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[人工智能]数据分析的N种特征方法实例

1 数据输入

import pandas as pd

df = pd.DataFrame({
    'student_id': [1,2,3,4,5,6,7],
    'country': ['China', 'USA', 'UK', 'Japan', 'Korea', 'China', 'USA'],
    'education': ['Master', 'Bachelor', 'Bachelor', 'Master', 'PHD', 'PHD', 'Bachelor'],
    'target': [1, 0, 1, 0, 1, 0, 1]
})
df.head(10)

	student_id	country	education	target
0	1	China	Master	1
1	2	USA	Bachelor	0
2	3	UK	Bachelor	1
3	4	Japan	Master	0
4	5	Korea	PHD	1
5	6	China	PHD	0
6	7	USA	Bachelor	1

2 特征编码

2.1 One Hot Encoding 独热编码

pd.get_dummies(df, columns=['education'])

	student_id	country	target	education_Bachelor	education_Master	education_PHD
0	1	China	1	0	1	0
1	2	USA	0	1	0	0
2	3	UK	1	1	0	0
3	4	Japan	0	0	1	0
4	5	Korea	1	0	0	1
5	6	China	0	0	0	1
6	7	USA	1	1	0	0

from sklearn.preprocessing import OneHotEncoder
ohe = OneHotEncoder()
ohe.fit_transform(df[['country']]).toarray()

array([[1., 0., 0., 0., 0.],
       [0., 0., 0., 0., 1.],
       [0., 0., 0., 1., 0.],
       [0., 1., 0., 0., 0.],
       [0., 0., 1., 0., 0.],
       [1., 0., 0., 0., 0.],
       [0., 0., 0., 0., 1.]])

2.2 Label Encoding 标签编码

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df['country_LabelEncoder'] = le.fit_transform(df['country'])
df.head(10)

	student_id	country	education	target	country_LabelEncoder
0	1	China	Master	1	0
1	2	USA	Bachelor	0	4
2	3	UK	Bachelor	1	3
3	4	Japan	Master	0	1
4	5	Korea	PHD	1	2
5	6	China	PHD	0	0
6	7	USA	Bachelor	1	4

df['country_LabelEncoder'] = pd.factorize(df['country'])[0]
df.head(10)

	student_id	country	education	target	country_LabelEncoder
0	1	China	Master	1	0
1	2	USA	Bachelor	0	1
2	3	UK	Bachelor	1	2
3	4	Japan	Master	0	3
4	5	Korea	PHD	1	4
5	6	China	PHD	0	0
6	7	USA	Bachelor	1	1

pd.factorize(df['country'])

(array([0, 1, 2, 3, 4, 0, 1]),
 Index(['China', 'USA', 'UK', 'Japan', 'Korea'], dtype='object'))

2.3 Ordinal Encoding 顺序编码

df['education'] = df['education'].map(
                    {'Bachelor': 1, 
                    'Master': 2, 
                    'PHD': 3})
df.head(10)

	student_id	country	education	target	country_LabelEncoder
0	1	China	2	1	0
1	2	USA	1	0	1
2	3	UK	1	1	2
3	4	Japan	2	0	3
4	5	Korea	3	1	4
5	6	China	3	0	0
6	7	USA	1	1	1

2.4 Binary Encoding 二进制编码

import category_encoders as ce
encoder = ce.BinaryEncoder(cols= ['country'])

pd.concat([df, encoder.fit_transform(df['country']).iloc[:, 1:]], axis=1)

	student_id	country	education	target	country_1	country_2	country_3
0	1	China	Master	1	0	0	1
1	2	USA	Bachelor	0	0	1	0
2	3	UK	Bachelor	1	0	1	1
3	4	Japan	Master	0	1	0	0
4	5	Korea	PHD	1	1	0	1
5	6	China	PHD	0	0	0	1
6	7	USA	Bachelor	1	0	1	0

2.5 Frequency Encoding、Count Encoding

df['country_count'] = df['country'].map(df['country'].value_counts()) / len(df)
df.head(10)

	student_id	country	education	target	country_count
0	1	China	Master	1	0.285714
1	2	USA	Bachelor	0	0.285714
2	3	UK	Bachelor	1	0.142857
3	4	Japan	Master	0	0.142857
4	5	Korea	PHD	1	0.142857
5	6	China	PHD	0	0.285714
6	7	USA	Bachelor	1	0.285714

df['country_count'] = df['country'].map(df['country'].value_counts())
df.head(10)

	student_id	country	education	target	country_count
0	1	China	Master	1	2
1	2	USA	Bachelor	0	2
2	3	UK	Bachelor	1	1
3	4	Japan	Master	0	1
4	5	Korea	PHD	1	1
5	6	China	PHD	0	2
6	7	USA	Bachelor	1	2

2.6 Mean/Target Encoding

df.groupby(['country'])['target'].mean()

country
China    0.5
Japan    0.0
Korea    1.0
UK       1.0
USA      0.5
Name: target, dtype: float64

df['country_target'] = df['country'].map(df.groupby(['country'])['target'].mean())
df.head(10)

	student_id	country	education	target	country_target
0	1	China	Master	1	0.5
1	2	USA	Bachelor	0	0.5
2	3	UK	Bachelor	1	1.0
3	4	Japan	Master	0	0.0
4	5	Korea	PHD	1	1.0
5	6	China	PHD	0	0.5
6	7	USA	Bachelor	1	0.5

df = pd.DataFrame({
    'student_id': [1,2,3,4,5,6,7],
    'country': ['China', 'USA', 'UK', 'Japan', 'Korea', 'China', 'USA'],
    'education': ['Master', 'Bachelor', 'Bachelor', 'Master', 'PHD', 'PHD', 'Bachelor'],
    'age': [34.5, 28.9, 19.5, 23.6, 19.8, 29.8, 31.7],
    'target': [1, 0, 1, 0, 1, 0, 1]
})
df.head(10)

	student_id	country	education	age	target
0	1	China	Master	34.5	1
1	2	USA	Bachelor	28.9	0
2	3	UK	Bachelor	19.5	1
3	4	Japan	Master	23.6	0
4	5	Korea	PHD	19.8	1
5	6	China	PHD	29.8	0
6	7	USA	Bachelor	31.7	1

df['age_round1'] = df['age'].round()
df['age_round2'] = (df['age'] / 10).astype(int)
df.head(10)

	student_id	country	education	age	target	age_round1	age_round2
0	1	China	Master	34.5	1	34.0	3
1	2	USA	Bachelor	28.9	0	29.0	2
2	3	UK	Bachelor	19.5	1	20.0	1
3	4	Japan	Master	23.6	0	24.0	2
4	5	Korea	PHD	19.8	1	20.0	1
5	6	China	PHD	29.8	0	30.0	2
6	7	USA	Bachelor	31.7	1	32.0	3

df['age_<20'] = (df['age'] <= 20).astype(int)
df['age_20-25'] = ((df['age'] > 20) & (df['age'] <=25)).astype(int)
df['age_20-25'] = ((df['age'] > 25) & (df['age'] <= 30)).astype(int)
df['age_>30'] = (df['age'] > 30).astype(int)
df.head(10)

	student_id	country	education	age	target	age_<20	age_20-25	age_>30
0	1	China	Master	34.5	1	0	0	1
1	2	USA	Bachelor	28.9	0	0	1	0
2	3	UK	Bachelor	19.5	1	1	0	0
3	4	Japan	Master	23.6	0	0	0	0
4	5	Korea	PHD	19.8	1	1	0	0
5	6	China	PHD	29.8	0	0	1	0
6	7	USA	Bachelor	31.7	1	0	0	1

df

	student_id	country	education	age	target	age_<20	age_20-25	age_>30
0	1	China	Master	34.5	1	0	0	1
1	2	USA	Bachelor	28.9	0	0	1	0
2	3	UK	Bachelor	19.5	1	1	0	0
3	4	Japan	Master	23.6	0	0	0	0
4	5	Korea	PHD	19.8	1	1	0	0
5	6	China	PHD	29.8	0	0	1	0
6	7	USA	Bachelor	31.7	1	0	0	1

3 绘图

用具体的取值衡量特征的重要性

%pylab inline
import numpy as np
from sklearn.ensemble import RandomForestRegressor

from sklearn.datasets import load_boston
data = load_boston()

rf = RandomForestRegressor()
rf.fit(data.data, data.target);
print(rf.feature_importances_)

plt.figure(figsize=(12, 6))
plt.bar(range(1, 14), rf.feature_importances_)
_ = plt.xticks(range(1, 14), data.feature_names)

Populating the interactive namespace from numpy and matplotlib
[0.03932896 0.00111874 0.00558259 0.00072371 0.0233215  0.43068769
 0.01233092 0.06505465 0.00364571 0.01419684 0.01678173 0.0108236
 0.37640336]

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import numpy as np
from sklearn.ensemble import RandomForestRegressor

from lightgbm import LGBMRegressor
data = load_boston()

clf = LGBMRegressor()
clf.fit(data.data, data.target)

plt.figure(figsize=(12, 6))
plt.bar(range(1, 14), clf.feature_importances_)
_ = plt.xticks(range(1, 14), data.feature_names)

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import numpy as np
from sklearn.ensemble import RandomForestRegressor

from xgboost import XGBRegressor
data = load_boston()

clf = XGBRegressor()
clf.fit(data.data, data.target)

plt.figure(figsize=(12, 6))
plt.bar(range(1, 14), clf.feature_importances_)
_ = plt.xticks(range(1, 14), data.feature_names)

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4 Two-Simg实例代码

房价预测，在这整个比赛中的工作。

import os
import sys
import operator
import numpy as np
import pandas as pd
from scipy import sparse
import random


import xgboost as xgb

# 支持sklearn接口
from xgboost import XGBClassifier, XGBRegressor
# 分类器 以 Classifier 结尾
# 回归器 以 Regressor 结尾

from sklearn import model_selection, preprocessing, ensemble
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import log_loss
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from collections import defaultdict, Counter

def runXGB(train_X, train_y, val_X, val_y=None, test_X=None, 
           feature_names=None, seed_val=0, num_rounds=1000):
    param = {}
    
    
    param['objective'] = 'multi:softprob'
    
    param['eta'] = 0.03
    param['max_depth'] = 6
    param['silent'] = 0
    param['num_class'] = 3
    param['eval_metric'] = "mlogloss"
    param['min_child_weight'] = 1
    param['subsample'] = 0.7
    param['colsample_bytree'] = 0.7
    param['seed'] = seed_val
    param['nthread'] = 12 # cpu核数
    num_rounds = num_rounds

    plst = list(param.items())
    xgtrain = xgb.DMatrix(train_X, label=train_y)

    if val_y is not None:
        xgval = xgb.DMatrix(val_X, label=val_y)
        watchlist = [ (xgtrain,'train'), (xgval, 'val') ]
        # 原生xgboost训练
        model = xgb.train(plst, xgtrain, num_rounds, watchlist, early_stopping_rounds=50)
    else:
        model = xgb.train(plst, xgtrain, num_rounds)
    xgtest = xgb.DMatrix(test_X)
    
    pred_test_y = model.predict(xgtest)
    return pred_test_y, model

这里需要下载训练和测试的文件，看这里。

train_df = pd.read_json('../input/train.json.zip', compression='zip')
test_df = pd.read_json('../input/test.json.zip', compression='zip')

features_to_use  = ["bathrooms", "bedrooms", "latitude", "longitude", "price"]

mean_price = int(train_df['price'].mean())
test_df.loc[test_df['price']<200,'price'] = mean_price
train_df.loc[train_df['price']<200,'price'] = mean_price

train_test = pd.concat([train_df, test_df], 0,sort=False)

features = train_test[["features"]].apply(
    lambda _: [list(map(str.strip, map(str.lower, x))) for x in _])


n = 5

feature_counts = Counter()
for feature in features.features:
    feature_counts.update(feature)
feature = sorted([k for (k,v) in feature_counts.items() if v > n])
feature[:10]


def clean(s):
    x = s.replace("-", "")
    x = x.replace(" ", "")
    x = x.replace("24/7", "24")
    x = x.replace("24hr", "24")
    x = x.replace("24-hour", "24")
    x = x.replace("24hour", "24")
    x = x.replace("24 hour", "24")
    x = x.replace("common", "cm")
    x = x.replace("concierge", "doorman")
    x = x.replace("bicycle", "bike")
    x = x.replace("pets:cats", "cats")
    x = x.replace("allpetsok", "pets")
    x = x.replace("dogs", "pets")
    x = x.replace("private", "pv")
    x = x.replace("deco", "dc")
    x = x.replace("decorative", "dc")
    x = x.replace("onsite", "os")
    x = x.replace("outdoor", "od")
    x = x.replace("ss appliances", "stainless")
    return x

def feature_hash(x):
    cleaned = clean(x, uniq)
    key = cleaned[:4].strip()
    return key


key2original = defaultdict(list)
k = 4
for f in feature:
    cleaned = clean(f)
    key = cleaned[:k].strip()

    key2original[key].append(f)

    
def to_tuples():
    for f in feature:
        key = clean(f)[:k].strip()
        yield (f, key2original[key][0])
        
deduped = list(to_tuples())
df = pd.DataFrame(deduped, columns=["original_feature", "unique_feature"])

dict_rep_features = pd.Series(df['unique_feature'].values, df['original_feature'].values)

test_df['features'] = test_df['features'].apply(lambda x: list(map(str.strip, map(str.lower, x))))\
                    .apply(lambda x: [dict_rep_features[i] for i in x if i in dict_rep_features.index])\
                    .apply(lambda x: list(set(x)))

train_df['features'] = train_df['features'].apply(lambda x: list(map(str.strip, map(str.lower, x))))\
                    .apply(lambda x: [dict_rep_features[i] for i in x if i in dict_rep_features.index])\
                    .apply(lambda x: list(set(x)))

import math
def cart2rho(x, y):
    rho = np.sqrt(x**2 + y**2)
    return rho


def cart2phi(x, y):
    phi = np.arctan2(y, x)
    return phi


def rotation_x(row, alpha):
    x = row['latitude']
    y = row['longitude']
    return x*math.cos(alpha) + y*math.sin(alpha)


def rotation_y(row, alpha):
    x = row['latitude']
    y = row['longitude']
    return y*math.cos(alpha) - x*math.sin(alpha)


def add_rotation(degrees, df):
    namex = "rot" + str(degrees) + "_X"
    namey = "rot" + str(degrees) + "_Y"

    df['num_' + namex] = df.apply(lambda row: rotation_x(row, math.pi/(180/degrees)), axis=1)
    df['num_' + namey] = df.apply(lambda row: rotation_y(row, math.pi/(180/degrees)), axis=1)

    return df

def operate_on_coordinates(tr_df, te_df):
    for df in [tr_df, te_df]:
        #polar coordinates system
        df["num_rho"] = df.apply(lambda x: cart2rho(x["latitude"] - 40.78222222, x["longitude"]+73.96527777), axis=1)
        df["num_phi"] = df.apply(lambda x: cart2phi(x["latitude"] - 40.78222222, x["longitude"]+73.96527777), axis=1)
        #rotations
        for angle in [15,30,45,60]:
            df = add_rotation(angle, df)

    return tr_df, te_df

train_df, test_df = operate_on_coordinates(train_df, test_df)

features_to_use.extend(['num_rho', 'num_phi', 'num_rot15_X', 'num_rot15_Y', 'num_rot30_X',
       'num_rot30_Y', 'num_rot45_X', 'num_rot45_Y', 'num_rot60_X',
       'num_rot60_Y'])

import re

def cap_share(x):
    return sum(1 for c in x if c.isupper())/float(len(x)+1)

for df in [train_df, test_df]:
    # do you think that users might feel annoyed BY A DESCRIPTION THAT IS SHOUTING AT THEM?
    df['num_cap_share'] = df['description'].apply(cap_share)
    
    # how long in lines the desc is?
    df['num_nr_of_lines'] = df['description'].apply(lambda x: x.count('<br /><br />'))
   
    # is the description redacted by the website?        
    df['num_redacted'] = 0
    df['num_redacted'].loc[df['description'].str.contains('website_redacted')] = 1

    
    # can we contact someone via e-mail to ask for the details?
    df['num_email'] = 0
    df['num_email'].loc[df['description'].str.contains('@')] = 1
    
    #and... can we call them?
    
    reg = re.compile(".*?(\(?\d{3}\D{0,3}\d{3}\D{0,3}\d{4}).*?", re.S)
    def try_and_find_nr(description):
        if reg.match(description) is None:
            return 0
        return 1

    df['num_phone_nr'] = df['description'].apply(try_and_find_nr)

    


features_to_use.extend(['num_cap_share', 'num_nr_of_lines', 'num_redacted',
       'num_email', 'num_phone_nr'])

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.fromiter(generator)) or the python sum builtin instead.
  after removing the cwd from sys.path.
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:15: DeprecationWarning: 
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing

See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated
  from ipykernel import kernelapp as app
/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py:190: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  self._setitem_with_indexer(indexer, value)
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:20: DeprecationWarning: 
.ix is deprecated. Please use
.loc for label based indexing or
.iloc for positional indexing

See the documentation here:
http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated

# count of photos #
train_df["num_photos"] = train_df["photos"].apply(len)  #photos原始是list类型
test_df["num_photos"] = test_df["photos"].apply(len)

# count of "features" #
train_df["num_features"] = train_df["features"].apply(len)
test_df["num_features"] = test_df["features"].apply(len)

# count of words present in description column #
train_df["num_description_words"] = train_df["description"].apply(lambda x: len(x.split(" ")))  #使用空白符进行分隔，统计有多少个字符类型
test_df["num_description_words"] = test_df["description"].apply(lambda x: len(x.split(" ")))

# convert the created column to datetime object so as to extract more features 
train_df["created"] = pd.to_datetime(train_df["created"])
test_df["created"] = pd.to_datetime(test_df["created"])

# Let us extract some features like year, month, day, hour from date columns #
train_df["created_year"] = train_df["created"].dt.year
test_df["created_year"] = test_df["created"].dt.year
train_df["created_month"] = train_df["created"].dt.month
test_df["created_month"] = test_df["created"].dt.month
train_df["created_day"] = train_df["created"].dt.day
test_df["created_day"] = test_df["created"].dt.day
train_df["created_hour"] = train_df["created"].dt.hour
test_df["created_hour"] = test_df["created"].dt.hour

# adding all these new features to use list #
features_to_use.extend(["num_photos", "num_features", "num_description_words","created_year", "created_month", "created_day", "listing_id", "created_hour"])

# 类型：数值 类别
train_df["price_t"] =train_df["price"]/train_df["bedrooms"]
test_df["price_t"] = test_df["price"]/test_df["bedrooms"] 

train_df["price_t"] =train_df["price"]/train_df["bathrooms"]
test_df["price_t"] = test_df["price"]/test_df["bathrooms"] 

train_df["room_sum"] = train_df["bedrooms"]+train_df["bathrooms"] 
test_df["room_sum"] = test_df["bedrooms"]+test_df["bathrooms"]   #总共有多少房间

train_df["price_t"] =train_df["price"]/train_df["room_sum"]
test_df["price_t"] = test_df["price"]/test_df["room_sum"]   #平摊到每个房间的价格

features_to_use.extend(["price_t", "room_sum", "num_description_words"])

start_values = [0,0,0]

index=list(range(train_df.shape[0]))
random.shuffle(index)
a=[np.nan]*len(train_df)
b=[np.nan]*len(train_df)
c=[np.nan]*len(train_df)


# 每次赋值，对未知的样本进行赋值
# manager_id 中介：id 高基数类别特征，target encoding

# 训练集部分进行统计，对验证部分进行编码
# 随机把数据集划分5份，4份计算target encoding，对剩余一份进行赋值

# 常规的模型：数值输入，sklean，神经网络
# xgboost、lightgbm、catboost：可以输入类别

# 分组统计：不同managerid分组下面的标签平均
# 不同managerid分组下面的价格分布

for i in range(5):
    building_level={}
    for j in train_df['manager_id'].values:
        building_level[j]= start_values.copy()
    test_index=index[int((i*train_df.shape[0])/5):int(((i+1)*train_df.shape[0])/5)]
    train_index=list(set(index).difference(test_index))
    for j in train_index:
        temp=train_df.iloc[j]
        if temp['interest_level']=='low':
            building_level[temp['manager_id']][0]+=1
        if temp['interest_level']=='medium':
            building_level[temp['manager_id']][1]+=1
        if temp['interest_level']=='high':
            building_level[temp['manager_id']][2]+=1
    for j in test_index:
        temp=train_df.iloc[j]
        if sum(building_level[temp['manager_id']])!=0:
            a[j]=building_level[temp['manager_id']][0]*1.0/sum(building_level[temp['manager_id']])
            b[j]=building_level[temp['manager_id']][1]*1.0/sum(building_level[temp['manager_id']])
            c[j]=building_level[temp['manager_id']][2]*1.0/sum(building_level[temp['manager_id']])
train_df['manager_level_low']=a
train_df['manager_level_medium']=b
train_df['manager_level_high']=c


a=[]
b=[]
c=[]
building_level={}
for j in train_df['manager_id'].values:
    building_level[j]= start_values.copy()
for j in range(train_df.shape[0]):
    temp=train_df.iloc[j]
    if temp['interest_level']=='low':
        building_level[temp['manager_id']][0]+=1
    if temp['interest_level']=='medium':
        building_level[temp['manager_id']][1]+=1
    if temp['interest_level']=='high':
        building_level[temp['manager_id']][2]+=1

for i in test_df['manager_id'].values:
    if i not in building_level.keys():
        a.append(np.nan)
        b.append(np.nan)
        c.append(np.nan)
    else:
        a.append(building_level[i][0]*1.0/sum(building_level[i]))
        b.append(building_level[i][1]*1.0/sum(building_level[i]))
        c.append(building_level[i][2]*1.0/sum(building_level[i]))
test_df['manager_level_low']=a
test_df['manager_level_medium']=b
test_df['manager_level_high']=c

features_to_use.append('manager_level_low') 
features_to_use.append('manager_level_medium') 
features_to_use.append('manager_level_high')

train_df["listing_id1"] = train_df["listing_id"] - 68119576.0
test_df["listing_id1"] =  test_df["listing_id"] - 68119576.0

train_df["num_price_by_furniture"] = (train_df["price"])/ (train_df["bathrooms"] + train_df["bedrooms"] + 1.0)
test_df["num_price_by_furniture"] =  (test_df["price"])/ (test_df["bathrooms"] + test_df["bedrooms"] +  1.0)

train_df["price_latitue"] = (train_df["price"])/ (train_df["latitude"]+1.0)
test_df["price_latitue"] =  (test_df["price"])/ (test_df["latitude"]+1.0)

train_df["price_longtitude"] = (train_df["price"])/ (train_df["longitude"]-1.0)
test_df["price_longtitude"] =  (test_df["price"])/ (test_df["longitude"]-1.0)  

train_df["num_furniture"] =  train_df["bathrooms"] + train_df["bedrooms"] 
test_df["num_furniture"] =   test_df["bathrooms"] + test_df["bedrooms"] 

train_df["total_days"] =   (train_df["created_month"] -4.0)*30 + train_df["created_day"] +  train_df["created_hour"] /25.0
test_df["total_days"] =(test_df["created_month"] -4.0)*30 + test_df["created_day"] +  test_df["created_hour"] /25.0        
train_df["diff_rank"]= train_df["total_days"]/train_df["listing_id1"]
test_df["diff_rank"]= test_df["total_days"]/test_df["listing_id1"]


features_to_use.extend([ "total_days","diff_rank",
"num_price_by_furniture","price_latitue","price_longtitude",'num_furniture'])

categorical = ["display_address", "manager_id", "building_id", "street_address"]
for f in categorical:
        if train_df[f].dtype=='object':
            #print(f)
            lbl = preprocessing.LabelEncoder()
            lbl.fit(list(train_df[f].values) + list(test_df[f].values))
            train_df[f] = lbl.transform(list(train_df[f].values))
            test_df[f] = lbl.transform(list(test_df[f].values))
            features_to_use.append(f)

train_df["price0"] = (train_df["price"]%10==0).astype(int)
test_df["price0"] = (test_df["price"]%10==0).astype(int)

train_df["manager_count"] = train_df["manager_id"].replace(train_df["manager_id"].value_counts())
test_df["manager_count"] = test_df["manager_id"].replace(train_df["manager_id"].value_counts())

features_to_use.extend(["price0",'manager_count'])

train_df['features'] = train_df["features"].apply(lambda x: " ".join(["_".join(i.split(" ")) for i in x]))
test_df['features'] = test_df["features"].apply(lambda x: " ".join(["_".join(i.split(" ")) for i in x]))
print(train_df["features"].head())
tfidf = CountVectorizer(stop_words='english', max_features=70)
te_sparse = tfidf.fit_transform(test_df["features"])
tr_sparse = tfidf.transform(train_df["features"])

tfidfdesc=TfidfVectorizer(min_df=20, max_features=50, strip_accents='unicode',lowercase =True,
                    analyzer='word', token_pattern=r'\w{16,}', ngram_range=(1, 2), use_idf=False,smooth_idf=False, 
                    sublinear_tf=True, stop_words = 'english')  

train_df['description'] =  train_df['description'].apply(lambda x: str(x).encode('utf-8') if len(x)>2 else "nulldesc") 
test_df['description'] =   test_df['description'].apply(lambda x: str(x).encode('utf-8') if len(x)>2 else "nulldesc") 
te_sparsed = tfidfdesc. fit_transform (test_df["description"])  
tr_sparsed = tfidfdesc.transform(train_df["description"])

train_X = sparse.hstack([train_df[features_to_use], tr_sparse,tr_sparsed]).tocsr()#
test_X = sparse.hstack([test_df[features_to_use], te_sparse,te_sparsed]).tocsr()#

target_num_map = {'high':0, 'medium':1, 'low':2}
train_y = np.array(train_df['interest_level'].apply(lambda x: target_num_map[x]))

print(train_X.shape, test_X.shape)

10                                                         
10000     elevator concierge fitness_center cats_allowed...
100004                         dish_washer hardwood laundry
100007                                      hardwood no_fee
100013                                              pre_war
Name: features, dtype: object
(49352, 166) (74659, 166)

preds, model = runXGB(train_X, train_y, val_X=None, val_y=None, test_X=test_X, num_rounds=100)
out_df = pd.DataFrame(preds)
out_df.columns = ["high", "medium", "low"]
out_df["listing_id"] = test_df.listing_id.values
out_df.to_csv("xgb_baseline3.csv", index=False)

cv_scores = []
test_pred = None
kf = model_selection.KFold(n_splits=5, shuffle=True, random_state=2016)
for dev_index, val_index in kf.split(range(train_X.shape[0])):
        dev_X, val_X = train_X[dev_index,:], train_X[val_index,:]
        dev_y, val_y = train_y[dev_index], train_y[val_index]
        preds, model = runXGB(dev_X, dev_y, val_X, val_y, test_X, num_rounds=2000)
        
        if test_pred is None:
            test_pred = preds
        else:
            test_pred += preds
test_pred /= 5
out_df = pd.DataFrame(test_pred)
out_df.columns = ["high", "medium", "low"]
out_df["listing_id"] = test_df.listing_id.values
out_df.to_csv("xgb_baseline3.csv", index=False)

[0]	train-mlogloss:1.07775	val-mlogloss:1.07827
Multiple eval metrics have been passed: 'val-mlogloss' will be used for early stopping.

Will train until val-mlogloss hasn't improved in 50 rounds.
[1]	train-mlogloss:1.05804	val-mlogloss:1.05915
[2]	train-mlogloss:1.0391	val-mlogloss:1.04076
[3]	train-mlogloss:1.02106	val-mlogloss:1.02331
[4]	train-mlogloss:1.00379	val-mlogloss:1.00659
[5]	train-mlogloss:0.987297	val-mlogloss:0.990692
[6]	train-mlogloss:0.971684	val-mlogloss:0.975621
[7]	train-mlogloss:0.956643	val-mlogloss:0.961212
[8]	train-mlogloss:0.942392	val-mlogloss:0.947541
[9]	train-mlogloss:0.928634	val-mlogloss:0.934332
[10]	train-mlogloss:0.915499	val-mlogloss:0.921662
[11]	train-mlogloss:0.902885	val-mlogloss:0.909531
[12]	train-mlogloss:0.890587	val-mlogloss:0.897704
[13]	train-mlogloss:0.879018	val-mlogloss:0.886706
[14]	train-mlogloss:0.867827	val-mlogloss:0.876054
[15]	train-mlogloss:0.857247	val-mlogloss:0.865963
[16]	train-mlogloss:0.847002	val-mlogloss:0.856158
[17]	train-mlogloss:0.837087	val-mlogloss:0.846743
[18]	train-mlogloss:0.827615	val-mlogloss:0.837644
[19]	train-mlogloss:0.818583	val-mlogloss:0.82898
[20]	train-mlogloss:0.809766	val-mlogloss:0.820555
[21]	train-mlogloss:0.801199	val-mlogloss:0.812395
[22]	train-mlogloss:0.79294	val-mlogloss:0.804595
[23]	train-mlogloss:0.785002	val-mlogloss:0.797013
[24]	train-mlogloss:0.777443	val-mlogloss:0.789759
[25]	train-mlogloss:0.770085	val-mlogloss:0.782812
[26]	train-mlogloss:0.762869	val-mlogloss:0.776079
[27]	train-mlogloss:0.756035	val-mlogloss:0.769586
[28]	train-mlogloss:0.749341	val-mlogloss:0.763342
[29]	train-mlogloss:0.742882	val-mlogloss:0.757373
[30]	train-mlogloss:0.736703	val-mlogloss:0.751577
[31]	train-mlogloss:0.730713	val-mlogloss:0.74597
[32]	train-mlogloss:0.724874	val-mlogloss:0.740554
[33]	train-mlogloss:0.719264	val-mlogloss:0.735261
[34]	train-mlogloss:0.713798	val-mlogloss:0.730179
[35]	train-mlogloss:0.708494	val-mlogloss:0.72532
[36]	train-mlogloss:0.703366	val-mlogloss:0.720577
[37]	train-mlogloss:0.698472	val-mlogloss:0.716033
[38]	train-mlogloss:0.693772	val-mlogloss:0.711691
[39]	train-mlogloss:0.689239	val-mlogloss:0.707555
[40]	train-mlogloss:0.684838	val-mlogloss:0.703506
[41]	train-mlogloss:0.680593	val-mlogloss:0.699603
[42]	train-mlogloss:0.676334	val-mlogloss:0.695704
[43]	train-mlogloss:0.672309	val-mlogloss:0.692011
[44]	train-mlogloss:0.668327	val-mlogloss:0.688428
[45]	train-mlogloss:0.664522	val-mlogloss:0.684887
[46]	train-mlogloss:0.660933	val-mlogloss:0.681643
[47]	train-mlogloss:0.657279	val-mlogloss:0.678362
[48]	train-mlogloss:0.653796	val-mlogloss:0.675239
[49]	train-mlogloss:0.650425	val-mlogloss:0.672188
[50]	train-mlogloss:0.647112	val-mlogloss:0.669102
[51]	train-mlogloss:0.643936	val-mlogloss:0.666283
[52]	train-mlogloss:0.640879	val-mlogloss:0.663544
[53]	train-mlogloss:0.637921	val-mlogloss:0.660922
[54]	train-mlogloss:0.634942	val-mlogloss:0.658315
[55]	train-mlogloss:0.632088	val-mlogloss:0.655759
[56]	train-mlogloss:0.629209	val-mlogloss:0.653304
[57]	train-mlogloss:0.626533	val-mlogloss:0.650923
[58]	train-mlogloss:0.623935	val-mlogloss:0.648687
[59]	train-mlogloss:0.621426	val-mlogloss:0.646462
[60]	train-mlogloss:0.618903	val-mlogloss:0.644235
[61]	train-mlogloss:0.616492	val-mlogloss:0.642063
[62]	train-mlogloss:0.61408	val-mlogloss:0.639956
[63]	train-mlogloss:0.611771	val-mlogloss:0.637903
[64]	train-mlogloss:0.60947	val-mlogloss:0.635978
[65]	train-mlogloss:0.607336	val-mlogloss:0.63416
[66]	train-mlogloss:0.605187	val-mlogloss:0.632312
[67]	train-mlogloss:0.603116	val-mlogloss:0.630568
[68]	train-mlogloss:0.60112	val-mlogloss:0.628791
[69]	train-mlogloss:0.599165	val-mlogloss:0.627139
[70]	train-mlogloss:0.597214	val-mlogloss:0.62547
[71]	train-mlogloss:0.595304	val-mlogloss:0.623908
[72]	train-mlogloss:0.593517	val-mlogloss:0.622375
[73]	train-mlogloss:0.591808	val-mlogloss:0.620976
[74]	train-mlogloss:0.590092	val-mlogloss:0.61953
[75]	train-mlogloss:0.588488	val-mlogloss:0.618189
[76]	train-mlogloss:0.586666	val-mlogloss:0.616749
[77]	train-mlogloss:0.584939	val-mlogloss:0.615338
[78]	train-mlogloss:0.58323	val-mlogloss:0.613948
[79]	train-mlogloss:0.581509	val-mlogloss:0.612552
[80]	train-mlogloss:0.579989	val-mlogloss:0.611326
[81]	train-mlogloss:0.578431	val-mlogloss:0.610125
[82]	train-mlogloss:0.576922	val-mlogloss:0.60898
[83]	train-mlogloss:0.575312	val-mlogloss:0.607687
[84]	train-mlogloss:0.573863	val-mlogloss:0.606527
[85]	train-mlogloss:0.572426	val-mlogloss:0.605464
[86]	train-mlogloss:0.571034	val-mlogloss:0.604398
[87]	train-mlogloss:0.569715	val-mlogloss:0.603359
[88]	train-mlogloss:0.568427	val-mlogloss:0.602321
[89]	train-mlogloss:0.567089	val-mlogloss:0.601322
[90]	train-mlogloss:0.565838	val-mlogloss:0.600377
[91]	train-mlogloss:0.564603	val-mlogloss:0.599448
[92]	train-mlogloss:0.563468	val-mlogloss:0.598632
[93]	train-mlogloss:0.562321	val-mlogloss:0.597795
[94]	train-mlogloss:0.561166	val-mlogloss:0.596895
[95]	train-mlogloss:0.55994	val-mlogloss:0.59598
[96]	train-mlogloss:0.558748	val-mlogloss:0.595017
[97]	train-mlogloss:0.557595	val-mlogloss:0.594185
[98]	train-mlogloss:0.556575	val-mlogloss:0.593453
[99]	train-mlogloss:0.555548	val-mlogloss:0.592668
[100]	train-mlogloss:0.554452	val-mlogloss:0.591822
[101]	train-mlogloss:0.553388	val-mlogloss:0.591059
[102]	train-mlogloss:0.552379	val-mlogloss:0.590298
[103]	train-mlogloss:0.551296	val-mlogloss:0.589535
[104]	train-mlogloss:0.550356	val-mlogloss:0.588745
[105]	train-mlogloss:0.549275	val-mlogloss:0.587961
[106]	train-mlogloss:0.548295	val-mlogloss:0.587239
[107]	train-mlogloss:0.547351	val-mlogloss:0.586562
[108]	train-mlogloss:0.546472	val-mlogloss:0.585977
[109]	train-mlogloss:0.545532	val-mlogloss:0.585268
[110]	train-mlogloss:0.544619	val-mlogloss:0.584629
[111]	train-mlogloss:0.543789	val-mlogloss:0.58401
[112]	train-mlogloss:0.542922	val-mlogloss:0.583409
[113]	train-mlogloss:0.542089	val-mlogloss:0.582749
[114]	train-mlogloss:0.541285	val-mlogloss:0.582233
[115]	train-mlogloss:0.540366	val-mlogloss:0.58157
[116]	train-mlogloss:0.539487	val-mlogloss:0.581026
[117]	train-mlogloss:0.538648	val-mlogloss:0.580401
[118]	train-mlogloss:0.537875	val-mlogloss:0.579826
[119]	train-mlogloss:0.537077	val-mlogloss:0.579299
[120]	train-mlogloss:0.536234	val-mlogloss:0.578751
[121]	train-mlogloss:0.535424	val-mlogloss:0.578289
[122]	train-mlogloss:0.534755	val-mlogloss:0.577885
[123]	train-mlogloss:0.533977	val-mlogloss:0.577328
[124]	train-mlogloss:0.533203	val-mlogloss:0.57689
[125]	train-mlogloss:0.532455	val-mlogloss:0.576445
[126]	train-mlogloss:0.531631	val-mlogloss:0.575928
[127]	train-mlogloss:0.530873	val-mlogloss:0.575459
[128]	train-mlogloss:0.530228	val-mlogloss:0.575039
[129]	train-mlogloss:0.529579	val-mlogloss:0.57462
[130]	train-mlogloss:0.528915	val-mlogloss:0.574171
[131]	train-mlogloss:0.528234	val-mlogloss:0.57379
[132]	train-mlogloss:0.527525	val-mlogloss:0.573392
[133]	train-mlogloss:0.52684	val-mlogloss:0.57292
[134]	train-mlogloss:0.52614	val-mlogloss:0.572507
[135]	train-mlogloss:0.525406	val-mlogloss:0.572068
[136]	train-mlogloss:0.524808	val-mlogloss:0.571702
[137]	train-mlogloss:0.524142	val-mlogloss:0.571292
[138]	train-mlogloss:0.523411	val-mlogloss:0.570863
[139]	train-mlogloss:0.522725	val-mlogloss:0.570455
[140]	train-mlogloss:0.522151	val-mlogloss:0.570082
[141]	train-mlogloss:0.521466	val-mlogloss:0.569587
[142]	train-mlogloss:0.520799	val-mlogloss:0.569195
[143]	train-mlogloss:0.520182	val-mlogloss:0.568809
[144]	train-mlogloss:0.519479	val-mlogloss:0.568464
[145]	train-mlogloss:0.518863	val-mlogloss:0.568147
[146]	train-mlogloss:0.518224	val-mlogloss:0.567792
[147]	train-mlogloss:0.51762	val-mlogloss:0.567481
[148]	train-mlogloss:0.517022	val-mlogloss:0.567087
[149]	train-mlogloss:0.516461	val-mlogloss:0.566718
[150]	train-mlogloss:0.515825	val-mlogloss:0.566333
[151]	train-mlogloss:0.515273	val-mlogloss:0.565989
[152]	train-mlogloss:0.514678	val-mlogloss:0.565621
[153]	train-mlogloss:0.514159	val-mlogloss:0.565331
[154]	train-mlogloss:0.513648	val-mlogloss:0.565005
[155]	train-mlogloss:0.513074	val-mlogloss:0.56471
[156]	train-mlogloss:0.512547	val-mlogloss:0.564468
[157]	train-mlogloss:0.511923	val-mlogloss:0.564102
[158]	train-mlogloss:0.511411	val-mlogloss:0.563827
[159]	train-mlogloss:0.510778	val-mlogloss:0.563483
[160]	train-mlogloss:0.510313	val-mlogloss:0.563274
[161]	train-mlogloss:0.509711	val-mlogloss:0.56301
[162]	train-mlogloss:0.50914	val-mlogloss:0.562714
[163]	train-mlogloss:0.508492	val-mlogloss:0.562426
[164]	train-mlogloss:0.507943	val-mlogloss:0.562215
[165]	train-mlogloss:0.507426	val-mlogloss:0.56191
[166]	train-mlogloss:0.506847	val-mlogloss:0.561624
[167]	train-mlogloss:0.506326	val-mlogloss:0.561331
[168]	train-mlogloss:0.505887	val-mlogloss:0.561086
[169]	train-mlogloss:0.505338	val-mlogloss:0.560864
[170]	train-mlogloss:0.504877	val-mlogloss:0.560647
[171]	train-mlogloss:0.504194	val-mlogloss:0.560321
[172]	train-mlogloss:0.503824	val-mlogloss:0.560112
[173]	train-mlogloss:0.503385	val-mlogloss:0.559882
[174]	train-mlogloss:0.502722	val-mlogloss:0.559504
[175]	train-mlogloss:0.502224	val-mlogloss:0.559311
[176]	train-mlogloss:0.501795	val-mlogloss:0.559084
[177]	train-mlogloss:0.50141	val-mlogloss:0.558864
[178]	train-mlogloss:0.500948	val-mlogloss:0.558714
[179]	train-mlogloss:0.50043	val-mlogloss:0.558479
[180]	train-mlogloss:0.499915	val-mlogloss:0.558233
[181]	train-mlogloss:0.499412	val-mlogloss:0.557988
[182]	train-mlogloss:0.498959	val-mlogloss:0.55773
[183]	train-mlogloss:0.498605	val-mlogloss:0.557536
[184]	train-mlogloss:0.498041	val-mlogloss:0.557348
[185]	train-mlogloss:0.497586	val-mlogloss:0.557168
[186]	train-mlogloss:0.497112	val-mlogloss:0.556978
[187]	train-mlogloss:0.496679	val-mlogloss:0.556789
[188]	train-mlogloss:0.496312	val-mlogloss:0.556634
[189]	train-mlogloss:0.495836	val-mlogloss:0.556458
[190]	train-mlogloss:0.495502	val-mlogloss:0.55629
[191]	train-mlogloss:0.495037	val-mlogloss:0.55608
[192]	train-mlogloss:0.494555	val-mlogloss:0.555862
[193]	train-mlogloss:0.494057	val-mlogloss:0.555644
[194]	train-mlogloss:0.493559	val-mlogloss:0.555467
[195]	train-mlogloss:0.493131	val-mlogloss:0.555313
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[777]	train-mlogloss:0.351333	val-mlogloss:0.528979
[778]	train-mlogloss:0.35116	val-mlogloss:0.528988
[779]	train-mlogloss:0.350991	val-mlogloss:0.528979
[780]	train-mlogloss:0.350776	val-mlogloss:0.528937
[781]	train-mlogloss:0.350583	val-mlogloss:0.528897
[782]	train-mlogloss:0.350422	val-mlogloss:0.528897
[783]	train-mlogloss:0.350296	val-mlogloss:0.528904
[784]	train-mlogloss:0.350181	val-mlogloss:0.528908
[785]	train-mlogloss:0.349999	val-mlogloss:0.528879
[786]	train-mlogloss:0.349795	val-mlogloss:0.528867
[787]	train-mlogloss:0.349627	val-mlogloss:0.528849
[788]	train-mlogloss:0.349414	val-mlogloss:0.528836
[789]	train-mlogloss:0.349251	val-mlogloss:0.528805
[790]	train-mlogloss:0.349089	val-mlogloss:0.528765
[791]	train-mlogloss:0.348921	val-mlogloss:0.528772
[792]	train-mlogloss:0.348751	val-mlogloss:0.528777
[793]	train-mlogloss:0.348564	val-mlogloss:0.528793
[794]	train-mlogloss:0.348361	val-mlogloss:0.528788
[795]	train-mlogloss:0.348206	val-mlogloss:0.528809
[796]	train-mlogloss:0.348033	val-mlogloss:0.528786
[797]	train-mlogloss:0.34787	val-mlogloss:0.528823
[798]	train-mlogloss:0.347737	val-mlogloss:0.528791
[799]	train-mlogloss:0.347551	val-mlogloss:0.52881
[800]	train-mlogloss:0.34743	val-mlogloss:0.528813
[801]	train-mlogloss:0.347264	val-mlogloss:0.528811
[802]	train-mlogloss:0.347138	val-mlogloss:0.528777
[803]	train-mlogloss:0.347019	val-mlogloss:0.528774
[804]	train-mlogloss:0.346825	val-mlogloss:0.528787
[805]	train-mlogloss:0.34663	val-mlogloss:0.528737
[806]	train-mlogloss:0.346503	val-mlogloss:0.528727
[807]	train-mlogloss:0.346316	val-mlogloss:0.52872
[808]	train-mlogloss:0.346191	val-mlogloss:0.528726
[809]	train-mlogloss:0.346024	val-mlogloss:0.528706
[810]	train-mlogloss:0.345836	val-mlogloss:0.528699
[811]	train-mlogloss:0.345616	val-mlogloss:0.528612
[812]	train-mlogloss:0.345439	val-mlogloss:0.528596
[813]	train-mlogloss:0.345283	val-mlogloss:0.528573
[814]	train-mlogloss:0.345145	val-mlogloss:0.528532
[815]	train-mlogloss:0.344958	val-mlogloss:0.528482
[816]	train-mlogloss:0.34477	val-mlogloss:0.528454
[817]	train-mlogloss:0.344667	val-mlogloss:0.528448
[818]	train-mlogloss:0.344531	val-mlogloss:0.528431
[819]	train-mlogloss:0.344373	val-mlogloss:0.528408
[820]	train-mlogloss:0.344194	val-mlogloss:0.528392
[821]	train-mlogloss:0.344023	val-mlogloss:0.528379
[822]	train-mlogloss:0.343869	val-mlogloss:0.528384
[823]	train-mlogloss:0.34371	val-mlogloss:0.528379
[824]	train-mlogloss:0.343512	val-mlogloss:0.528381
[825]	train-mlogloss:0.343346	val-mlogloss:0.528383
[826]	train-mlogloss:0.343154	val-mlogloss:0.528392
[827]	train-mlogloss:0.342966	val-mlogloss:0.528369
[828]	train-mlogloss:0.342867	val-mlogloss:0.528349
[829]	train-mlogloss:0.342685	val-mlogloss:0.528317
[830]	train-mlogloss:0.342504	val-mlogloss:0.528287
[831]	train-mlogloss:0.342279	val-mlogloss:0.528262
[832]	train-mlogloss:0.342061	val-mlogloss:0.528248
[833]	train-mlogloss:0.341925	val-mlogloss:0.528251
[834]	train-mlogloss:0.341789	val-mlogloss:0.528211
[835]	train-mlogloss:0.34162	val-mlogloss:0.52824
[836]	train-mlogloss:0.341453	val-mlogloss:0.528242
[837]	train-mlogloss:0.34132	val-mlogloss:0.528236
[838]	train-mlogloss:0.341137	val-mlogloss:0.528233
[839]	train-mlogloss:0.341038	val-mlogloss:0.52822
[840]	train-mlogloss:0.340886	val-mlogloss:0.5282
[841]	train-mlogloss:0.340741	val-mlogloss:0.528193
[842]	train-mlogloss:0.340542	val-mlogloss:0.528191
[843]	train-mlogloss:0.340433	val-mlogloss:0.528205
[844]	train-mlogloss:0.340242	val-mlogloss:0.528183
[845]	train-mlogloss:0.340025	val-mlogloss:0.528178
[846]	train-mlogloss:0.33984	val-mlogloss:0.528193
[847]	train-mlogloss:0.339726	val-mlogloss:0.528201
[848]	train-mlogloss:0.339633	val-mlogloss:0.528213
[849]	train-mlogloss:0.339496	val-mlogloss:0.528221
[850]	train-mlogloss:0.339336	val-mlogloss:0.528196
[851]	train-mlogloss:0.339113	val-mlogloss:0.528193
[852]	train-mlogloss:0.338999	val-mlogloss:0.528235
[853]	train-mlogloss:0.338847	val-mlogloss:0.52823
[854]	train-mlogloss:0.33871	val-mlogloss:0.528245
[855]	train-mlogloss:0.338536	val-mlogloss:0.528242
[856]	train-mlogloss:0.3384	val-mlogloss:0.528234
[857]	train-mlogloss:0.338204	val-mlogloss:0.528248
[858]	train-mlogloss:0.338001	val-mlogloss:0.528251
[859]	train-mlogloss:0.33789	val-mlogloss:0.528248
[860]	train-mlogloss:0.337711	val-mlogloss:0.52826
[861]	train-mlogloss:0.337629	val-mlogloss:0.528258
[862]	train-mlogloss:0.337463	val-mlogloss:0.528267
[863]	train-mlogloss:0.337324	val-mlogloss:0.52825
[864]	train-mlogloss:0.337114	val-mlogloss:0.528242
[865]	train-mlogloss:0.336922	val-mlogloss:0.52824
[866]	train-mlogloss:0.336774	val-mlogloss:0.528262
[867]	train-mlogloss:0.336627	val-mlogloss:0.528242
[868]	train-mlogloss:0.336439	val-mlogloss:0.528233
[869]	train-mlogloss:0.33632	val-mlogloss:0.528225
[870]	train-mlogloss:0.336174	val-mlogloss:0.528191
[871]	train-mlogloss:0.336065	val-mlogloss:0.52818
[872]	train-mlogloss:0.335923	val-mlogloss:0.528193
[873]	train-mlogloss:0.335758	val-mlogloss:0.528174
[874]	train-mlogloss:0.335566	val-mlogloss:0.528174
[875]	train-mlogloss:0.335404	val-mlogloss:0.528176
[876]	train-mlogloss:0.335273	val-mlogloss:0.528156
[877]	train-mlogloss:0.335078	val-mlogloss:0.52816
[878]	train-mlogloss:0.334961	val-mlogloss:0.528162
[879]	train-mlogloss:0.334827	val-mlogloss:0.528187
[880]	train-mlogloss:0.33468	val-mlogloss:0.52816
[881]	train-mlogloss:0.334539	val-mlogloss:0.528167
[882]	train-mlogloss:0.334436	val-mlogloss:0.528148
[883]	train-mlogloss:0.334287	val-mlogloss:0.528145
[884]	train-mlogloss:0.334117	val-mlogloss:0.52817
[885]	train-mlogloss:0.333965	val-mlogloss:0.528191
[886]	train-mlogloss:0.333825	val-mlogloss:0.528194
[887]	train-mlogloss:0.333704	val-mlogloss:0.528238
[888]	train-mlogloss:0.333523	val-mlogloss:0.528183
[889]	train-mlogloss:0.333399	val-mlogloss:0.528182
[890]	train-mlogloss:0.333246	val-mlogloss:0.528184
[891]	train-mlogloss:0.333128	val-mlogloss:0.528163
[892]	train-mlogloss:0.332999	val-mlogloss:0.528165
[893]	train-mlogloss:0.332847	val-mlogloss:0.52816
[894]	train-mlogloss:0.332722	val-mlogloss:0.528153
[895]	train-mlogloss:0.332567	val-mlogloss:0.528141
[896]	train-mlogloss:0.332443	val-mlogloss:0.528144
[897]	train-mlogloss:0.332266	val-mlogloss:0.528142
[898]	train-mlogloss:0.332139	val-mlogloss:0.528152
[899]	train-mlogloss:0.331954	val-mlogloss:0.528156
[900]	train-mlogloss:0.331802	val-mlogloss:0.528159
[901]	train-mlogloss:0.331613	val-mlogloss:0.528143
[902]	train-mlogloss:0.331422	val-mlogloss:0.528133
[903]	train-mlogloss:0.331245	val-mlogloss:0.528124
[904]	train-mlogloss:0.331058	val-mlogloss:0.528107
[905]	train-mlogloss:0.330916	val-mlogloss:0.528114
[906]	train-mlogloss:0.330829	val-mlogloss:0.52812
[907]	train-mlogloss:0.330706	val-mlogloss:0.528104
[908]	train-mlogloss:0.330544	val-mlogloss:0.528068
[909]	train-mlogloss:0.330381	val-mlogloss:0.528077
[910]	train-mlogloss:0.330242	val-mlogloss:0.528057
[911]	train-mlogloss:0.330066	val-mlogloss:0.528069
[912]	train-mlogloss:0.329893	val-mlogloss:0.528045
[913]	train-mlogloss:0.329759	val-mlogloss:0.528064
[914]	train-mlogloss:0.329647	val-mlogloss:0.528071
[915]	train-mlogloss:0.329545	val-mlogloss:0.528085
[916]	train-mlogloss:0.329357	val-mlogloss:0.528074
[917]	train-mlogloss:0.329207	val-mlogloss:0.528063
[918]	train-mlogloss:0.329094	val-mlogloss:0.528086
[919]	train-mlogloss:0.328961	val-mlogloss:0.528106
[920]	train-mlogloss:0.328844	val-mlogloss:0.528068
[921]	train-mlogloss:0.328632	val-mlogloss:0.528104
[922]	train-mlogloss:0.32848	val-mlogloss:0.528151
[923]	train-mlogloss:0.328312	val-mlogloss:0.528097
[924]	train-mlogloss:0.328147	val-mlogloss:0.528034
[925]	train-mlogloss:0.327967	val-mlogloss:0.528044
[926]	train-mlogloss:0.327836	val-mlogloss:0.528063
[927]	train-mlogloss:0.327692	val-mlogloss:0.52805
[928]	train-mlogloss:0.327545	val-mlogloss:0.528045
[929]	train-mlogloss:0.327356	val-mlogloss:0.528053
[930]	train-mlogloss:0.327215	val-mlogloss:0.528033
[931]	train-mlogloss:0.327073	val-mlogloss:0.528029
[932]	train-mlogloss:0.326964	val-mlogloss:0.528046
[933]	train-mlogloss:0.326814	val-mlogloss:0.52806
[934]	train-mlogloss:0.326662	val-mlogloss:0.528055
[935]	train-mlogloss:0.326464	val-mlogloss:0.528039
[936]	train-mlogloss:0.326306	val-mlogloss:0.528041
[937]	train-mlogloss:0.32611	val-mlogloss:0.528025
[938]	train-mlogloss:0.325971	val-mlogloss:0.528027
[939]	train-mlogloss:0.32585	val-mlogloss:0.528053
[940]	train-mlogloss:0.325693	val-mlogloss:0.52808
[941]	train-mlogloss:0.325496	val-mlogloss:0.528036
[942]	train-mlogloss:0.325352	val-mlogloss:0.528007
[943]	train-mlogloss:0.325224	val-mlogloss:0.528002
[944]	train-mlogloss:0.325091	val-mlogloss:0.528047
[945]	train-mlogloss:0.324915	val-mlogloss:0.528096
[946]	train-mlogloss:0.324783	val-mlogloss:0.528074
[947]	train-mlogloss:0.324644	val-mlogloss:0.528126
[948]	train-mlogloss:0.324525	val-mlogloss:0.528118
[949]	train-mlogloss:0.324357	val-mlogloss:0.528098
[950]	train-mlogloss:0.324189	val-mlogloss:0.528099
[951]	train-mlogloss:0.324061	val-mlogloss:0.528106
[952]	train-mlogloss:0.323875	val-mlogloss:0.528109
[953]	train-mlogloss:0.323764	val-mlogloss:0.528082
[954]	train-mlogloss:0.323699	val-mlogloss:0.528068
[955]	train-mlogloss:0.323551	val-mlogloss:0.528051
[956]	train-mlogloss:0.32335	val-mlogloss:0.528063
[957]	train-mlogloss:0.323188	val-mlogloss:0.528073
[958]	train-mlogloss:0.32304	val-mlogloss:0.528051
[959]	train-mlogloss:0.322828	val-mlogloss:0.528056
[960]	train-mlogloss:0.322638	val-mlogloss:0.528066
[961]	train-mlogloss:0.322486	val-mlogloss:0.528053
[962]	train-mlogloss:0.322354	val-mlogloss:0.528039
[963]	train-mlogloss:0.322163	val-mlogloss:0.52804
[964]	train-mlogloss:0.322008	val-mlogloss:0.528058
[965]	train-mlogloss:0.32188	val-mlogloss:0.528069
[966]	train-mlogloss:0.321747	val-mlogloss:0.528035
[967]	train-mlogloss:0.321592	val-mlogloss:0.52801
[968]	train-mlogloss:0.32142	val-mlogloss:0.527992
[969]	train-mlogloss:0.321268	val-mlogloss:0.527983
[970]	train-mlogloss:0.321113	val-mlogloss:0.528005
[971]	train-mlogloss:0.320979	val-mlogloss:0.52799
[972]	train-mlogloss:0.32084	val-mlogloss:0.52797
[973]	train-mlogloss:0.320754	val-mlogloss:0.527985
[974]	train-mlogloss:0.320625	val-mlogloss:0.527998
[975]	train-mlogloss:0.320502	val-mlogloss:0.527975
[976]	train-mlogloss:0.320342	val-mlogloss:0.527982
[977]	train-mlogloss:0.32016	val-mlogloss:0.527975
[978]	train-mlogloss:0.320044	val-mlogloss:0.527969
[979]	train-mlogloss:0.319901	val-mlogloss:0.527954
[980]	train-mlogloss:0.319743	val-mlogloss:0.528009
[981]	train-mlogloss:0.319617	val-mlogloss:0.528004
[982]	train-mlogloss:0.319425	val-mlogloss:0.528063
[983]	train-mlogloss:0.319263	val-mlogloss:0.528066
[984]	train-mlogloss:0.319153	val-mlogloss:0.528061
[985]	train-mlogloss:0.318969	val-mlogloss:0.528075
[986]	train-mlogloss:0.318826	val-mlogloss:0.528066
[987]	train-mlogloss:0.318721	val-mlogloss:0.528064
[988]	train-mlogloss:0.318593	val-mlogloss:0.528105
[989]	train-mlogloss:0.318489	val-mlogloss:0.528081
[990]	train-mlogloss:0.318339	val-mlogloss:0.528087
[991]	train-mlogloss:0.318215	val-mlogloss:0.528056
[992]	train-mlogloss:0.318041	val-mlogloss:0.528052
[993]	train-mlogloss:0.317847	val-mlogloss:0.52803
[994]	train-mlogloss:0.317691	val-mlogloss:0.528032
[995]	train-mlogloss:0.317562	val-mlogloss:0.528032
[996]	train-mlogloss:0.3174	val-mlogloss:0.528038
[997]	train-mlogloss:0.317216	val-mlogloss:0.528041
[998]	train-mlogloss:0.317087	val-mlogloss:0.528029
[999]	train-mlogloss:0.316924	val-mlogloss:0.528023
[1000]	train-mlogloss:0.316755	val-mlogloss:0.528071
[1001]	train-mlogloss:0.316637	val-mlogloss:0.528079
[1002]	train-mlogloss:0.316485	val-mlogloss:0.528094
[1003]	train-mlogloss:0.316314	val-mlogloss:0.528093
[1004]	train-mlogloss:0.316211	val-mlogloss:0.528084
[1005]	train-mlogloss:0.316092	val-mlogloss:0.528104
[1006]	train-mlogloss:0.315957	val-mlogloss:0.528106
[1007]	train-mlogloss:0.315782	val-mlogloss:0.52811
[1008]	train-mlogloss:0.315625	val-mlogloss:0.528115
[1009]	train-mlogloss:0.315469	val-mlogloss:0.528068
[1010]	train-mlogloss:0.315374	val-mlogloss:0.528086
[1011]	train-mlogloss:0.31525	val-mlogloss:0.5281
[1012]	train-mlogloss:0.315127	val-mlogloss:0.52812
[1013]	train-mlogloss:0.314985	val-mlogloss:0.528125
[1014]	train-mlogloss:0.314815	val-mlogloss:0.528174
[1015]	train-mlogloss:0.31463	val-mlogloss:0.528177
[1016]	train-mlogloss:0.314486	val-mlogloss:0.528189
[1017]	train-mlogloss:0.314336	val-mlogloss:0.528167
[1018]	train-mlogloss:0.314189	val-mlogloss:0.528178
[1019]	train-mlogloss:0.314041	val-mlogloss:0.528181
[1020]	train-mlogloss:0.313979	val-mlogloss:0.528181
[1021]	train-mlogloss:0.313818	val-mlogloss:0.528187
[1022]	train-mlogloss:0.313686	val-mlogloss:0.528162
[1023]	train-mlogloss:0.313532	val-mlogloss:0.528133
[1024]	train-mlogloss:0.313365	val-mlogloss:0.528117
[1025]	train-mlogloss:0.313227	val-mlogloss:0.52813
[1026]	train-mlogloss:0.313062	val-mlogloss:0.528102
[1027]	train-mlogloss:0.312919	val-mlogloss:0.528111
[1028]	train-mlogloss:0.312773	val-mlogloss:0.528097
[1029]	train-mlogloss:0.312653	val-mlogloss:0.528098
Stopping. Best iteration:
[979]	train-mlogloss:0.319901	val-mlogloss:0.527954

[0]	train-mlogloss:1.07767	val-mlogloss:1.07815
Multiple eval metrics have been passed: 'val-mlogloss' will be used for early stopping.

Will train until val-mlogloss hasn't improved in 50 rounds.
[1]	train-mlogloss:1.05806	val-mlogloss:1.05906
[2]	train-mlogloss:1.03919	val-mlogloss:1.04071
[3]	train-mlogloss:1.02124	val-mlogloss:1.02323
[4]	train-mlogloss:1.00398	val-mlogloss:1.00646
[5]	train-mlogloss:0.98748	val-mlogloss:0.990486
[6]	train-mlogloss:0.971963	val-mlogloss:0.975414
[7]	train-mlogloss:0.956857	val-mlogloss:0.960818
[8]	train-mlogloss:0.942506	val-mlogloss:0.946983
[9]	train-mlogloss:0.928764	val-mlogloss:0.933616
[10]	train-mlogloss:0.915592	val-mlogloss:0.920944
[11]	train-mlogloss:0.902997	val-mlogloss:0.908762
[12]	train-mlogloss:0.890779	val-mlogloss:0.897061
[13]	train-mlogloss:0.879202	val-mlogloss:0.885924
[14]	train-mlogloss:0.868047	val-mlogloss:0.875236
[15]	train-mlogloss:0.857392	val-mlogloss:0.865048
[16]	train-mlogloss:0.847187	val-mlogloss:0.855252
[17]	train-mlogloss:0.837292	val-mlogloss:0.845799
[18]	train-mlogloss:0.827776	val-mlogloss:0.836699
[19]	train-mlogloss:0.818744	val-mlogloss:0.828045
[20]	train-mlogloss:0.809986	val-mlogloss:0.819765
[21]	train-mlogloss:0.801495	val-mlogloss:0.81174
[22]	train-mlogloss:0.793204	val-mlogloss:0.803835
[23]	train-mlogloss:0.785279	val-mlogloss:0.796255
[24]	train-mlogloss:0.777775	val-mlogloss:0.789106
[25]	train-mlogloss:0.770446	val-mlogloss:0.782169
[26]	train-mlogloss:0.763309	val-mlogloss:0.775468
[27]	train-mlogloss:0.756385	val-mlogloss:0.768883
[28]	train-mlogloss:0.749686	val-mlogloss:0.762523
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[898]	train-mlogloss:0.332121	val-mlogloss:0.522884
[899]	train-mlogloss:0.331961	val-mlogloss:0.522859
[900]	train-mlogloss:0.331842	val-mlogloss:0.522851
[901]	train-mlogloss:0.331648	val-mlogloss:0.522883
[902]	train-mlogloss:0.331474	val-mlogloss:0.522855
[903]	train-mlogloss:0.331273	val-mlogloss:0.522898
[904]	train-mlogloss:0.331089	val-mlogloss:0.522878
[905]	train-mlogloss:0.330961	val-mlogloss:0.522882
[906]	train-mlogloss:0.330827	val-mlogloss:0.522893
[907]	train-mlogloss:0.330636	val-mlogloss:0.522899
[908]	train-mlogloss:0.330424	val-mlogloss:0.522862
[909]	train-mlogloss:0.330288	val-mlogloss:0.522865
[910]	train-mlogloss:0.330196	val-mlogloss:0.522886
[911]	train-mlogloss:0.330057	val-mlogloss:0.522894
[912]	train-mlogloss:0.329876	val-mlogloss:0.522889
[913]	train-mlogloss:0.329669	val-mlogloss:0.52291
[914]	train-mlogloss:0.329537	val-mlogloss:0.522908
[915]	train-mlogloss:0.32941	val-mlogloss:0.522892
Stopping. Best iteration:
[865]	train-mlogloss:0.337405	val-mlogloss:0.522804

[0]	train-mlogloss:1.07785	val-mlogloss:1.07825
Multiple eval metrics have been passed: 'val-mlogloss' will be used for early stopping.

Will train until val-mlogloss hasn't improved in 50 rounds.
[1]	train-mlogloss:1.0582	val-mlogloss:1.05903
[2]	train-mlogloss:1.03946	val-mlogloss:1.04071
[3]	train-mlogloss:1.02154	val-mlogloss:1.02319
[4]	train-mlogloss:1.00437	val-mlogloss:1.00642
[5]	train-mlogloss:0.987855	val-mlogloss:0.990305
[6]	train-mlogloss:0.972221	val-mlogloss:0.975104
[7]	train-mlogloss:0.957366	val-mlogloss:0.960616
[8]	train-mlogloss:0.943297	val-mlogloss:0.946981
[9]	train-mlogloss:0.929506	val-mlogloss:0.933642
[10]	train-mlogloss:0.916266	val-mlogloss:0.920858
[11]	train-mlogloss:0.903524	val-mlogloss:0.908541
[12]	train-mlogloss:0.891404	val-mlogloss:0.896886
[13]	train-mlogloss:0.879722	val-mlogloss:0.885705
[14]	train-mlogloss:0.8689	val-mlogloss:0.875322
[15]	train-mlogloss:0.858222	val-mlogloss:0.864991
[16]	train-mlogloss:0.847995	val-mlogloss:0.855176
[17]	train-mlogloss:0.838038	val-mlogloss:0.84568
[18]	train-mlogloss:0.828382	val-mlogloss:0.836429
[19]	train-mlogloss:0.819163	val-mlogloss:0.827621
[20]	train-mlogloss:0.810383	val-mlogloss:0.819255
[21]	train-mlogloss:0.801857	val-mlogloss:0.811102
[22]	train-mlogloss:0.793707	val-mlogloss:0.80333
[23]	train-mlogloss:0.785836	val-mlogloss:0.795771
[24]	train-mlogloss:0.778428	val-mlogloss:0.788722
[25]	train-mlogloss:0.771093	val-mlogloss:0.781718
[26]	train-mlogloss:0.763979	val-mlogloss:0.774973
[27]	train-mlogloss:0.757088	val-mlogloss:0.768487
[28]	train-mlogloss:0.750398	val-mlogloss:0.762178
[29]	train-mlogloss:0.743901	val-mlogloss:0.75604
[30]	train-mlogloss:0.737662	val-mlogloss:0.750153
[31]	train-mlogloss:0.731693	val-mlogloss:0.74453
[32]	train-mlogloss:0.725852	val-mlogloss:0.738958
[33]	train-mlogloss:0.720252	val-mlogloss:0.733735
[34]	train-mlogloss:0.714827	val-mlogloss:0.728684
[35]	train-mlogloss:0.709578	val-mlogloss:0.723759
[36]	train-mlogloss:0.704497	val-mlogloss:0.718999
[37]	train-mlogloss:0.699626	val-mlogloss:0.714491
[38]	train-mlogloss:0.694773	val-mlogloss:0.710037
[39]	train-mlogloss:0.690238	val-mlogloss:0.70579
[40]	train-mlogloss:0.685813	val-mlogloss:0.701629
[41]	train-mlogloss:0.681533	val-mlogloss:0.697623
[42]	train-mlogloss:0.677345	val-mlogloss:0.693759
[43]	train-mlogloss:0.673366	val-mlogloss:0.690083
[44]	train-mlogloss:0.669395	val-mlogloss:0.686464
[45]	train-mlogloss:0.665575	val-mlogloss:0.682988
[46]	train-mlogloss:0.661837	val-mlogloss:0.679562
[47]	train-mlogloss:0.658217	val-mlogloss:0.676277
[48]	train-mlogloss:0.65475	val-mlogloss:0.673134
[49]	train-mlogloss:0.651412	val-mlogloss:0.670106
[50]	train-mlogloss:0.648128	val-mlogloss:0.667196
[51]	train-mlogloss:0.644857	val-mlogloss:0.664234
[52]	train-mlogloss:0.641723	val-mlogloss:0.661384
[53]	train-mlogloss:0.638677	val-mlogloss:0.658674
[54]	train-mlogloss:0.635725	val-mlogloss:0.656072
[55]	train-mlogloss:0.632872	val-mlogloss:0.653521
[56]	train-mlogloss:0.630086	val-mlogloss:0.650979
[57]	train-mlogloss:0.627273	val-mlogloss:0.648463
[58]	train-mlogloss:0.624551	val-mlogloss:0.646015
[59]	train-mlogloss:0.62197	val-mlogloss:0.643723
[60]	train-mlogloss:0.61944	val-mlogloss:0.641483
[61]	train-mlogloss:0.616964	val-mlogloss:0.639327
[62]	train-mlogloss:0.614619	val-mlogloss:0.637322
[63]	train-mlogloss:0.612233	val-mlogloss:0.635285
[64]	train-mlogloss:0.609922	val-mlogloss:0.633292
[65]	train-mlogloss:0.607697	val-mlogloss:0.631391
[66]	train-mlogloss:0.605498	val-mlogloss:0.629606
[67]	train-mlogloss:0.603355	val-mlogloss:0.627792
[68]	train-mlogloss:0.601346	val-mlogloss:0.626151
[69]	train-mlogloss:0.599292	val-mlogloss:0.62445
[70]	train-mlogloss:0.597326	val-mlogloss:0.622801
[71]	train-mlogloss:0.595447	val-mlogloss:0.621207
[72]	train-mlogloss:0.593632	val-mlogloss:0.619683
[73]	train-mlogloss:0.591846	val-mlogloss:0.618164
[74]	train-mlogloss:0.590101	val-mlogloss:0.616719
[75]	train-mlogloss:0.588289	val-mlogloss:0.615183
[76]	train-mlogloss:0.586602	val-mlogloss:0.613799
[77]	train-mlogloss:0.584893	val-mlogloss:0.61237
[78]	train-mlogloss:0.583316	val-mlogloss:0.611039
[79]	train-mlogloss:0.581734	val-mlogloss:0.609774
[80]	train-mlogloss:0.580268	val-mlogloss:0.608554
[81]	train-mlogloss:0.578785	val-mlogloss:0.607345
[82]	train-mlogloss:0.577278	val-mlogloss:0.606117
[83]	train-mlogloss:0.575804	val-mlogloss:0.604974
[84]	train-mlogloss:0.574357	val-mlogloss:0.60382
[85]	train-mlogloss:0.572941	val-mlogloss:0.602667
[86]	train-mlogloss:0.571605	val-mlogloss:0.60161
[87]	train-mlogloss:0.570198	val-mlogloss:0.600547
[88]	train-mlogloss:0.568929	val-mlogloss:0.599592
[89]	train-mlogloss:0.567643	val-mlogloss:0.598599
[90]	train-mlogloss:0.566372	val-mlogloss:0.59768
[91]	train-mlogloss:0.565085	val-mlogloss:0.596689
[92]	train-mlogloss:0.563884	val-mlogloss:0.59582
[93]	train-mlogloss:0.562644	val-mlogloss:0.594855
[94]	train-mlogloss:0.561561	val-mlogloss:0.594006
[95]	train-mlogloss:0.560391	val-mlogloss:0.593116
[96]	train-mlogloss:0.559274	val-mlogloss:0.59229
[97]	train-mlogloss:0.558084	val-mlogloss:0.591398
[98]	train-mlogloss:0.556943	val-mlogloss:0.590538
[99]	train-mlogloss:0.555858	val-mlogloss:0.589803
[100]	train-mlogloss:0.554837	val-mlogloss:0.589035
[101]	train-mlogloss:0.553805	val-mlogloss:0.58836
[102]	train-mlogloss:0.552648	val-mlogloss:0.587532
[103]	train-mlogloss:0.551648	val-mlogloss:0.586783
[104]	train-mlogloss:0.550663	val-mlogloss:0.586083
[105]	train-mlogloss:0.549617	val-mlogloss:0.585348
[106]	train-mlogloss:0.54865	val-mlogloss:0.584681
[107]	train-mlogloss:0.547785	val-mlogloss:0.584105
[108]	train-mlogloss:0.546879	val-mlogloss:0.583442
[109]	train-mlogloss:0.545901	val-mlogloss:0.58276
[110]	train-mlogloss:0.544986	val-mlogloss:0.582161
[111]	train-mlogloss:0.544139	val-mlogloss:0.581622
[112]	train-mlogloss:0.543167	val-mlogloss:0.580955
[113]	train-mlogloss:0.542304	val-mlogloss:0.58035
[114]	train-mlogloss:0.541369	val-mlogloss:0.57978
[115]	train-mlogloss:0.540522	val-mlogloss:0.579216
[116]	train-mlogloss:0.539658	val-mlogloss:0.578638
[117]	train-mlogloss:0.538679	val-mlogloss:0.578041
[118]	train-mlogloss:0.537863	val-mlogloss:0.577519
[119]	train-mlogloss:0.537107	val-mlogloss:0.576979
[120]	train-mlogloss:0.536314	val-mlogloss:0.576421
[121]	train-mlogloss:0.535567	val-mlogloss:0.575928
[122]	train-mlogloss:0.534803	val-mlogloss:0.575442
[123]	train-mlogloss:0.534078	val-mlogloss:0.574993
[124]	train-mlogloss:0.533389	val-mlogloss:0.574526
[125]	train-mlogloss:0.532703	val-mlogloss:0.574091
[126]	train-mlogloss:0.532017	val-mlogloss:0.573663
[127]	train-mlogloss:0.531309	val-mlogloss:0.573253
[128]	train-mlogloss:0.53063	val-mlogloss:0.572846
[129]	train-mlogloss:0.529968	val-mlogloss:0.572415
[130]	train-mlogloss:0.529335	val-mlogloss:0.572049
[131]	train-mlogloss:0.528685	val-mlogloss:0.571711
[132]	train-mlogloss:0.528061	val-mlogloss:0.571366
[133]	train-mlogloss:0.527301	val-mlogloss:0.570923
[134]	train-mlogloss:0.526539	val-mlogloss:0.570497
[135]	train-mlogloss:0.525815	val-mlogloss:0.570027
[136]	train-mlogloss:0.525122	val-mlogloss:0.569657
[137]	train-mlogloss:0.524489	val-mlogloss:0.569284
[138]	train-mlogloss:0.523731	val-mlogloss:0.568811
[139]	train-mlogloss:0.522972	val-mlogloss:0.56842
[140]	train-mlogloss:0.5223	val-mlogloss:0.568048
[141]	train-mlogloss:0.521723	val-mlogloss:0.567638
[142]	train-mlogloss:0.521182	val-mlogloss:0.567328
[143]	train-mlogloss:0.52058	val-mlogloss:0.566909
[144]	train-mlogloss:0.520076	val-mlogloss:0.566641
[145]	train-mlogloss:0.519462	val-mlogloss:0.566317
[146]	train-mlogloss:0.518859	val-mlogloss:0.565986
[147]	train-mlogloss:0.518194	val-mlogloss:0.565587
[148]	train-mlogloss:0.51744	val-mlogloss:0.565218
[149]	train-mlogloss:0.516725	val-mlogloss:0.564843
[150]	train-mlogloss:0.516055	val-mlogloss:0.564456
[151]	train-mlogloss:0.515416	val-mlogloss:0.56407
[152]	train-mlogloss:0.514901	val-mlogloss:0.563762
[153]	train-mlogloss:0.514311	val-mlogloss:0.563434
[154]	train-mlogloss:0.513734	val-mlogloss:0.563169
[155]	train-mlogloss:0.513167	val-mlogloss:0.562879
[156]	train-mlogloss:0.512589	val-mlogloss:0.562547
[157]	train-mlogloss:0.511944	val-mlogloss:0.562145
[158]	train-mlogloss:0.51138	val-mlogloss:0.561897
[159]	train-mlogloss:0.510808	val-mlogloss:0.561551
[160]	train-mlogloss:0.510272	val-mlogloss:0.561292
[161]	train-mlogloss:0.509834	val-mlogloss:0.56101
[162]	train-mlogloss:0.509298	val-mlogloss:0.560671
[163]	train-mlogloss:0.50881	val-mlogloss:0.560378
[164]	train-mlogloss:0.508305	val-mlogloss:0.56017
[165]	train-mlogloss:0.507741	val-mlogloss:0.55989
[166]	train-mlogloss:0.507189	val-mlogloss:0.559571
[167]	train-mlogloss:0.506647	val-mlogloss:0.559371
[168]	train-mlogloss:0.506029	val-mlogloss:0.558949
[169]	train-mlogloss:0.505543	val-mlogloss:0.558686
[170]	train-mlogloss:0.505113	val-mlogloss:0.558437
[171]	train-mlogloss:0.504671	val-mlogloss:0.558226
[172]	train-mlogloss:0.504233	val-mlogloss:0.558007
[173]	train-mlogloss:0.503679	val-mlogloss:0.557749
[174]	train-mlogloss:0.503192	val-mlogloss:0.557459
[175]	train-mlogloss:0.502703	val-mlogloss:0.557235
[176]	train-mlogloss:0.502156	val-mlogloss:0.557024
[177]	train-mlogloss:0.501658	val-mlogloss:0.556775
[178]	train-mlogloss:0.501222	val-mlogloss:0.556531
[179]	train-mlogloss:0.500667	val-mlogloss:0.55634
[180]	train-mlogloss:0.500342	val-mlogloss:0.55616
[181]	train-mlogloss:0.499914	val-mlogloss:0.555947
[182]	train-mlogloss:0.499343	val-mlogloss:0.555745
[183]	train-mlogloss:0.498898	val-mlogloss:0.555535
[184]	train-mlogloss:0.49826	val-mlogloss:0.555282
[185]	train-mlogloss:0.497954	val-mlogloss:0.555067
[186]	train-mlogloss:0.49751	val-mlogloss:0.554851
[187]	train-mlogloss:0.49703	val-mlogloss:0.554695
[188]	train-mlogloss:0.496589	val-mlogloss:0.554523
[189]	train-mlogloss:0.495993	val-mlogloss:0.554334
[190]	train-mlogloss:0.495666	val-mlogloss:0.554193
[191]	train-mlogloss:0.495252	val-mlogloss:0.554002
[192]	train-mlogloss:0.494855	val-mlogloss:0.55381
[193]	train-mlogloss:0.494441	val-mlogloss:0.553616
[194]	train-mlogloss:0.494088	val-mlogloss:0.55348
[195]	train-mlogloss:0.493671	val-mlogloss:0.553313
[196]	train-mlogloss:0.493239	val-mlogloss:0.553137
[197]	train-mlogloss:0.49268	val-mlogloss:0.552878
[198]	train-mlogloss:0.492216	val-mlogloss:0.552693
[199]	train-mlogloss:0.49184	val-mlogloss:0.552551
[200]	train-mlogloss:0.491429	val-mlogloss:0.552429
[201]	train-mlogloss:0.490851	val-mlogloss:0.552207
[202]	train-mlogloss:0.490464	val-mlogloss:0.552086
[203]	train-mlogloss:0.489891	val-mlogloss:0.551866
[204]	train-mlogloss:0.489437	val-mlogloss:0.551619
[205]	train-mlogloss:0.488978	val-mlogloss:0.55144
[206]	train-mlogloss:0.488494	val-mlogloss:0.551249
[207]	train-mlogloss:0.488092	val-mlogloss:0.551073
[208]	train-mlogloss:0.487638	val-mlogloss:0.550952
[209]	train-mlogloss:0.487065	val-mlogloss:0.550739
[210]	train-mlogloss:0.486616	val-mlogloss:0.55058
[211]	train-mlogloss:0.48622	val-mlogloss:0.55042
[212]	train-mlogloss:0.485853	val-mlogloss:0.5503
[213]	train-mlogloss:0.485378	val-mlogloss:0.550088
[214]	train-mlogloss:0.485036	val-mlogloss:0.549964
[215]	train-mlogloss:0.484552	val-mlogloss:0.549798
[216]	train-mlogloss:0.484269	val-mlogloss:0.54968
[217]	train-mlogloss:0.483891	val-mlogloss:0.549583
[218]	train-mlogloss:0.48347	val-mlogloss:0.549424
[219]	train-mlogloss:0.483091	val-mlogloss:0.549273
[220]	train-mlogloss:0.482798	val-mlogloss:0.54916
[221]	train-mlogloss:0.482357	val-mlogloss:0.549014
[222]	train-mlogloss:0.48193	val-mlogloss:0.548858
[223]	train-mlogloss:0.481492	val-mlogloss:0.54869
[224]	train-mlogloss:0.480971	val-mlogloss:0.548494
[225]	train-mlogloss:0.480581	val-mlogloss:0.548341
[226]	train-mlogloss:0.480097	val-mlogloss:0.548211
[227]	train-mlogloss:0.479648	val-mlogloss:0.548091
[228]	train-mlogloss:0.47921	val-mlogloss:0.547966
[229]	train-mlogloss:0.478926	val-mlogloss:0.547817
[230]	train-mlogloss:0.478524	val-mlogloss:0.547653
[231]	train-mlogloss:0.478152	val-mlogloss:0.54751
[232]	train-mlogloss:0.477809	val-mlogloss:0.54737
[233]	train-mlogloss:0.477444	val-mlogloss:0.547203
[234]	train-mlogloss:0.477026	val-mlogloss:0.54705
[235]	train-mlogloss:0.476673	val-mlogloss:0.546912
[236]	train-mlogloss:0.47627	val-mlogloss:0.546745
[237]	train-mlogloss:0.47585	val-mlogloss:0.546651
[238]	train-mlogloss:0.475429	val-mlogloss:0.546494
[239]	train-mlogloss:0.47509	val-mlogloss:0.546328
[240]	train-mlogloss:0.474733	val-mlogloss:0.546159
[241]	train-mlogloss:0.474366	val-mlogloss:0.546064
[242]	train-mlogloss:0.473955	val-mlogloss:0.545925
[243]	train-mlogloss:0.473698	val-mlogloss:0.545821
[244]	train-mlogloss:0.473255	val-mlogloss:0.545753
[245]	train-mlogloss:0.473001	val-mlogloss:0.545607
[246]	train-mlogloss:0.472593	val-mlogloss:0.545528
[247]	train-mlogloss:0.472184	val-mlogloss:0.545423
[248]	train-mlogloss:0.47187	val-mlogloss:0.545307
[249]	train-mlogloss:0.47159	val-mlogloss:0.545226
[250]	train-mlogloss:0.471295	val-mlogloss:0.545095
[251]	train-mlogloss:0.47101	val-mlogloss:0.544998
[252]	train-mlogloss:0.470636	val-mlogloss:0.544832
[253]	train-mlogloss:0.47021	val-mlogloss:0.544694
[254]	train-mlogloss:0.469853	val-mlogloss:0.54461
[255]	train-mlogloss:0.469462	val-mlogloss:0.544507
[256]	train-mlogloss:0.46914	val-mlogloss:0.544413
[257]	train-mlogloss:0.468804	val-mlogloss:0.544286
[258]	train-mlogloss:0.468397	val-mlogloss:0.544181
[259]	train-mlogloss:0.468127	val-mlogloss:0.544077
[260]	train-mlogloss:0.467721	val-mlogloss:0.543944
[261]	train-mlogloss:0.467431	val-mlogloss:0.543861
[262]	train-mlogloss:0.467097	val-mlogloss:0.543734
[263]	train-mlogloss:0.466649	val-mlogloss:0.54362
[264]	train-mlogloss:0.466319	val-mlogloss:0.543565
[265]	train-mlogloss:0.466038	val-mlogloss:0.54347
[266]	train-mlogloss:0.465789	val-mlogloss:0.543377
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[848]	train-mlogloss:0.340182	val-mlogloss:0.529056
[849]	train-mlogloss:0.340047	val-mlogloss:0.529058
[850]	train-mlogloss:0.339936	val-mlogloss:0.529047
[851]	train-mlogloss:0.339801	val-mlogloss:0.529016
[852]	train-mlogloss:0.339628	val-mlogloss:0.52904
[853]	train-mlogloss:0.339452	val-mlogloss:0.529004
[854]	train-mlogloss:0.339274	val-mlogloss:0.52898
[855]	train-mlogloss:0.339145	val-mlogloss:0.528971
[856]	train-mlogloss:0.338951	val-mlogloss:0.528997
[857]	train-mlogloss:0.338813	val-mlogloss:0.529011
[858]	train-mlogloss:0.338664	val-mlogloss:0.529022
[859]	train-mlogloss:0.338537	val-mlogloss:0.529038
[860]	train-mlogloss:0.338379	val-mlogloss:0.529039
[861]	train-mlogloss:0.338255	val-mlogloss:0.52904
[862]	train-mlogloss:0.338115	val-mlogloss:0.529058
Stopping. Best iteration:
[812]	train-mlogloss:0.345931	val-mlogloss:0.528806

[0]	train-mlogloss:1.078	val-mlogloss:1.07854
Multiple eval metrics have been passed: 'val-mlogloss' will be used for early stopping.

Will train until val-mlogloss hasn't improved in 50 rounds.
[1]	train-mlogloss:1.05838	val-mlogloss:1.05926
[2]	train-mlogloss:1.0395	val-mlogloss:1.04091
[3]	train-mlogloss:1.02156	val-mlogloss:1.02338
[4]	train-mlogloss:1.00447	val-mlogloss:1.00668
[5]	train-mlogloss:0.988109	val-mlogloss:0.990719
[6]	train-mlogloss:0.97252	val-mlogloss:0.975535
[7]	train-mlogloss:0.957648	val-mlogloss:0.96101
[8]	train-mlogloss:0.943645	val-mlogloss:0.947351
[9]	train-mlogloss:0.929912	val-mlogloss:0.934029
[10]	train-mlogloss:0.916797	val-mlogloss:0.921278
[11]	train-mlogloss:0.904248	val-mlogloss:0.908961
[12]	train-mlogloss:0.892116	val-mlogloss:0.897169
[13]	train-mlogloss:0.880533	val-mlogloss:0.885966
[14]	train-mlogloss:0.869777	val-mlogloss:0.875586
[15]	train-mlogloss:0.859157	val-mlogloss:0.865385
[16]	train-mlogloss:0.848955	val-mlogloss:0.85548
[17]	train-mlogloss:0.839074	val-mlogloss:0.845915
[18]	train-mlogloss:0.829461	val-mlogloss:0.836576
[19]	train-mlogloss:0.820252	val-mlogloss:0.827593
[20]	train-mlogloss:0.811411	val-mlogloss:0.819085
[21]	train-mlogloss:0.802868	val-mlogloss:0.810883
[22]	train-mlogloss:0.79472	val-mlogloss:0.802979
[23]	train-mlogloss:0.78682	val-mlogloss:0.795381
[24]	train-mlogloss:0.779531	val-mlogloss:0.788368
[25]	train-mlogloss:0.772252	val-mlogloss:0.781388
[26]	train-mlogloss:0.765164	val-mlogloss:0.774643
[27]	train-mlogloss:0.758381	val-mlogloss:0.768154
[28]	train-mlogloss:0.751713	val-mlogloss:0.761825
[29]	train-mlogloss:0.745232	val-mlogloss:0.755654
[30]	train-mlogloss:0.739061	val-mlogloss:0.749747
[31]	train-mlogloss:0.733079	val-mlogloss:0.744019
[32]	train-mlogloss:0.727369	val-mlogloss:0.738503
[33]	train-mlogloss:0.721756	val-mlogloss:0.733211
[34]	train-mlogloss:0.716399	val-mlogloss:0.72808
[35]	train-mlogloss:0.711077	val-mlogloss:0.72303
[36]	train-mlogloss:0.706048	val-mlogloss:0.7182
[37]	train-mlogloss:0.701221	val-mlogloss:0.71359
[38]	train-mlogloss:0.696473	val-mlogloss:0.709147
[39]	train-mlogloss:0.691877	val-mlogloss:0.704882
[40]	train-mlogloss:0.687404	val-mlogloss:0.700675
[41]	train-mlogloss:0.683091	val-mlogloss:0.696693
[42]	train-mlogloss:0.678879	val-mlogloss:0.692759
[43]	train-mlogloss:0.67483	val-mlogloss:0.68896
[44]	train-mlogloss:0.670928	val-mlogloss:0.685356
[45]	train-mlogloss:0.667148	val-mlogloss:0.68181
[46]	train-mlogloss:0.663428	val-mlogloss:0.678469
[47]	train-mlogloss:0.659945	val-mlogloss:0.675224
[48]	train-mlogloss:0.656485	val-mlogloss:0.672077
[49]	train-mlogloss:0.653107	val-mlogloss:0.66905
[50]	train-mlogloss:0.649844	val-mlogloss:0.666073
[51]	train-mlogloss:0.646588	val-mlogloss:0.66319
[52]	train-mlogloss:0.643418	val-mlogloss:0.660272
[53]	train-mlogloss:0.64037	val-mlogloss:0.657494
[54]	train-mlogloss:0.637434	val-mlogloss:0.654836
[55]	train-mlogloss:0.634658	val-mlogloss:0.652281
[56]	train-mlogloss:0.631957	val-mlogloss:0.649871
[57]	train-mlogloss:0.629209	val-mlogloss:0.647372
[58]	train-mlogloss:0.626469	val-mlogloss:0.644922
[59]	train-mlogloss:0.62394	val-mlogloss:0.642679
[60]	train-mlogloss:0.621431	val-mlogloss:0.640419
[61]	train-mlogloss:0.618958	val-mlogloss:0.638179
[62]	train-mlogloss:0.616554	val-mlogloss:0.636021
[63]	train-mlogloss:0.614231	val-mlogloss:0.633987
[64]	train-mlogloss:0.611928	val-mlogloss:0.631921
[65]	train-mlogloss:0.609687	val-mlogloss:0.629971
[66]	train-mlogloss:0.607554	val-mlogloss:0.628088
[67]	train-mlogloss:0.605461	val-mlogloss:0.626329
[68]	train-mlogloss:0.603374	val-mlogloss:0.624502
[69]	train-mlogloss:0.601335	val-mlogloss:0.622744
[70]	train-mlogloss:0.599369	val-mlogloss:0.621052
[71]	train-mlogloss:0.597452	val-mlogloss:0.619411
[72]	train-mlogloss:0.595599	val-mlogloss:0.617778
[73]	train-mlogloss:0.593666	val-mlogloss:0.61616
[74]	train-mlogloss:0.591857	val-mlogloss:0.614607
[75]	train-mlogloss:0.590084	val-mlogloss:0.613155
[76]	train-mlogloss:0.588395	val-mlogloss:0.611659
[77]	train-mlogloss:0.586673	val-mlogloss:0.610093
[78]	train-mlogloss:0.585048	val-mlogloss:0.608773
[79]	train-mlogloss:0.583448	val-mlogloss:0.607423
[80]	train-mlogloss:0.581938	val-mlogloss:0.606183
[81]	train-mlogloss:0.580435	val-mlogloss:0.604947
[82]	train-mlogloss:0.579007	val-mlogloss:0.603798
[83]	train-mlogloss:0.577557	val-mlogloss:0.602594
[84]	train-mlogloss:0.576182	val-mlogloss:0.601433
[85]	train-mlogloss:0.574801	val-mlogloss:0.600222
[86]	train-mlogloss:0.573377	val-mlogloss:0.599003
[87]	train-mlogloss:0.572076	val-mlogloss:0.597922
[88]	train-mlogloss:0.57074	val-mlogloss:0.596781
[89]	train-mlogloss:0.569409	val-mlogloss:0.595688
[90]	train-mlogloss:0.568155	val-mlogloss:0.594672
[91]	train-mlogloss:0.566838	val-mlogloss:0.593646
[92]	train-mlogloss:0.565568	val-mlogloss:0.592586
[93]	train-mlogloss:0.564356	val-mlogloss:0.591632
[94]	train-mlogloss:0.563111	val-mlogloss:0.590669
[95]	train-mlogloss:0.562065	val-mlogloss:0.589811
[96]	train-mlogloss:0.560951	val-mlogloss:0.588941
[97]	train-mlogloss:0.559821	val-mlogloss:0.58811
[98]	train-mlogloss:0.558781	val-mlogloss:0.587279
[99]	train-mlogloss:0.557637	val-mlogloss:0.586385
[100]	train-mlogloss:0.556571	val-mlogloss:0.585549
[101]	train-mlogloss:0.555507	val-mlogloss:0.584762
[102]	train-mlogloss:0.554522	val-mlogloss:0.583991
[103]	train-mlogloss:0.553528	val-mlogloss:0.583171
[104]	train-mlogloss:0.552509	val-mlogloss:0.582413
[105]	train-mlogloss:0.551488	val-mlogloss:0.581624
[106]	train-mlogloss:0.550477	val-mlogloss:0.580861
[107]	train-mlogloss:0.549536	val-mlogloss:0.580146
[108]	train-mlogloss:0.548559	val-mlogloss:0.579415
[109]	train-mlogloss:0.547608	val-mlogloss:0.578645
[110]	train-mlogloss:0.546813	val-mlogloss:0.578095
[111]	train-mlogloss:0.545947	val-mlogloss:0.577443
[112]	train-mlogloss:0.545066	val-mlogloss:0.576827
[113]	train-mlogloss:0.544068	val-mlogloss:0.576131
[114]	train-mlogloss:0.543099	val-mlogloss:0.575467
[115]	train-mlogloss:0.542191	val-mlogloss:0.574828
[116]	train-mlogloss:0.541443	val-mlogloss:0.574251
[117]	train-mlogloss:0.540685	val-mlogloss:0.573709
[118]	train-mlogloss:0.53988	val-mlogloss:0.5731
[119]	train-mlogloss:0.539055	val-mlogloss:0.572553
[120]	train-mlogloss:0.538308	val-mlogloss:0.572008
[121]	train-mlogloss:0.537532	val-mlogloss:0.5715
[122]	train-mlogloss:0.536817	val-mlogloss:0.571029
[123]	train-mlogloss:0.5361	val-mlogloss:0.570529
[124]	train-mlogloss:0.535387	val-mlogloss:0.570097
[125]	train-mlogloss:0.534624	val-mlogloss:0.569509
[126]	train-mlogloss:0.533941	val-mlogloss:0.569051
[127]	train-mlogloss:0.533261	val-mlogloss:0.568553
[128]	train-mlogloss:0.532662	val-mlogloss:0.568142
[129]	train-mlogloss:0.531976	val-mlogloss:0.567702
[130]	train-mlogloss:0.531263	val-mlogloss:0.56726
[131]	train-mlogloss:0.530579	val-mlogloss:0.566804
[132]	train-mlogloss:0.529914	val-mlogloss:0.566355
[133]	train-mlogloss:0.529146	val-mlogloss:0.565838
[134]	train-mlogloss:0.528372	val-mlogloss:0.565325
[135]	train-mlogloss:0.527586	val-mlogloss:0.56482
[136]	train-mlogloss:0.526946	val-mlogloss:0.5644
[137]	train-mlogloss:0.526318	val-mlogloss:0.563997
[138]	train-mlogloss:0.525646	val-mlogloss:0.56357
[139]	train-mlogloss:0.525087	val-mlogloss:0.563192
[140]	train-mlogloss:0.524516	val-mlogloss:0.562833
[141]	train-mlogloss:0.523932	val-mlogloss:0.562502
[142]	train-mlogloss:0.52337	val-mlogloss:0.562144
[143]	train-mlogloss:0.522797	val-mlogloss:0.56179
[144]	train-mlogloss:0.522291	val-mlogloss:0.561436
[145]	train-mlogloss:0.52165	val-mlogloss:0.561031
[146]	train-mlogloss:0.520978	val-mlogloss:0.560648
[147]	train-mlogloss:0.520288	val-mlogloss:0.560212
[148]	train-mlogloss:0.519684	val-mlogloss:0.55979
[149]	train-mlogloss:0.518985	val-mlogloss:0.559403
[150]	train-mlogloss:0.518446	val-mlogloss:0.559064
[151]	train-mlogloss:0.517833	val-mlogloss:0.558647
[152]	train-mlogloss:0.517251	val-mlogloss:0.558316
[153]	train-mlogloss:0.516719	val-mlogloss:0.558012
[154]	train-mlogloss:0.516263	val-mlogloss:0.557748
[155]	train-mlogloss:0.515715	val-mlogloss:0.557373
[156]	train-mlogloss:0.515111	val-mlogloss:0.556965
[157]	train-mlogloss:0.514464	val-mlogloss:0.556587
[158]	train-mlogloss:0.513961	val-mlogloss:0.55637
[159]	train-mlogloss:0.513409	val-mlogloss:0.555999
[160]	train-mlogloss:0.512925	val-mlogloss:0.555658
[161]	train-mlogloss:0.512395	val-mlogloss:0.555377
[162]	train-mlogloss:0.511972	val-mlogloss:0.555174
[163]	train-mlogloss:0.511404	val-mlogloss:0.554854
[164]	train-mlogloss:0.510855	val-mlogloss:0.554543
[165]	train-mlogloss:0.510246	val-mlogloss:0.554223
[166]	train-mlogloss:0.509603	val-mlogloss:0.553921
[167]	train-mlogloss:0.509022	val-mlogloss:0.553626
[168]	train-mlogloss:0.508429	val-mlogloss:0.553329
[169]	train-mlogloss:0.507996	val-mlogloss:0.553115
[170]	train-mlogloss:0.507547	val-mlogloss:0.55287
[171]	train-mlogloss:0.507051	val-mlogloss:0.552572
[172]	train-mlogloss:0.506624	val-mlogloss:0.552307
[173]	train-mlogloss:0.506127	val-mlogloss:0.552045
[174]	train-mlogloss:0.505663	val-mlogloss:0.551771
[175]	train-mlogloss:0.505127	val-mlogloss:0.551501
[176]	train-mlogloss:0.504714	val-mlogloss:0.551294
[177]	train-mlogloss:0.504072	val-mlogloss:0.550971
[178]	train-mlogloss:0.503553	val-mlogloss:0.550729
[179]	train-mlogloss:0.503046	val-mlogloss:0.550457
[180]	train-mlogloss:0.502547	val-mlogloss:0.550219
[181]	train-mlogloss:0.501997	val-mlogloss:0.549913
[182]	train-mlogloss:0.501533	val-mlogloss:0.54967
[183]	train-mlogloss:0.501068	val-mlogloss:0.549488
[184]	train-mlogloss:0.500482	val-mlogloss:0.549217
[185]	train-mlogloss:0.500044	val-mlogloss:0.54897
[186]	train-mlogloss:0.499613	val-mlogloss:0.548765
[187]	train-mlogloss:0.499127	val-mlogloss:0.548537
[188]	train-mlogloss:0.498686	val-mlogloss:0.548272
[189]	train-mlogloss:0.498142	val-mlogloss:0.547992
[190]	train-mlogloss:0.497801	val-mlogloss:0.547782
[191]	train-mlogloss:0.497219	val-mlogloss:0.547497
[192]	train-mlogloss:0.49669	val-mlogloss:0.547308
[193]	train-mlogloss:0.496191	val-mlogloss:0.54703
[194]	train-mlogloss:0.495725	val-mlogloss:0.546839
[195]	train-mlogloss:0.495359	val-mlogloss:0.546669
[196]	train-mlogloss:0.49494	val-mlogloss:0.54651
[197]	train-mlogloss:0.494472	val-mlogloss:0.546267
[198]	train-mlogloss:0.494085	val-mlogloss:0.546121
[199]	train-mlogloss:0.493582	val-mlogloss:0.545925
[200]	train-mlogloss:0.493174	val-mlogloss:0.54576
[201]	train-mlogloss:0.492775	val-mlogloss:0.545578
[202]	train-mlogloss:0.492451	val-mlogloss:0.545403
[203]	train-mlogloss:0.492091	val-mlogloss:0.545262
[204]	train-mlogloss:0.491646	val-mlogloss:0.545066
[205]	train-mlogloss:0.491296	val-mlogloss:0.544885
[206]	train-mlogloss:0.490905	val-mlogloss:0.544703
[207]	train-mlogloss:0.490515	val-mlogloss:0.544512
[208]	train-mlogloss:0.490084	val-mlogloss:0.544313
[209]	train-mlogloss:0.489776	val-mlogloss:0.544121
[210]	train-mlogloss:0.489361	val-mlogloss:0.54389
[211]	train-mlogloss:0.488899	val-mlogloss:0.543696
[212]	train-mlogloss:0.488439	val-mlogloss:0.543534
[213]	train-mlogloss:0.487968	val-mlogloss:0.543335
[214]	train-mlogloss:0.487517	val-mlogloss:0.543197
[215]	train-mlogloss:0.487138	val-mlogloss:0.543061
[216]	train-mlogloss:0.486747	val-mlogloss:0.542873
[217]	train-mlogloss:0.486331	val-mlogloss:0.542708
[218]	train-mlogloss:0.485958	val-mlogloss:0.542498
[219]	train-mlogloss:0.485549	val-mlogloss:0.54242
[220]	train-mlogloss:0.485127	val-mlogloss:0.54226
[221]	train-mlogloss:0.484838	val-mlogloss:0.542113
[222]	train-mlogloss:0.484514	val-mlogloss:0.541951
[223]	train-mlogloss:0.484086	val-mlogloss:0.541822
[224]	train-mlogloss:0.483654	val-mlogloss:0.541698
[225]	train-mlogloss:0.483205	val-mlogloss:0.541554
[226]	train-mlogloss:0.482871	val-mlogloss:0.541421
[227]	train-mlogloss:0.482407	val-mlogloss:0.541173
[228]	train-mlogloss:0.481985	val-mlogloss:0.54095
[229]	train-mlogloss:0.481638	val-mlogloss:0.540842
[230]	train-mlogloss:0.481312	val-mlogloss:0.540718
[231]	train-mlogloss:0.48088	val-mlogloss:0.540563
[232]	train-mlogloss:0.48057	val-mlogloss:0.540435
[233]	train-mlogloss:0.480313	val-mlogloss:0.540336
[234]	train-mlogloss:0.479865	val-mlogloss:0.540166
[235]	train-mlogloss:0.47938	val-mlogloss:0.539981
[236]	train-mlogloss:0.478965	val-mlogloss:0.539845
[237]	train-mlogloss:0.478708	val-mlogloss:0.539706
[238]	train-mlogloss:0.478378	val-mlogloss:0.539599
[239]	train-mlogloss:0.478048	val-mlogloss:0.539468
[240]	train-mlogloss:0.477626	val-mlogloss:0.539287
[241]	train-mlogloss:0.477329	val-mlogloss:0.539173
[242]	train-mlogloss:0.477034	val-mlogloss:0.53907
[243]	train-mlogloss:0.4767	val-mlogloss:0.538949
[244]	train-mlogloss:0.47633	val-mlogloss:0.538832
[245]	train-mlogloss:0.475924	val-mlogloss:0.538685
[246]	train-mlogloss:0.475476	val-mlogloss:0.538537
[247]	train-mlogloss:0.475135	val-mlogloss:0.5384
[248]	train-mlogloss:0.474746	val-mlogloss:0.538285
[249]	train-mlogloss:0.4743	val-mlogloss:0.538137
[250]	train-mlogloss:0.473915	val-mlogloss:0.538076
[251]	train-mlogloss:0.473628	val-mlogloss:0.538012
[252]	train-mlogloss:0.473262	val-mlogloss:0.537888
[253]	train-mlogloss:0.472957	val-mlogloss:0.537748
[254]	train-mlogloss:0.472629	val-mlogloss:0.537644
[255]	train-mlogloss:0.472147	val-mlogloss:0.537508
[256]	train-mlogloss:0.471879	val-mlogloss:0.537434
[257]	train-mlogloss:0.471513	val-mlogloss:0.537332
[258]	train-mlogloss:0.471159	val-mlogloss:0.537182
[259]	train-mlogloss:0.470827	val-mlogloss:0.537081
[260]	train-mlogloss:0.47036	val-mlogloss:0.536875
[261]	train-mlogloss:0.470005	val-mlogloss:0.53677
[262]	train-mlogloss:0.469763	val-mlogloss:0.536685
[263]	train-mlogloss:0.469381	val-mlogloss:0.536551
[264]	train-mlogloss:0.469128	val-mlogloss:0.536436
[265]	train-mlogloss:0.468906	val-mlogloss:0.536342
[266]	train-mlogloss:0.468611	val-mlogloss:0.536205
[267]	train-mlogloss:0.468315	val-mlogloss:0.536078
[268]	train-mlogloss:0.467987	val-mlogloss:0.535962
[269]	train-mlogloss:0.467701	val-mlogloss:0.535843
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