8s	remaining: 27s
286:	learn: 1765.5680723	total: 10.9s	remaining: 27s
287:	learn: 1765.3666445	total: 10.9s	remaining: 27s
288:	learn: 1765.1500794	total: 10.9s	remaining: 26.9s
289:	learn: 1764.8564873	total: 11s	remaining: 26.9s
290:	learn: 1764.5185652	total: 11s	remaining: 26.9s
291:	learn: 1764.1867872	total: 11.1s	remaining: 26.8s
292:	learn: 1763.8993393	total: 11.1s	remaining: 26.8s
293:	learn: 1763.6660679	total: 11.1s	remaining: 26.8s
294:	learn: 1763.3575871	total: 11.2s	remaining: 26.7s
295:	learn: 1763.1142639	total: 11.2s	remaining: 26.7s
296:	learn: 1762.6812398	total: 11.3s	remaining: 26.6s
297:	learn: 1762.2830127	total: 11.3s	remaining: 26.6s
298:	learn: 1762.0901304	total: 11.3s	remaining: 26.6s
299:	learn: 1761.7681685	total: 11.4s	remaining: 26.5s
300:	learn: 1761.4818991	total: 11.4s	remaining: 26.5s
301:	learn: 1761.1103422	total: 11.4s	remaining: 26.4s
302:	learn: 1760.8641990	total: 11.5s	remaining: 26.4s
303:	learn: 1760.6317205	total: 11.5s	remaining: 26.4s
304:	learn: 1760.3780051	total: 11.6s	remaining: 26.3s
305:	learn: 1760.0414518	total: 11.6s	remaining: 26.3s
306:	learn: 1759.8263695	total: 11.6s	remaining: 26.3s
307:	learn: 1759.5421926	total: 11.7s	remaining: 26.2s
308:	learn: 1759.2803304	total: 11.7s	remaining: 26.2s
309:	learn: 1758.7037405	total: 11.8s	remaining: 26.2s
310:	learn: 1758.4650643	total: 11.8s	remaining: 26.1s
311:	learn: 1758.0051509	total: 11.8s	remaining: 26.1s
312:	learn: 1757.8479266	total: 11.9s	remaining: 26.1s
313:	learn: 1757.5739874	total: 11.9s	remaining: 26s
314:	learn: 1757.3063596	total: 11.9s	remaining: 26s
315:	learn: 1757.0848107	total: 12s	remaining: 25.9s
316:	learn: 1756.7994567	total: 12s	remaining: 25.9s
317:	learn: 1756.5600904	total: 12.1s	remaining: 25.9s
318:	learn: 1756.2659534	total: 12.1s	remaining: 25.8s
319:	learn: 1756.0907102	total: 12.1s	remaining: 25.8s
320:	learn: 1755.8491010	total: 12.2s	remaining: 25.7s
321:	learn: 1755.5478877	total: 12.2s	remaining: 25.7s
322:	learn: 1755.1168360	total: 12.2s	remaining: 25.7s
323:	learn: 1754.8449535	total: 12.3s	remaining: 25.6s
324:	learn: 1754.5057935	total: 12.3s	remaining: 25.6s
325:	learn: 1754.0880522	total: 12.4s	remaining: 25.6s
326:	learn: 1753.8344479	total: 12.4s	remaining: 25.5s
327:	learn: 1753.6281704	total: 12.4s	remaining: 25.5s
328:	learn: 1753.2965795	total: 12.5s	remaining: 25.4s
329:	learn: 1752.9300382	total: 12.5s	remaining: 25.4s
330:	learn: 1752.5147734	total: 12.6s	remaining: 25.4s
331:	learn: 1752.1240970	total: 12.6s	remaining: 25.4s
332:	learn: 1751.9118084	total: 12.6s	remaining: 25.3s
333:	learn: 1751.5902142	total: 12.7s	remaining: 25.3s
334:	learn: 1751.3225292	total: 12.7s	remaining: 25.3s
335:	learn: 1750.9929799	total: 12.8s	remaining: 25.2s
336:	learn: 1750.7245450	total: 12.8s	remaining: 25.2s
337:	learn: 1750.3474505	total: 12.8s	remaining: 25.1s
338:	learn: 1750.1473267	total: 12.9s	remaining: 25.1s
339:	learn: 1749.9925988	total: 12.9s	remaining: 25.1s
340:	learn: 1749.7293843	total: 12.9s	remaining: 25s
341:	learn: 1749.5443800	total: 13s	remaining: 25s
342:	learn: 1749.2786608	total: 13s	remaining: 24.9s
343:	learn: 1749.0282543	total: 13.1s	remaining: 24.9s
344:	learn: 1748.8016758	total: 13.1s	remaining: 24.9s
345:	learn: 1748.5816151	total: 13.1s	remaining: 24.8s
346:	learn: 1748.2391353	total: 13.2s	remaining: 24.8s
347:	learn: 1747.9193570	total: 13.2s	remaining: 24.8s
348:	learn: 1747.5006882	total: 13.3s	remaining: 24.7s
349:	learn: 1747.2459727	total: 13.3s	remaining: 24.7s
350:	learn: 1746.9780406	total: 13.3s	remaining: 24.7s
351:	learn: 1746.6494659	total: 13.4s	remaining: 24.6s
352:	learn: 1746.4461148	total: 13.4s	remaining: 24.6s
353:	learn: 1746.2071127	total: 13.5s	remaining: 24.5s
354:	learn: 1745.9392687	total: 13.5s	remaining: 24.5s
355:	learn: 1745.5625000	total: 13.5s	remaining: 24.5s
356:	learn: 1745.2700794	total: 13.6s	remaining: 24.4s
357:	learn: 1744.8984304	total: 13.6s	remaining: 24.4s
358:	learn: 1744.6932283	total: 13.6s	remaining: 24.4s
359:	learn: 1744.5093506	total: 13.7s	remaining: 24.3s
360:	learn: 1744.2132385	total: 13.7s	remaining: 24.3s
361:	learn: 1743.7651854	total: 13.8s	remaining: 24.3s
362:	learn: 1743.2861394	total: 13.8s	remaining: 24.2s
363:	learn: 1743.0874491	total: 13.8s	remaining: 24.2s
364:	learn: 1742.8353213	total: 13.9s	remaining: 24.2s
365:	learn: 1742.6024486	total: 13.9s	remaining: 24.1s
366:	learn: 1742.3204285	total: 14s	remaining: 24.1s
367:	learn: 1742.1034864	total: 14s	remaining: 24s
368:	learn: 1741.6239581	total: 14s	remaining: 24s
369:	learn: 1741.3617940	total: 14.1s	remaining: 24s
370:	learn: 1741.0757835	total: 14.1s	remaining: 23.9s
371:	learn: 1740.7511262	total: 14.2s	remaining: 23.9s
372:	learn: 1740.4421277	total: 14.2s	remaining: 23.9s
373:	learn: 1740.1921007	total: 14.2s	remaining: 23.8s
374:	learn: 1739.7759459	total: 14.3s	remaining: 23.8s
375:	learn: 1739.5004514	total: 14.3s	remaining: 23.8s
376:	learn: 1739.2280361	total: 14.4s	remaining: 23.7s
377:	learn: 1739.0545965	total: 14.4s	remaining: 23.7s
378:	learn: 1738.8086327	total: 14.4s	remaining: 23.6s
379:	learn: 1738.6508553	total: 14.5s	remaining: 23.6s
380:	learn: 1738.3791908	total: 14.5s	remaining: 23.6s
381:	learn: 1738.1401524	total: 14.5s	remaining: 23.5s
382:	learn: 1737.8842934	total: 14.6s	remaining: 23.5s
383:	learn: 1737.6455247	total: 14.6s	remaining: 23.5s
384:	learn: 1737.4289805	total: 14.7s	remaining: 23.4s
385:	learn: 1737.1759950	total: 14.7s	remaining: 23.4s
386:	learn: 1736.9287996	total: 14.7s	remaining: 23.4s
387:	learn: 1736.6644316	total: 14.8s	remaining: 23.3s
388:	learn: 1736.4509275	total: 14.8s	remaining: 23.3s
389:	learn: 1736.1780686	total: 14.9s	remaining: 23.2s
390:	learn: 1735.8027451	total: 14.9s	remaining: 23.2s
391:	learn: 1735.5312872	total: 14.9s	remaining: 23.2s
392:	learn: 1735.2365895	total: 15s	remaining: 23.1s
393:	learn: 1735.0621982	total: 15s	remaining: 23.1s
394:	learn: 1734.7786646	total: 15.1s	remaining: 23.1s
395:	learn: 1734.5900719	total: 15.1s	remaining: 23s
396:	learn: 1734.4205195	total: 15.1s	remaining: 23s
397:	learn: 1734.1769668	total: 15.2s	remaining: 22.9s
398:	learn: 1734.0092385	total: 15.2s	remaining: 22.9s
399:	learn: 1733.6076731	total: 15.2s	remaining: 22.9s
400:	learn: 1733.4073943	total: 15.3s	remaining: 22.8s
401:	learn: 1733.1870464	total: 15.3s	remaining: 22.8s
402:	learn: 1732.8418426	total: 15.3s	remaining: 22.7s
403:	learn: 1732.5884280	total: 15.4s	remaining: 22.7s
404:	learn: 1732.4253999	total: 15.4s	remaining: 22.7s
405:	learn: 1732.2204470	total: 15.5s	remaining: 22.6s
406:	learn: 1732.0078576	total: 15.5s	remaining: 22.6s
407:	learn: 1731.6408685	total: 15.5s	remaining: 22.6s
408:	learn: 1731.4376410	total: 15.6s	remaining: 22.5s
409:	learn: 1731.0719856	total: 15.6s	remaining: 22.5s
410:	learn: 1730.9297863	total: 15.7s	remaining: 22.4s
411:	learn: 1730.5167411	total: 15.7s	remaining: 22.4s
412:	learn: 1730.2006985	total: 15.7s	remaining: 22.4s
413:	learn: 1729.9278694	total: 15.8s	remaining: 22.3s
414:	learn: 1729.6756361	total: 15.8s	remaining: 22.3s
415:	learn: 1729.4111341	total: 15.8s	remaining: 22.2s
416:	learn: 1729.2568988	total: 15.9s	remaining: 22.2s
417:	learn: 1729.0928255	total: 15.9s	remaining: 22.2s
418:	learn: 1728.8499582	total: 16s	remaining: 22.1s
419:	learn: 1728.5351801	total: 16s	remaining: 22.1s
420:	learn: 1728.2158585	total: 16s	remaining: 22.1s
421:	learn: 1728.0929619	total: 16.1s	remaining: 22s
422:	learn: 1727.9116979	total: 16.1s	remaining: 22s
423:	learn: 1727.6771975	total: 16.2s	remaining: 21.9s
424:	learn: 1727.4173729	total: 16.2s	remaining: 21.9s
425:	learn: 1727.0662389	total: 16.2s	remaining: 21.9s
426:	learn: 1726.8669824	total: 16.3s	remaining: 21.8s
427:	learn: 1726.6371360	total: 16.3s	remaining: 21.8s
428:	learn: 1726.3195048	total: 16.3s	remaining: 21.7s
429:	learn: 1726.0887469	total: 16.4s	remaining: 21.7s
430:	learn: 1725.8260628	total: 16.4s	remaining: 21.7s
431:	learn: 1725.5895624	total: 16.5s	remaining: 21.6s
432:	learn: 1725.2705483	total: 16.5s	remaining: 21.6s
433:	learn: 1725.1367573	total: 16.5s	remaining: 21.5s
434:	learn: 1725.0005454	total: 16.6s	remaining: 21.5s
435:	learn: 1724.8051876	total: 16.6s	remaining: 21.5s
436:	learn: 1724.6564043	total: 16.6s	remaining: 21.4s
437:	learn: 1724.5366264	total: 16.7s	remaining: 21.4s
438:	learn: 1724.2321711	total: 16.7s	remaining: 21.4s
439:	learn: 1724.0565394	total: 16.8s	remaining: 21.3s
440:	learn: 1723.8850055	total: 16.8s	remaining: 21.3s
441:	learn: 1723.6696154	total: 16.9s	remaining: 21.3s
442:	learn: 1723.3431246	total: 16.9s	remaining: 21.3s
443:	learn: 1723.1398505	total: 17s	remaining: 21.2s
444:	learn: 1722.8639785	total: 17s	remaining: 21.2s
445:	learn: 1722.6735897	total: 17.1s	remaining: 21.2s
446:	learn: 1722.5845354	total: 17.1s	remaining: 21.1s
447:	learn: 1722.4097049	total: 17.1s	remaining: 21.1s
448:	learn: 1722.1139844	total: 17.2s	remaining: 21.1s
449:	learn: 1721.7760568	total: 17.2s	remaining: 21s
450:	learn: 1721.4713080	total: 17.3s	remaining: 21s
451:	learn: 1721.2194677	total: 17.3s	remaining: 21s
452:	learn: 1721.0612090	total: 17.3s	remaining: 20.9s
453:	learn: 1720.9146539	total: 17.4s	remaining: 20.9s
454:	learn: 1720.8222587	total: 17.4s	remaining: 20.9s
455:	learn: 1720.6646628	total: 17.4s	remaining: 20.8s
456:	learn: 1720.4637749	total: 17.5s	remaining: 20.8s
457:	learn: 1720.2393639	total: 17.5s	remaining: 20.7s
458:	learn: 1720.1202312	total: 17.6s	remaining: 20.7s
459:	learn: 1719.7680282	total: 17.6s	remaining: 20.7s
460:	learn: 1719.5163737	total: 17.7s	remaining: 20.6s
461:	learn: 1719.3732885	total: 17.7s	remaining: 20.6s
462:	learn: 1719.2246511	total: 17.7s	remaining: 20.6s
463:	learn: 1719.1061951	total: 17.8s	remaining: 20.5s
464:	learn: 1718.8771668	total: 17.8s	remaining: 20.5s
465:	learn: 1718.6532539	total: 17.8s	remaining: 20.4s
466:	learn: 1718.4049513	total: 17.9s	remaining: 20.4s
467:	learn: 1718.2375973	total: 17.9s	remaining: 20.4s
468:	learn: 1717.9303183	total: 17.9s	remaining: 20.3s
469:	learn: 1717.7103214	total: 18s	remaining: 20.3s
470:	learn: 1717.4267298	total: 18s	remaining: 20.2s
471:	learn: 1717.1456832	total: 18.1s	remaining: 20.2s
472:	learn: 1716.9588359	total: 18.1s	remaining: 20.2s
473:	learn: 1716.8348814	total: 18.1s	remaining: 20.1s
474:	learn: 1716.5559279	total: 18.2s	remaining: 20.1s
475:	learn: 1716.3504419	total: 18.2s	remaining: 20s
476:	learn: 1716.2040290	total: 18.2s	remaining: 20s
477:	learn: 1716.0826465	total: 18.3s	remaining: 20s
478:	learn: 1715.9015271	total: 18.3s	remaining: 19.9s
479:	learn: 1715.7024150	total: 18.4s	remaining: 19.9s
480:	learn: 1715.6024799	total: 18.4s	remaining: 19.8s
481:	learn: 1715.3289656	total: 18.4s	remaining: 19.8s
482:	learn: 1715.2076024	total: 18.5s	remaining: 19.8s
483:	learn: 1714.9178652	total: 18.5s	remaining: 19.7s
484:	learn: 1714.7177816	total: 18.6s	remaining: 19.7s
485:	learn: 1714.4598430	total: 18.6s	remaining: 19.7s
486:	learn: 1714.0183643	total: 18.6s	remaining: 19.6s
487:	learn: 1713.7304333	total: 18.7s	remaining: 19.6s
488:	learn: 1713.5103897	total: 18.7s	remaining: 19.6s
489:	learn: 1713.3742397	total: 18.7s	remaining: 19.5s
490:	learn: 1713.2619764	total: 18.8s	remaining: 19.5s
491:	learn: 1713.0387966	total: 18.8s	remaining: 19.4s
492:	learn: 1712.9401211	total: 18.9s	remaining: 19.4s
493:	learn: 1712.7394449	total: 18.9s	remaining: 19.4s
494:	learn: 1712.5343663	total: 18.9s	remaining: 19.3s
495:	learn: 1712.3784743	total: 19s	remaining: 19.3s
496:	learn: 1712.0922978	total: 19s	remaining: 19.2s
497:	learn: 1711.8156402	total: 19s	remaining: 19.2s
498:	learn: 1711.6820771	total: 19.1s	remaining: 19.2s
499:	learn: 1711.4950015	total: 19.1s	remaining: 19.1s
500:	learn: 1711.2591853	total: 19.2s	remaining: 19.1s
501:	learn: 1711.0977516	total: 19.2s	remaining: 19.1s
502:	learn: 1710.9929387	total: 19.3s	remaining: 19s
503:	learn: 1710.8271099	total: 19.3s	remaining: 19s
504:	learn: 1710.6331287	total: 19.3s	remaining: 18.9s
505:	learn: 1710.4762621	total: 19.4s	remaining: 18.9s
506:	learn: 1710.3142589	total: 19.4s	remaining: 18.9s
507:	learn: 1710.1899185	total: 19.4s	remaining: 18.8s
508:	learn: 1709.9372884	total: 19.5s	remaining: 18.8s
509:	learn: 1709.7034273	total: 19.5s	remaining: 18.8s
510:	learn: 1709.5120325	total: 19.6s	remaining: 18.7s
511:	learn: 1709.2075357	total: 19.6s	remaining: 18.7s
512:	learn: 1709.0654959	total: 19.6s	remaining: 18.6s
513:	learn: 1708.8787820	total: 19.7s	remaining: 18.6s
514:	learn: 1708.6184315	total: 19.7s	remaining: 18.6s
515:	learn: 1708.5472845	total: 19.8s	remaining: 18.5s
516:	learn: 1708.4376861	total: 19.8s	remaining: 18.5s
517:	learn: 1708.2355679	total: 19.8s	remaining: 18.5s
518:	learn: 1707.9584908	total: 19.9s	remaining: 18.4s
519:	learn: 1707.6131821	total: 19.9s	remaining: 18.4s
520:	learn: 1707.3502514	total: 20s	remaining: 18.4s
521:	learn: 1707.1687955	total: 20s	remaining: 18.3s
522:	learn: 1706.8852222	total: 20s	remaining: 18.3s
523:	learn: 1706.7647810	total: 20.1s	remaining: 18.2s
524:	learn: 1706.6047831	total: 20.1s	remaining: 18.2s
525:	learn: 1706.4204895	total: 20.2s	remaining: 18.2s
526:	learn: 1706.2095678	total: 20.2s	remaining: 18.1s
527:	learn: 1706.0232433	total: 20.2s	remaining: 18.1s
528:	learn: 1705.7865371	total: 20.3s	remaining: 18.1s
529:	learn: 1705.6659996	total: 20.3s	remaining: 18s
530:	learn: 1705.5108255	total: 20.4s	remaining: 18s
531:	learn: 1705.2820013	total: 20.4s	remaining: 18s
532:	learn: 1705.0822526	total: 20.5s	remaining: 17.9s
533:	learn: 1704.9111084	total: 20.5s	remaining: 17.9s
534:	learn: 1704.7166620	total: 20.5s	remaining: 17.9s
535:	learn: 1704.5360805	total: 20.6s	remaining: 17.8s
536:	learn: 1704.3616382	total: 20.6s	remaining: 17.8s
537:	learn: 1704.2146442	total: 20.7s	remaining: 17.7s
538:	learn: 1704.0714488	total: 20.7s	remaining: 17.7s
539:	learn: 1703.8316317	total: 20.8s	remaining: 17.7s
540:	learn: 1703.5975953	total: 20.8s	remaining: 17.7s
541:	learn: 1703.4061100	total: 20.9s	remaining: 17.6s
542:	learn: 1703.2313359	total: 20.9s	remaining: 17.6s
543:	learn: 1703.0239437	total: 20.9s	remaining: 17.6s
544:	learn: 1702.8668595	total: 21s	remaining: 17.5s
545:	learn: 1702.7368019	total: 21s	remaining: 17.5s
546:	learn: 1702.5079268	total: 21s	remaining: 17.4s
547:	learn: 1702.4216445	total: 21.1s	remaining: 17.4s
548:	learn: 1702.2927510	total: 21.1s	remaining: 17.4s
549:	learn: 1702.0422832	total: 21.2s	remaining: 17.3s
550:	learn: 1701.8703780	total: 21.2s	remaining: 17.3s
551:	learn: 1701.7375122	total: 21.2s	remaining: 17.2s
552:	learn: 1701.6181835	total: 21.3s	remaining: 17.2s
553:	learn: 1701.5040375	total: 21.3s	remaining: 17.2s
554:	learn: 1701.3217843	total: 21.4s	remaining: 17.1s
555:	learn: 1701.1424688	total: 21.4s	remaining: 17.1s
556:	learn: 1700.8518772	total: 21.5s	remaining: 17.1s
557:	learn: 1700.6148405	total: 21.5s	remaining: 17s
558:	learn: 1700.4257132	total: 21.5s	remaining: 17s
559:	learn: 1700.3250255	total: 21.6s	remaining: 17s
560:	learn: 1700.1770679	total: 21.6s	remaining: 16.9s
561:	learn: 1700.0776019	total: 21.7s	remaining: 16.9s
562:	learn: 1699.9274944	total: 21.7s	remaining: 16.8s
563:	learn: 1699.7862868	total: 21.7s	remaining: 16.8s
564:	learn: 1699.5850276	total: 21.8s	remaining: 16.8s
565:	learn: 1699.3798315	total: 21.8s	remaining: 16.7s
566:	learn: 1699.2410570	total: 21.9s	remaining: 16.7s
567:	learn: 1699.0771009	total: 21.9s	remaining: 16.7s
568:	learn: 1698.9200611	total: 21.9s	remaining: 16.6s
569:	learn: 1698.8371874	total: 22s	remaining: 16.6s
570:	learn: 1698.6693974	total: 22s	remaining: 16.5s
571:	learn: 1698.4458230	total: 22.1s	remaining: 16.5s
572:	learn: 1698.2352671	total: 22.1s	remaining: 16.5s
573:	learn: 1698.0114403	total: 22.1s	remaining: 16.4s
574:	learn: 1697.9620283	total: 22.2s	remaining: 16.4s
575:	learn: 1697.7285149	total: 22.2s	remaining: 16.4s
576:	learn: 1697.5572914	total: 22.2s	remaining: 16.3s
577:	learn: 1697.2472830	total: 22.3s	remaining: 16.3s
578:	learn: 1697.0865106	total: 22.3s	remaining: 16.2s
579:	learn: 1696.8986570	total: 22.4s	remaining: 16.2s
580:	learn: 1696.6495019	total: 22.4s	remaining: 16.2s
581:	learn: 1696.5006199	total: 22.5s	remaining: 16.1s
582:	learn: 1696.3224090	total: 22.5s	remaining: 16.1s
583:	learn: 1696.1328786	total: 22.5s	remaining: 16.1s
584:	learn: 1695.9305801	total: 22.6s	remaining: 16s
585:	learn: 1695.7786486	total: 22.6s	remaining: 16s
586:	learn: 1695.6893543	total: 22.7s	remaining: 15.9s
587:	learn: 1695.6262466	total: 22.7s	remaining: 15.9s
588:	learn: 1695.4207369	total: 22.7s	remaining: 15.9s
589:	learn: 1695.3013015	total: 22.8s	remaining: 15.8s
590:	learn: 1695.1384591	total: 22.8s	remaining: 15.8s
591:	learn: 1694.9932710	total: 22.9s	remaining: 15.8s
592:	learn: 1694.7981887	total: 22.9s	remaining: 15.7s
593:	learn: 1694.6564635	total: 22.9s	remaining: 15.7s
594:	learn: 1694.2926821	total: 23s	remaining: 15.6s
595:	learn: 1694.1911682	total: 23s	remaining: 15.6s
596:	learn: 1694.0801305	total: 23.1s	remaining: 15.6s
597:	learn: 1693.9037712	total: 23.1s	remaining: 15.5s
598:	learn: 1693.6964583	total: 23.1s	remaining: 15.5s
599:	learn: 1693.5709800	total: 23.2s	remaining: 15.4s
600:	learn: 1693.4147224	total: 23.2s	remaining: 15.4s
601:	learn: 1693.2299947	total: 23.3s	remaining: 15.4s
602:	learn: 1693.0585101	total: 23.3s	remaining: 15.3s
603:	learn: 1692.8997478	total: 23.3s	remaining: 15.3s
604:	learn: 1692.7430051	total: 23.4s	remaining: 15.3s
605:	learn: 1692.6348260	total: 23.4s	remaining: 15.2s
606:	learn: 1692.4622758	total: 23.5s	remaining: 15.2s
607:	learn: 1692.3065340	total: 23.5s	remaining: 15.1s
608:	learn: 1692.1929430	total: 23.5s	remaining: 15.1s
609:	learn: 1691.9846774	total: 23.6s	remaining: 15.1s
610:	learn: 1691.8124788	total: 23.6s	remaining: 15s
611:	learn: 1691.4861328	total: 23.7s	remaining: 15s
612:	learn: 1691.3641961	total: 23.7s	remaining: 15s
613:	learn: 1691.2283038	total: 23.7s	remaining: 14.9s
614:	learn: 1691.1405414	total: 23.8s	remaining: 14.9s
615:	learn: 1690.8798935	total: 23.8s	remaining: 14.8s
616:	learn: 1690.7862335	total: 23.9s	remaining: 14.8s
617:	learn: 1690.6369271	total: 23.9s	remaining: 14.8s
618:	learn: 1690.4939458	total: 23.9s	remaining: 14.7s
619:	learn: 1690.4174686	total: 24s	remaining: 14.7s
620:	learn: 1690.2908262	total: 24s	remaining: 14.7s
621:	learn: 1690.0350903	total: 24s	remaining: 14.6s
622:	learn: 1689.9688565	total: 24.1s	remaining: 14.6s
623:	learn: 1689.7280056	total: 24.1s	remaining: 14.5s
624:	learn: 1689.5527882	total: 24.2s	remaining: 14.5s
625:	learn: 1689.3676756	total: 24.2s	remaining: 14.5s
626:	learn: 1689.1537142	total: 24.2s	remaining: 14.4s
627:	learn: 1688.9982296	total: 24.3s	remaining: 14.4s
628:	learn: 1688.7045118	total: 24.3s	remaining: 14.3s
629:	learn: 1688.5835104	total: 24.4s	remaining: 14.3s
630:	learn: 1688.4210762	total: 24.4s	remaining: 14.3s
631:	learn: 1688.3263688	total: 24.5s	remaining: 14.2s
632:	learn: 1688.2075667	total: 24.5s	remaining: 14.2s
633:	learn: 1687.9733608	total: 24.5s	remaining: 14.2s
634:	learn: 1687.8498311	total: 24.6s	remaining: 14.1s
635:	learn: 1687.6652688	total: 24.6s	remaining: 14.1s
636:	learn: 1687.4933491	total: 24.7s	remaining: 14.1s
637:	learn: 1687.3639066	total: 24.7s	remaining: 14s
638:	learn: 1687.2164424	total: 24.7s	remaining: 14s
639:	learn: 1687.1215159	total: 24.8s	remaining: 13.9s
640:	learn: 1686.9672682	total: 24.8s	remaining: 13.9s
641:	learn: 1686.7835279	total: 24.9s	remaining: 13.9s
642:	learn: 1686.6344900	total: 24.9s	remaining: 13.8s
643:	learn: 1686.4368296	total: 25s	remaining: 13.8s
644:	learn: 1686.1283055	total: 25s	remaining: 13.8s
645:	learn: 1686.0319744	total: 25s	remaining: 13.7s
646:	learn: 1685.8784097	total: 25.1s	remaining: 13.7s
647:	learn: 1685.7247837	total: 25.1s	remaining: 13.6s
648:	learn: 1685.6297880	total: 25.2s	remaining: 13.6s
649:	learn: 1685.4069375	total: 25.2s	remaining: 13.6s
650:	learn: 1685.2322691	total: 25.2s	remaining: 13.5s
651:	learn: 1685.0892882	total: 25.3s	remaining: 13.5s
652:	learn: 1684.9853738	total: 25.4s	remaining: 13.5s
653:	learn: 1684.8911601	total: 25.4s	remaining: 13.4s
654:	learn: 1684.7264021	total: 25.5s	remaining: 13.4s
655:	learn: 1684.6448773	total: 25.6s	remaining: 13.4s
656:	learn: 1684.4500992	total: 25.6s	remaining: 13.4s
657:	learn: 1684.2712591	total: 25.7s	remaining: 13.3s
658:	learn: 1684.0837157	total: 25.7s	remaining: 13.3s
659:	learn: 1683.9473053	total: 25.7s	remaining: 13.3s
660:	learn: 1683.8144984	total: 25.8s	remaining: 13.2s
661:	learn: 1683.7403280	total: 25.8s	remaining: 13.2s
662:	learn: 1683.5161112	total: 25.9s	remaining: 13.2s
663:	learn: 1683.3907945	total: 25.9s	remaining: 13.1s
664:	learn: 1683.2898509	total: 25.9s	remaining: 13.1s
665:	learn: 1683.0970672	total: 26s	remaining: 13s
666:	learn: 1683.0061456	total: 26s	remaining: 13s
667:	learn: 1682.9051575	total: 26.1s	remaining: 12.9s
668:	learn: 1682.7057448	total: 26.1s	remaining: 12.9s
669:	learn: 1682.5544938	total: 26.1s	remaining: 12.9s
670:	learn: 1682.3945318	total: 26.2s	remaining: 12.8s
671:	learn: 1682.2167941	total: 26.2s	remaining: 12.8s
672:	learn: 1682.0770457	total: 26.3s	remaining: 12.8s
673:	learn: 1681.8354251	total: 26.3s	remaining: 12.7s
674:	learn: 1681.6784281	total: 26.3s	remaining: 12.7s
675:	learn: 1681.4974934	total: 26.4s	remaining: 12.6s
676:	learn: 1681.3850834	total: 26.4s	remaining: 12.6s
677:	learn: 1681.2517942	total: 26.5s	remaining: 12.6s
678:	learn: 1681.1611254	total: 26.5s	remaining: 12.5s
679:	learn: 1681.0016668	total: 26.6s	remaining: 12.5s
680:	learn: 1680.8935747	total: 26.6s	remaining: 12.5s
681:	learn: 1680.8247735	total: 26.7s	remaining: 12.4s
682:	learn: 1680.7218487	total: 26.7s	remaining: 12.4s
683:	learn: 1680.5864594	total: 26.8s	remaining: 12.4s
684:	learn: 1680.4605058	total: 26.8s	remaining: 12.3s
685:	learn: 1680.2376068	total: 26.8s	remaining: 12.3s
686:	learn: 1680.1758320	total: 26.9s	remaining: 12.2s
687:	learn: 1680.0241639	total: 26.9s	remaining: 12.2s
688:	learn: 1679.8791839	total: 27s	remaining: 12.2s
689:	learn: 1679.7162481	total: 27s	remaining: 12.1s
690:	learn: 1679.5416180	total: 27s	remaining: 12.1s
691:	learn: 1679.4170630	total: 27.1s	remaining: 12s
692:	learn: 1679.2436408	total: 27.1s	remaining: 12s
693:	learn: 1679.1117381	total: 27.2s	remaining: 12s
694:	learn: 1678.9576004	total: 27.2s	remaining: 11.9s
695:	learn: 1678.8058421	total: 27.2s	remaining: 11.9s
696:	learn: 1678.6894377	total: 27.3s	remaining: 11.9s
697:	learn: 1678.5200190	total: 27.3s	remaining: 11.8s
698:	learn: 1678.3965343	total: 27.4s	remaining: 11.8s
699:	learn: 1678.2687540	total: 27.4s	remaining: 11.7s
700:	learn: 1678.0802346	total: 27.4s	remaining: 11.7s
701:	learn: 1677.8958989	total: 27.5s	remaining: 11.7s
702:	learn: 1677.7963547	total: 27.5s	remaining: 11.6s
703:	learn: 1677.6392694	total: 27.6s	remaining: 11.6s
704:	learn: 1677.4352800	total: 27.6s	remaining: 11.5s
705:	learn: 1677.3125238	total: 27.6s	remaining: 11.5s
706:	learn: 1677.1476560	total: 27.7s	remaining: 11.5s
707:	learn: 1677.0367564	total: 27.7s	remaining: 11.4s
708:	learn: 1676.9361584	total: 27.8s	remaining: 11.4s
709:	learn: 1676.8059478	total: 27.8s	remaining: 11.4s
710:	learn: 1676.7154256	total: 27.8s	remaining: 11.3s
711:	learn: 1676.6077365	total: 27.9s	remaining: 11.3s
712:	learn: 1676.4350408	total: 27.9s	remaining: 11.2s
713:	learn: 1676.3364161	total: 27.9s	remaining: 11.2s
714:	learn: 1676.1101897	total: 28s	remaining: 11.2s
715:	learn: 1675.9471705	total: 28s	remaining: 11.1s
716:	learn: 1675.7844364	total: 28.1s	remaining: 11.1s
717:	learn: 1675.6109474	total: 28.1s	remaining: 11s
718:	learn: 1675.4847957	total: 28.1s	remaining: 11s
719:	learn: 1675.3977002	total: 28.2s	remaining: 11s
720:	learn: 1675.3084707	total: 28.2s	remaining: 10.9s
721:	learn: 1675.1707373	total: 28.3s	remaining: 10.9s
722:	learn: 1675.1093974	total: 28.3s	remaining: 10.8s
723:	learn: 1674.8997906	total: 28.3s	remaining: 10.8s
724:	learn: 1674.8447168	total: 28.4s	remaining: 10.8s
725:	learn: 1674.7399367	total: 28.4s	remaining: 10.7s
726:	learn: 1674.6348980	total: 28.4s	remaining: 10.7s
727:	learn: 1674.5470940	total: 28.5s	remaining: 10.6s
728:	learn: 1674.4660068	total: 28.5s	remaining: 10.6s
729:	learn: 1674.3420098	total: 28.6s	remaining: 10.6s
730:	learn: 1674.2147016	total: 28.6s	remaining: 10.5s
731:	learn: 1674.1328517	total: 28.6s	remaining: 10.5s
732:	learn: 1673.9818478	total: 28.7s	remaining: 10.4s
733:	learn: 1673.8285175	total: 28.7s	remaining: 10.4s
734:	learn: 1673.6368248	total: 28.7s	remaining: 10.4s
735:	learn: 1673.4523845	total: 28.8s	remaining: 10.3s
736:	learn: 1673.3858658	total: 28.8s	remaining: 10.3s
737:	learn: 1673.1952204	total: 28.9s	remaining: 10.2s
738:	learn: 1672.9860140	total: 28.9s	remaining: 10.2s
739:	learn: 1672.8757114	total: 28.9s	remaining: 10.2s
740:	learn: 1672.6993988	total: 29s	remaining: 10.1s
741:	learn: 1672.6454698	total: 29s	remaining: 10.1s
742:	learn: 1672.5069602	total: 29.1s	remaining: 10.1s
743:	learn: 1672.4251156	total: 29.1s	remaining: 10s
744:	learn: 1672.3218743	total: 29.1s	remaining: 9.97s
745:	learn: 1672.2356822	total: 29.2s	remaining: 9.93s
746:	learn: 1672.0915272	total: 29.2s	remaining: 9.9s
747:	learn: 1671.8967974	total: 29.3s	remaining: 9.86s
748:	learn: 1671.8407001	total: 29.3s	remaining: 9.81s
749:	learn: 1671.7207630	total: 29.3s	remaining: 9.78s
750:	learn: 1671.4845382	total: 29.4s	remaining: 9.73s
751:	learn: 1671.3925004	total: 29.4s	remaining: 9.69s
752:	learn: 1671.2904745	total: 29.4s	remaining: 9.65s
753:	learn: 1671.1817530	total: 29.5s	remaining: 9.61s
754:	learn: 1671.0265159	total: 29.5s	remaining: 9.58s
755:	learn: 1670.9156212	total: 29.5s	remaining: 9.54s
756:	learn: 1670.8636604	total: 29.6s	remaining: 9.49s
757:	learn: 1670.7288488	total: 29.6s	remaining: 9.46s
758:	learn: 1670.5928166	total: 29.7s	remaining: 9.41s
759:	learn: 1670.4388169	total: 29.7s	remaining: 9.38s
760:	learn: 1670.2313387	total: 29.7s	remaining: 9.34s
761:	learn: 1670.1281205	total: 29.8s	remaining: 9.29s
762:	learn: 1670.0122737	total: 29.8s	remaining: 9.26s
763:	learn: 1669.9326477	total: 29.8s	remaining: 9.21s
764:	learn: 1669.7738884	total: 29.9s	remaining: 9.17s
765:	learn: 1669.5812741	total: 29.9s	remaining: 9.14s
766:	learn: 1669.4864356	total: 29.9s	remaining: 9.09s
767:	learn: 1669.4404012	total: 30s	remaining: 9.05s
768:	learn: 1669.2543329	total: 30s	remaining: 9.02s
769:	learn: 1669.0674034	total: 30s	remaining: 8.97s
770:	learn: 1668.9608453	total: 30.1s	remaining: 8.93s
771:	learn: 1668.8669561	total: 30.1s	remaining: 8.89s
772:	learn: 1668.7490347	total: 30.1s	remaining: 8.85s
773:	learn: 1668.6607003	total: 30.2s	remaining: 8.81s
774:	learn: 1668.5564180	total: 30.2s	remaining: 8.78s
775:	learn: 1668.4903669	total: 30.3s	remaining: 8.73s
776:	learn: 1668.4272804	total: 30.3s	remaining: 8.7s
777:	learn: 1668.3239406	total: 30.3s	remaining: 8.66s
778:	learn: 1668.2659643	total: 30.4s	remaining: 8.62s
779:	learn: 1668.1750383	total: 30.4s	remaining: 8.58s
780:	learn: 1668.0925076	total: 30.4s	remaining: 8.54s
781:	learn: 1668.0232905	total: 30.5s	remaining: 8.5s
782:	learn: 1667.8234460	total: 30.5s	remaining: 8.46s
783:	learn: 1667.5820075	total: 30.6s	remaining: 8.42s
784:	learn: 1667.4734628	total: 30.6s	remaining: 8.38s
785:	learn: 1667.3592407	total: 30.6s	remaining: 8.34s
786:	learn: 1667.2361999	total: 30.7s	remaining: 8.3s
787:	learn: 1667.1536925	total: 30.7s	remaining: 8.26s
788:	learn: 1667.0105916	total: 30.8s	remaining: 8.22s
789:	learn: 1666.9582421	total: 30.8s	remaining: 8.18s
790:	learn: 1666.8017005	total: 30.8s	remaining: 8.14s
791:	learn: 1666.6900655	total: 30.9s	remaining: 8.11s
792:	learn: 1666.5414016	total: 30.9s	remaining: 8.07s
793:	learn: 1666.4293517	total: 30.9s	remaining: 8.03s
794:	learn: 1666.2746960	total: 31s	remaining: 7.99s
795:	learn: 1666.1624518	total: 31s	remaining: 7.95s
796:	learn: 1666.0229459	total: 31.1s	remaining: 7.91s
797:	learn: 1665.9226514	total: 31.1s	remaining: 7.87s
798:	learn: 1665.8103572	total: 31.1s	remaining: 7.83s
799:	learn: 1665.7351465	total: 31.2s	remaining: 7.79s
800:	learn: 1665.6280624	total: 31.2s	remaining: 7.75s
801:	learn: 1665.4438989	total: 31.2s	remaining: 7.71s
802:	learn: 1665.3764375	total: 31.3s	remaining: 7.67s
803:	learn: 1665.2819024	total: 31.3s	remaining: 7.63s
804:	learn: 1665.1838539	total: 31.3s	remaining: 7.59s
805:	learn: 1665.0629147	total: 31.4s	remaining: 7.55s
806:	learn: 1664.9524694	total: 31.4s	remaining: 7.51s
807:	learn: 1664.9092102	total: 31.5s	remaining: 7.47s
808:	learn: 1664.8031237	total: 31.5s	remaining: 7.43s
809:	learn: 1664.7098368	total: 31.5s	remaining: 7.39s
810:	learn: 1664.6523562	total: 31.6s	remaining: 7.35s
811:	learn: 1664.5839101	total: 31.6s	remaining: 7.31s
812:	learn: 1664.4589302	total: 31.6s	remaining: 7.27s
813:	learn: 1664.3875261	total: 31.7s	remaining: 7.24s
814:	learn: 1664.3105962	total: 31.7s	remaining: 7.2s
815:	learn: 1664.2588160	total: 31.7s	remaining: 7.15s
816:	learn: 1664.0818948	total: 31.8s	remaining: 7.11s
817:	learn: 1663.9482962	total: 31.8s	remaining: 7.07s
818:	learn: 1663.7970567	total: 31.8s	remaining: 7.04s
819:	learn: 1663.6541816	total: 31.9s	remaining: 7s
820:	learn: 1663.4795958	total: 31.9s	remaining: 6.96s
821:	learn: 1663.2679883	total: 32s	remaining: 6.92s
822:	learn: 1663.1442857	total: 32s	remaining: 6.88s
823:	learn: 1663.0255818	total: 32s	remaining: 6.84s
824:	learn: 1662.9347651	total: 32.1s	remaining: 6.8s
825:	learn: 1662.7862990	total: 32.1s	remaining: 6.76s
826:	learn: 1662.7249768	total: 32.1s	remaining: 6.72s
827:	learn: 1662.6156137	total: 32.2s	remaining: 6.68s
828:	learn: 1662.5073758	total: 32.2s	remaining: 6.64s
829:	learn: 1662.4122239	total: 32.2s	remaining: 6.6s
830:	learn: 1662.3202180	total: 32.3s	remaining: 6.56s
831:	learn: 1662.1821761	total: 32.3s	remaining: 6.52s
832:	learn: 1662.0872805	total: 32.3s	remaining: 6.49s
833:	learn: 1661.9780387	total: 32.4s	remaining: 6.45s
834:	learn: 1661.8551608	total: 32.4s	remaining: 6.41s
835:	learn: 1661.7299061	total: 32.5s	remaining: 6.37s
836:	learn: 1661.6226776	total: 32.5s	remaining: 6.33s
837:	learn: 1661.5175607	total: 32.5s	remaining: 6.29s
838:	learn: 1661.4005224	total: 32.6s	remaining: 6.25s
839:	learn: 1661.2881224	total: 32.6s	remaining: 6.21s
840:	learn: 1661.1634719	total: 32.6s	remaining: 6.17s
841:	learn: 1661.0212829	total: 32.7s	remaining: 6.13s
842:	learn: 1660.9705221	total: 32.7s	remaining: 6.09s
843:	learn: 1660.8269297	total: 32.8s	remaining: 6.05s
844:	learn: 1660.7710185	total: 32.8s	remaining: 6.01s
845:	learn: 1660.7101926	total: 32.8s	remaining: 5.98s
846:	learn: 1660.6050936	total: 32.9s	remaining: 5.94s
847:	learn: 1660.4695481	total: 32.9s	remaining: 5.9s
848:	learn: 1660.3535390	total: 32.9s	remaining: 5.86s
849:	learn: 1660.2096117	total: 33s	remaining: 5.82s
850:	learn: 1660.0370291	total: 33s	remaining: 5.78s
851:	learn: 1659.8604050	total: 33s	remaining: 5.74s
852:	learn: 1659.7291276	total: 33.1s	remaining: 5.7s
853:	learn: 1659.6404716	total: 33.1s	remaining: 5.66s
854:	learn: 1659.5241863	total: 33.2s	remaining: 5.62s
855:	learn: 1659.4011182	total: 33.2s	remaining: 5.58s
856:	learn: 1659.3228199	total: 33.2s	remaining: 5.54s
857:	learn: 1659.2441871	total: 33.3s	remaining: 5.5s
858:	learn: 1659.1544882	total: 33.3s	remaining: 5.46s
859:	learn: 1659.0466642	total: 33.3s	remaining: 5.42s
860:	learn: 1658.9797395	total: 33.4s	remaining: 5.38s
861:	learn: 1658.8539859	total: 33.4s	remaining: 5.35s
862:	learn: 1658.7501992	total: 33.4s	remaining: 5.31s
863:	learn: 1658.6791128	total: 33.5s	remaining: 5.27s
864:	learn: 1658.6117976	total: 33.5s	remaining: 5.23s
865:	learn: 1658.5427267	total: 33.5s	remaining: 5.19s
866:	learn: 1658.3896135	total: 33.6s	remaining: 5.15s
867:	learn: 1658.2685957	total: 33.6s	remaining: 5.11s
868:	learn: 1658.1483773	total: 33.7s	remaining: 5.07s
869:	learn: 1658.1136286	total: 33.7s	remaining: 5.03s
870:	learn: 1657.9893127	total: 33.7s	remaining: 4.99s
871:	learn: 1657.9530228	total: 33.8s	remaining: 4.95s
872:	learn: 1657.8841781	total: 33.8s	remaining: 4.92s
873:	learn: 1657.8441870	total: 33.8s	remaining: 4.88s
874:	learn: 1657.7298265	total: 33.9s	remaining: 4.84s
875:	learn: 1657.5645105	total: 33.9s	remaining: 4.8s
876:	learn: 1657.4149168	total: 33.9s	remaining: 4.76s
877:	learn: 1657.2922775	total: 34s	remaining: 4.72s
878:	learn: 1657.2065193	total: 34s	remaining: 4.68s
879:	learn: 1657.0564731	total: 34s	remaining: 4.64s
880:	learn: 1656.9803490	total: 34.1s	remaining: 4.6s
881:	learn: 1656.8775548	total: 34.1s	remaining: 4.56s
882:	learn: 1656.7923397	total: 34.1s	remaining: 4.52s
883:	learn: 1656.6830786	total: 34.2s	remaining: 4.49s
884:	learn: 1656.5719416	total: 34.2s	remaining: 4.45s
885:	learn: 1656.4009333	total: 34.3s	remaining: 4.41s
886:	learn: 1656.2932785	total: 34.3s	remaining: 4.37s
887:	learn: 1656.1893944	total: 34.3s	remaining: 4.33s
888:	learn: 1656.0869241	total: 34.4s	remaining: 4.29s
889:	learn: 1655.9653013	total: 34.4s	remaining: 4.25s
890:	learn: 1655.8005605	total: 34.4s	remaining: 4.21s
891:	learn: 1655.7291182	total: 34.5s	remaining: 4.17s
892:	learn: 1655.6244260	total: 34.5s	remaining: 4.14s
893:	learn: 1655.5210290	total: 34.6s	remaining: 4.1s
894:	learn: 1655.4644645	total: 34.6s	remaining: 4.06s
895:	learn: 1655.4008028	total: 34.6s	remaining: 4.02s
896:	learn: 1655.3411920	total: 34.7s	remaining: 3.98s
897:	learn: 1655.2021571	total: 34.7s	remaining: 3.94s
898:	learn: 1655.1483780	total: 34.7s	remaining: 3.9s
899:	learn: 1655.0558898	total: 34.8s	remaining: 3.86s
900:	learn: 1655.0051558	total: 34.8s	remaining: 3.82s
901:	learn: 1654.9429659	total: 34.8s	remaining: 3.79s
902:	learn: 1654.8447666	total: 34.9s	remaining: 3.75s
903:	learn: 1654.6648911	total: 34.9s	remaining: 3.71s
904:	learn: 1654.5540094	total: 35s	remaining: 3.67s
905:	learn: 1654.4748853	total: 35s	remaining: 3.63s
906:	learn: 1654.3005618	total: 35s	remaining: 3.59s
907:	learn: 1654.2435769	total: 35.1s	remaining: 3.55s
908:	learn: 1654.1453072	total: 35.1s	remaining: 3.51s
909:	learn: 1654.0064250	total: 35.1s	remaining: 3.48s
910:	learn: 1653.9616946	total: 35.2s	remaining: 3.44s
911:	learn: 1653.7641910	total: 35.2s	remaining: 3.4s
912:	learn: 1653.6989258	total: 35.3s	remaining: 3.36s
913:	learn: 1653.6453149	total: 35.3s	remaining: 3.32s
914:	learn: 1653.5486930	total: 35.3s	remaining: 3.28s
915:	learn: 1653.4114197	total: 35.4s	remaining: 3.24s
916:	learn: 1653.3095058	total: 35.4s	remaining: 3.2s
917:	learn: 1653.1816451	total: 35.4s	remaining: 3.17s
918:	learn: 1653.0882668	total: 35.5s	remaining: 3.13s
919:	learn: 1652.9353790	total: 35.5s	remaining: 3.09s
920:	learn: 1652.7593966	total: 35.5s	remaining: 3.05s
921:	learn: 1652.6690695	total: 35.6s	remaining: 3.01s
922:	learn: 1652.6176983	total: 35.6s	remaining: 2.97s
923:	learn: 1652.5320858	total: 35.7s	remaining: 2.93s
924:	learn: 1652.4458549	total: 35.7s	remaining: 2.89s
925:	learn: 1652.3911495	total: 35.7s	remaining: 2.85s
926:	learn: 1652.2986861	total: 35.8s	remaining: 2.82s
927:	learn: 1652.1576076	total: 35.8s	remaining: 2.78s
928:	learn: 1652.0645619	total: 35.8s	remaining: 2.74s
929:	learn: 1651.9870058	total: 35.9s	remaining: 2.7s
930:	learn: 1651.9224912	total: 35.9s	remaining: 2.66s
931:	learn: 1651.8234396	total: 36s	remaining: 2.62s
932:	learn: 1651.7612482	total: 36s	remaining: 2.58s
933:	learn: 1651.7129760	total: 36s	remaining: 2.54s
934:	learn: 1651.6155328	total: 36.1s	remaining: 2.51s
935:	learn: 1651.4623266	total: 36.1s	remaining: 2.47s
936:	learn: 1651.3770334	total: 36.1s	remaining: 2.43s
937:	learn: 1651.2849192	total: 36.2s	remaining: 2.39s
938:	learn: 1651.2420981	total: 36.2s	remaining: 2.35s
939:	learn: 1651.1610870	total: 36.2s	remaining: 2.31s
940:	learn: 1651.0465573	total: 36.3s	remaining: 2.27s
941:	learn: 1650.9578085	total: 36.3s	remaining: 2.24s
942:	learn: 1650.8343009	total: 36.4s	remaining: 2.2s
943:	learn: 1650.7619780	total: 36.4s	remaining: 2.16s
944:	learn: 1650.5560382	total: 36.5s	remaining: 2.12s
945:	learn: 1650.5123775	total: 36.5s	remaining: 2.08s
946:	learn: 1650.4054168	total: 36.6s	remaining: 2.04s
947:	learn: 1650.2885719	total: 36.6s	remaining: 2.01s
948:	learn: 1650.1734628	total: 36.6s	remaining: 1.97s
949:	learn: 1650.0581258	total: 36.7s	remaining: 1.93s
950:	learn: 1649.9169632	total: 36.7s	remaining: 1.89s
951:	learn: 1649.8252828	total: 36.7s	remaining: 1.85s
952:	learn: 1649.7296482	total: 36.8s	remaining: 1.81s
953:	learn: 1649.6505258	total: 36.8s	remaining: 1.77s
954:	learn: 1649.5890916	total: 36.9s	remaining: 1.74s
955:	learn: 1649.4368426	total: 36.9s	remaining: 1.7s
956:	learn: 1649.3196911	total: 36.9s	remaining: 1.66s
957:	learn: 1649.2532661	total: 37s	remaining: 1.62s
958:	learn: 1649.2175616	total: 37s	remaining: 1.58s
959:	learn: 1649.0834561	total: 37s	remaining: 1.54s
960:	learn: 1649.0037356	total: 37.1s	remaining: 1.5s
961:	learn: 1648.8409269	total: 37.1s	remaining: 1.47s
962:	learn: 1648.7188453	total: 37.1s	remaining: 1.43s
963:	learn: 1648.6059574	total: 37.2s	remaining: 1.39s
964:	learn: 1648.5285705	total: 37.2s	remaining: 1.35s
965:	learn: 1648.3602970	total: 37.3s	remaining: 1.31s
966:	learn: 1648.1969677	total: 37.3s	remaining: 1.27s
967:	learn: 1648.0400003	total: 37.3s	remaining: 1.23s
968:	learn: 1647.9618039	total: 37.4s	remaining: 1.2s
969:	learn: 1647.8484358	total: 37.4s	remaining: 1.16s
970:	learn: 1647.7433993	total: 37.5s	remaining: 1.12s
971:	learn: 1647.5803916	total: 37.5s	remaining: 1.08s
972:	learn: 1647.4964278	total: 37.5s	remaining: 1.04s
973:	learn: 1647.3827909	total: 37.6s	remaining: 1s
974:	learn: 1647.2962397	total: 37.6s	remaining: 964ms
975:	learn: 1647.1139250	total: 37.6s	remaining: 926ms
976:	learn: 1646.9720188	total: 37.7s	remaining: 887ms
977:	learn: 1646.8701372	total: 37.7s	remaining: 849ms
978:	learn: 1646.7621446	total: 37.8s	remaining: 810ms
979:	learn: 1646.5576286	total: 37.8s	remaining: 772ms
980:	learn: 1646.4652620	total: 37.8s	remaining: 733ms
981:	learn: 1646.3408469	total: 37.9s	remaining: 694ms
982:	learn: 1646.1384320	total: 37.9s	remaining: 656ms
983:	learn: 1646.0612687	total: 38s	remaining: 617ms
984:	learn: 1645.8514629	total: 38s	remaining: 579ms
985:	learn: 1645.7763870	total: 38.1s	remaining: 540ms
986:	learn: 1645.7180827	total: 38.1s	remaining: 502ms
987:	learn: 1645.5174151	total: 38.1s	remaining: 463ms
988:	learn: 1645.4243853	total: 38.2s	remaining: 424ms
989:	learn: 1645.3219164	total: 38.2s	remaining: 386ms
990:	learn: 1645.1874401	total: 38.2s	remaining: 347ms
991:	learn: 1645.1293047	total: 38.3s	remaining: 309ms
992:	learn: 1645.0970315	total: 38.3s	remaining: 270ms
993:	learn: 1644.9962728	total: 38.3s	remaining: 231ms
994:	learn: 1644.8872127	total: 38.4s	remaining: 193ms
995:	learn: 1644.8296122	total: 38.4s	remaining: 154ms
996:	learn: 1644.7116419	total: 38.5s	remaining: 116ms
997:	learn: 1644.6253627	total: 38.5s	remaining: 77.2ms
998:	learn: 1644.4871057	total: 38.5s	remaining: 38.6ms
999:	learn: 1644.4227966	total: 38.6s	remaining: 0us
Модель   : CatBoostRegressor
RMSE     : 1726.4647502068021
Время    : 204.62451720237732 секунд
Параметры:
 Pipeline(steps=[('preprocessor',
                 ColumnTransformer(transformers=[('pipeline',
                                                  Pipeline(steps=[('standardscaler',
                                                                   StandardScaler())]),
                                                  Index(['vehicle_type', 'registration_year', 'gearbox', 'power', 'model',
       'kilometer', 'fuel_type', 'brand', 'repaired'],
      dtype='object'))])),
                ('regressor',
                 <catboost.core.CatBoostRegressor object at 0x7fa94bf40310>)])

--------------------
In [47]:
# LGBMRegressor
data_grids, data_times = grids_LGBMRegressor(
    features_train, 
    features_test, 
    target_train, 
    target_test, 
    data_grids, 
    data_times
)
print_model_result(data_grids, data_times, 'LGBMRegressor')

Модель   : LGBMRegressor
RMSE     : 4384.374489966158
Время    : 322.27989077568054 секунд
Параметры:
 Pipeline(steps=[('preprocessor',
                 ColumnTransformer(transformers=[('pipeline',
                                                  Pipeline(steps=[('standardscaler',
                                                                   StandardScaler())]),
                                                  Index(['vehicle_type', 'registration_year', 'gearbox', 'power', 'model',
       'kilometer', 'fuel_type', 'brand', 'repaired'],
      dtype='object'))])),
                ('regressor',
                 LGBMRegressor(max_depth=1, n_estimators=1, random_state=42))])

--------------------

Выбор лучшей модели¶

In [48]:
# Лучшая модель из расчета RMSE
data_grids_best = data_grids[0]
data_times_best = data_times[0]
n = 0
for i in range(0, len(data_grids)-1):
    if data_grids[i].best_score_ < data_grids_best.best_score_: 
    #if (data_grids[i].best_score_ < data_grids_best.best_score_) & (data_times[i] < data_times_best): 
        data_grids_best = data_grids[i]
        data_times_best = data_times[i]

print('Лучшее время : ', data_times_best)
print('Лучшее RMSE  : ', data_grids_best.best_score_)
print('Лучшая модель: ')
data_grids_best

Лучшее время :  204.62451720237732
Лучшее RMSE  :  1726.4647502068021
Лучшая модель:
Out[48]:
HalvingGridSearchCV(cv=4, error_score='raise',
                    estimator=Pipeline(steps=[('preprocessor',
                                               ColumnTransformer(transformers=[('pipeline',
                                                                                Pipeline(steps=[('standardscaler',
                                                                                                 StandardScaler())]),
                                                                                Index(['vehicle_type', 'registration_year', 'gearbox', 'power', 'model',
       'kilometer', 'fuel_type', 'brand', 'repaired'],
      dtype='object'))])),
                                              ('regressor',
                                               <catboost.core.CatBoostRegressor object at 0x7fa950219220>)]),
                    n_jobs=-1,
                    param_grid=[{'regressor': [<catboost.core.CatBoostRegressor object at 0x7fa950219220>]}],
                    random_state=42,
                    refit=<function _refit_callable at 0x7fa95cd84f70>,
                    scoring=make_scorer(mean_squared_error, squared=False))
In [49]:
start_time = time.time()

# Предсказание лучшей модели
predict = data_grids_best.predict(features_test)

finish_time = time.time()
funtion_time = finish_time - start_time

# Расчет RMSE и времени выполнения предсказания
print('RMSE =', mean_squared_error(target_test, predict, squared=False))
print(f'Время предсказания = {funtion_time} секунд')

RMSE = 1709.3459181142257
Время предсказания = 0.3003842830657959 секунд

Анализ моделей¶

Для использования в данном проекте были выбраны следующие модели: LinearRegression в качестве дамми-модели, DecisionTreeRegressor, SGDRegressor, CatBoostRegressor от Яндекса и LGBMRegressor.

По критерию минимального параметра RMSE в качестве лучшей модели после обучения была выбрана модель CatBoostRegressor с параметрами по-умолчанию. Ее показатели на обучающей выборке:

RMSE : 1726.4647502068021
Время: 178.93473863601685 секунд

При предсказании на тестовых данных эта модель показала хорошие результаты:

RMSE : 1709.3459181142257
Время: 0.128037691116333 секунд

RMSE оказался близким к тому, что было получено при обучении, а время предсказания многократно меньше обучения. Учитывая тот факт, что заказчика интересуют время обучения и предсказания, но отсутствуют точные критерии требуемого времени, а RMSE выбранной модели, как и требуется, меньше 2500, то для эксплуатации предлагается модель CatBoostRegressor с параметрами по-умолчанию.

Выводы проекта¶

Цель проекта достигнута. Выбрана модель CatBoostRegressor() с параметрами по-умолчанию для предсказания цены подержанных автомобилей по их параметрам. Для этого были выполнены следующие действия:

  1. Загружены и проканализированы данные.
  2. Выполнена предварительная обработка данных.
  3. Данные подготовлены к машинному обучению.
  4. Обучены несколько моделей, включая LGBMRegressor и одна не бустинговая модель.
  5. Выбрана лучшая модель по критериям заказчика, включая минимальное время обучения и предсказания, а также RMSE меньшее 2500.
  6. Проанализированы результаты обучения и предсказания и сделаны выводы.

Чек-лист проверки¶

Поставьте ‘x’ в выполненных пунктах. Далее нажмите Shift+Enter.

  • [x] Jupyter Notebook открыт
  • [x] Весь код выполняется без ошибок
  • [x] Ячейки с кодом расположены в порядке исполнения
  • [x] Выполнена загрузка и подготовка данных
  • [x] Выполнено обучение моделей
  • [x] Есть анализ скорости работы и качества моделей

Политика конфиденциальности

Продолжая использовать данный сайт вы подтверждаете свое согласие с условиями его политики конфиденциальности. Подробнее…




Администрация и владельцы данного информационного ресурса не несут ответственности за возможные последствия, связанные с использованием информации, размещенной на нем.


Все права защищены. При копировании материалов сайта обязательно указывать ссылку на © Microsegment.ru (2020-2025)