In [1]:

    

%load_ext autoreload
%autoreload 2
%matplotlib inline


    

In [2]:

    

from fastai.imports import *
from fastai.structured import *

import time
from gplearn.genetic import SymbolicTransformer
from pandas_summary import DataFrameSummary
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from IPython.display import display
import xgboost as xgb
import lightgbm as lgb
from catboost import CatBoostClassifier
import gc, mlcrate
from scipy.cluster import hierarchy as hc
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.metrics import  roc_auc_score, log_loss
from sklearn.model_selection import StratifiedKFold
def ignore_warn(*args, **kwargs):
    pass
warnings.warn = ignore_warn
import category_encoders as cat_ed
#will ignore all warning from sklearn, seaborn etc..

pd.option_context("display.max_rows", 1000);
pd.option_context("display.max_columns", 1000);


    

In [3]:

    

PATH = os.getcwd();
PATH


    

    
Out[3]:





'D:\\Github\\fastai\\courses\\ml1'

In [4]:

    

df_raw  = pd.read_csv(f'{PATH}\\AV_Stud\\01_05_18_category_encoder_df_train.csv', low_memory= False)
df_test = pd.read_csv(f'{PATH}\\AV_Stud\\01_05_18_category_encoder_df_test.csv', low_memory=False)


    

In [5]:

    

target = df_raw.is_pass.values
df_raw.drop('is_pass',axis=1, inplace=True)


    

In [17]:

    

def Intersection(lst1, lst2):
    return list(set(lst1).intersection(lst2))


    

In [37]:

    

len(Intersection(df_raw['trainee_id'].values, df_test['trainee_id'].values)),\
len(Intersection(df_raw['test_id'].values, df_test['test_id'].values))


    

    
Out[37]:





(13857, 188)

In [22]:

    

len(np.unique(df_raw.trainee_id.values)), len(np.unique(df_test.trainee_id.values))


    

    
Out[22]:





(18500, 14779)

In [26]:

    

df_raw['target'] = target
preds = df_test.trainee_id.map(df_raw.groupby('trainee_id')['target'].mean())
preds.fillna(method='ffill', inplace=True)
df_raw.drop('target', axis=1, inplace=True)


    

In [33]:

    

df_raw.head(2)


    

    
Out[33]:







  

    

      

      
col_0
      
col_1
      
col_2
      
col_3
      
col_4
      
col_5
      
col_6
      
col_7
      
col_8
      
col_9
      
program_duration
      
test_id
      
trainee_id
      
city_tier
      
age
      
total_programs_enrolled
      
is_age_39
      
is_age_39_45
      
is_age_45
    
  
  

    

      
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In [38]:

    

df_raw['avg_prog_dur'] = df_raw['program_duration']/df_raw['total_programs_enrolled']
df_test['avg_prog_dur'] = df_test['program_duration']/df_test['total_programs_enrolled']


    

In [14]:

    

df_raw['age'].plot(kind='kde')
plt.vlines(37,0,0.01)


    

    
Out[14]:





<matplotlib.collections.LineCollection at 0x2bf8fd665c0>






    

In [6]:

    

df_raw.head(1)


    

    
Out[6]:







  

    

      

      
col_0
      
col_1
      
col_2
      
col_3
      
col_4
      
col_5
      
col_6
      
col_7
      
col_8
      
col_9
      
program_duration
      
test_id
      
trainee_id
      
city_tier
      
age
      
total_programs_enrolled
      
is_age_39
      
is_age_39_45
      
is_age_45
    
  
  

    

      
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0.0
    
  

In [7]:

    

df_raw.drop(['id'], axis =1, inplace=True)
df_test.drop(['id'], axis =1, inplace=True)


    

In [8]:

    

df_raw['program_type__program_duration'] = df_raw.program_type.str.cat(df_raw.program_duration.astype(str),sep='_')
df_raw['test_id__program_duration'] = df_raw.test_id.astype(str).str.cat(df_raw.program_duration.astype(str),sep='_')
df_raw['test_id__test_type'] = df_raw.test_id.astype(str).str.cat(df_raw.test_type.astype(str),sep='_')
df_raw['test_type__difficulty_level'] = df_raw.test_type.str.cat(df_raw.difficulty_level.astype(str),sep='_')
df_raw['education__gender'] = df_raw.education.str.cat(df_raw.gender.astype(str),sep='_')
df_raw['education__city_tier'] = df_raw.education.str.cat(df_raw.city_tier.astype(str),sep='_')
df_raw['gender__city_tier'] = df_raw.gender.str.cat(df_raw.city_tier.astype(str),sep='_')
df_raw['trainee_engagement_rating']  = df_raw['trainee_engagement_rating'].astype(object)
########################### kind of binning age at trivial level #####################################

df_raw['is_age_39'] = np.zeros(df_raw.shape[0])
my_query = df_raw.query('age<=39.').index
df_raw.iloc[my_query, -1] = 1
df_raw['is_age_39_45'] = np.zeros(df_raw.shape[0])
my_query = df_raw.query('age>=39. & age<=45.').index
df_raw.iloc[my_query, -1] = 1
df_raw['is_age_45'] = np.zeros(df_raw.shape[0])
my_query = df_raw.query('age>=45.').index
df_raw.iloc[my_query, -1] = 1

#######################################################################################################
###################young age (13–30), middle age (31–50) and senior age (51–70)########################
#######################################################################################################

df_raw['age_group'] = np.zeros(df_raw.shape[0])
my_query = df_raw.query('age>=13. & age<=30.').index
df_raw.iloc[my_query, -1] = 'young'
my_query = df_raw.query('age>=31. & age<=50.').index
df_raw.iloc[my_query, -1] = 'middle_aged'
my_query = df_raw.query('age>=51. & age<=70.').index
df_raw.iloc[my_query, -1] = 'senior_aged'
###################################################################################################################
###################################################################################################################
###################################################################################################################

df_test['program_type__program_duration'] = df_test.program_type.str.cat(df_test.program_duration.astype(str),sep='_')
df_test['test_id__program_duration'] = df_test.test_id.astype(str).str.cat(df_test.program_duration.astype(str),sep='_')
df_test['test_id__test_type'] = df_test.test_id.astype(str).str.cat(df_test.test_type.astype(str),sep='_')
df_test['test_type__difficulty_level'] = df_test.test_type.str.cat(df_test.difficulty_level.astype(str),sep='_')
df_test['education__gender'] = df_test.education.str.cat(df_test.gender.astype(str),sep='_')
df_test['education__city_tier'] = df_test.education.str.cat(df_test.city_tier.astype(str),sep='_')
df_test['gender__city_tier'] = df_test.gender.str.cat(df_test.city_tier.astype(str),sep='_')
df_test['trainee_engagement_rating']  = df_test['trainee_engagement_rating'].astype(object)
########################### kind of binning age at trivial level #####################################

df_test['is_age_39'] = np.zeros(df_test.shape[0])
my_query = df_test.query('age<=39.').index
df_test.iloc[my_query, -1] = 1
df_test['is_age_39_45'] = np.zeros(df_test.shape[0])
my_query = df_test.query('age>=39. & age<=45.').index
df_test.iloc[my_query, -1] = 1
df_test['is_age_45'] = np.zeros(df_test.shape[0])
my_query = df_test.query('age>=45.').index
df_test.iloc[my_query, -1] = 1

#######################################################################################################
###################young age (13–30), middle age (31–50) and senior age (51–70)########################
#######################################################################################################

df_test['age_group'] = np.zeros(df_test.shape[0])
my_query = df_test.query('age>=13. & age<=30.').index
df_test.iloc[my_query, -1] = 'young'
my_query = df_test.query('age>=31. & age<=50.').index
df_test.iloc[my_query, -1] = 'middle_aged'
my_query = df_test.query('age>=51. & age<=70.').index
df_test.iloc[my_query, -1] = 'senior_aged';
###############################################################################


    

In [9]:

    

features = df_raw.columns
numeric_features = []
categorical_features = []

for dtype, feature in zip(df_raw.dtypes, df_raw.columns):
    if dtype == object:
        #print(column)
        #print(train_data[column].describe())
        categorical_features.append(feature)
    else:
        numeric_features.append(feature)
categorical_features


    

    
Out[9]:





['program_id',
 'program_type',
 'test_type',
 'difficulty_level',
 'gender',
 'education',
 'is_handicapped',
 'trainee_engagement_rating',
 'program_type__program_duration',
 'test_id__program_duration',
 'test_id__test_type',
 'test_type__difficulty_level',
 'education__gender',
 'education__city_tier',
 'gender__city_tier',
 'age_group']

In [10]:

    

enc = cat_ed.hashing.HashingEncoder(2,cols=categorical_features, n_components= 10,drop_invariant=True)


    

In [11]:

    

enc.fit(df_raw, target)


    

    
Out[11]:





HashingEncoder(cols=['program_id', 'program_type', 'test_type', 'difficulty_level', 'gender', 'education', 'is_handicapped', 'trainee_engagement_rating', 'program_type__program_duration', 'test_id__program_duration', 'test_id__test_type', 'test_type__difficulty_level', 'education__gender', 'education__city_tier', 'gender__city_tier', 'age_group'],
        drop_invariant=True, hash_method='md5', n_components=10,
        return_df=True, verbose=2)

In [12]:

    

df_raw = enc.transform(df_raw)


    

In [13]:

    

df_test = enc.transform(df_test)


    

In [14]:

    

df_raw.shape, df_test.shape


    

    
Out[14]:





((73147, 19), (31349, 19))

In [51]:

    

params = {}
params['booster'] = 'gbtree'
#params['updater'] = 'coord_descent'
params["objective"] = "binary:logistic"
params['eval_metric'] = 'auc'
params["eta"] = 0.05 #0.03
params["subsample"] = .85
params["silent"] = 0
params['verbose'] = 2
params["max_depth"] = 11
params["seed"] = 1
params["max_delta_step"] = 4
params['scale_pos_weight'] =  0.4380049934141978
params["gamma"] = 0.6 #.5 #.1 #.2
params['colsample_bytree'] = 0.9
params['nrounds'] = 2000 #3600 #2000 #4000 #using lower no for demo
params['max_leaves'] = 511


    

In [52]:

    

model, p_train, p_test, imps = mlcrate.xgb.train_kfold(params, df_raw, target, df_test, folds = 7, stratify=target, print_imp='final')


    

    




[mlcrate] Training 7 stratified XGBoost models on training set (73147, 20) with test set (31349, 20)
[mlcrate] Running fold 0, 62697 train samples, 10450 validation samples
[0]	train-auc:0.744755	valid-auc:0.667156
Multiple eval metrics have been passed: 'valid-auc' will be used for early stopping.

Will train until valid-auc hasn't improved in 50 rounds.
[1]	train-auc:0.771816	valid-auc:0.685183
[2]	train-auc:0.779707	valid-auc:0.691154
[3]	train-auc:0.786306	valid-auc:0.696358
[4]	train-auc:0.791158	valid-auc:0.699105
[5]	train-auc:0.794934	valid-auc:0.700866
[6]	train-auc:0.799055	valid-auc:0.702848
[7]	train-auc:0.802149	valid-auc:0.705401
[8]	train-auc:0.804459	valid-auc:0.706938
[9]	train-auc:0.807047	valid-auc:0.708032
[10]	train-auc:0.809039	valid-auc:0.708208
[11]	train-auc:0.811514	valid-auc:0.708505
[12]	train-auc:0.815521	valid-auc:0.70881
[13]	train-auc:0.816756	valid-auc:0.708916
[14]	train-auc:0.818725	valid-auc:0.709598
[15]	train-auc:0.820795	valid-auc:0.710718
[16]	train-auc:0.822277	valid-auc:0.711629
[17]	train-auc:0.823298	valid-auc:0.712839
[18]	train-auc:0.82541	valid-auc:0.713725
[19]	train-auc:0.827231	valid-auc:0.715089
[20]	train-auc:0.828762	valid-auc:0.715326
[21]	train-auc:0.830035	valid-auc:0.715666
[22]	train-auc:0.831798	valid-auc:0.716276
[23]	train-auc:0.833472	valid-auc:0.71702
[24]	train-auc:0.834728	valid-auc:0.716968
[25]	train-auc:0.83673	valid-auc:0.717642
[26]	train-auc:0.837839	valid-auc:0.718002
[27]	train-auc:0.838988	valid-auc:0.718765
[28]	train-auc:0.840681	valid-auc:0.719608
[29]	train-auc:0.842049	valid-auc:0.719992
[30]	train-auc:0.843414	valid-auc:0.720741
[31]	train-auc:0.844097	valid-auc:0.72089
[32]	train-auc:0.845104	valid-auc:0.720896
[33]	train-auc:0.846553	valid-auc:0.721036
[34]	train-auc:0.847293	valid-auc:0.721255
[35]	train-auc:0.848521	valid-auc:0.72193
[36]	train-auc:0.849559	valid-auc:0.722465
[37]	train-auc:0.850658	valid-auc:0.722735
[38]	train-auc:0.851783	valid-auc:0.722948
[39]	train-auc:0.852791	valid-auc:0.723668
[40]	train-auc:0.854249	valid-auc:0.724153
[41]	train-auc:0.854934	valid-auc:0.724452
[42]	train-auc:0.856498	valid-auc:0.724706
[43]	train-auc:0.857857	valid-auc:0.725135
[44]	train-auc:0.858719	valid-auc:0.725384
[45]	train-auc:0.860005	valid-auc:0.725879
[46]	train-auc:0.861101	valid-auc:0.7263
[47]	train-auc:0.862587	valid-auc:0.72663
[48]	train-auc:0.863615	valid-auc:0.726784
[49]	train-auc:0.864803	valid-auc:0.727255
[50]	train-auc:0.866406	valid-auc:0.727727
[51]	train-auc:0.867333	valid-auc:0.728097
[52]	train-auc:0.868362	valid-auc:0.728474
[53]	train-auc:0.86925	valid-auc:0.728613
[54]	train-auc:0.869679	valid-auc:0.728729
[55]	train-auc:0.870234	valid-auc:0.728661
[56]	train-auc:0.871237	valid-auc:0.729003
[57]	train-auc:0.872443	valid-auc:0.729594
[58]	train-auc:0.873094	valid-auc:0.729802
[59]	train-auc:0.87386	valid-auc:0.729621
[60]	train-auc:0.874408	valid-auc:0.729644
[61]	train-auc:0.874896	valid-auc:0.729821
[62]	train-auc:0.875132	valid-auc:0.72997
[63]	train-auc:0.875365	valid-auc:0.730172
[64]	train-auc:0.875906	valid-auc:0.7302
[65]	train-auc:0.876744	valid-auc:0.730502
[66]	train-auc:0.877583	valid-auc:0.730669
[67]	train-auc:0.878822	valid-auc:0.730906
[68]	train-auc:0.879424	valid-auc:0.731134
[69]	train-auc:0.88044	valid-auc:0.731389
[70]	train-auc:0.88058	valid-auc:0.731528
[71]	train-auc:0.8812	valid-auc:0.731692
[72]	train-auc:0.882059	valid-auc:0.73182
[73]	train-auc:0.882936	valid-auc:0.731846
[74]	train-auc:0.883776	valid-auc:0.731951
[75]	train-auc:0.884171	valid-auc:0.732205
[76]	train-auc:0.88445	valid-auc:0.732378
[77]	train-auc:0.885346	valid-auc:0.732319
[78]	train-auc:0.885809	valid-auc:0.732526
[79]	train-auc:0.88591	valid-auc:0.732496
[80]	train-auc:0.886181	valid-auc:0.732418
[81]	train-auc:0.886847	valid-auc:0.732631
[82]	train-auc:0.887509	valid-auc:0.732893
[83]	train-auc:0.887835	valid-auc:0.732993
[84]	train-auc:0.888774	valid-auc:0.733434
[85]	train-auc:0.889627	valid-auc:0.733888
[86]	train-auc:0.88989	valid-auc:0.733929
[87]	train-auc:0.889957	valid-auc:0.73396
[88]	train-auc:0.890411	valid-auc:0.733922
[89]	train-auc:0.891142	valid-auc:0.733985
[90]	train-auc:0.891498	valid-auc:0.733991
[91]	train-auc:0.892423	valid-auc:0.73441
[92]	train-auc:0.893232	valid-auc:0.734504
[93]	train-auc:0.893255	valid-auc:0.734465
[94]	train-auc:0.893838	valid-auc:0.734785
[95]	train-auc:0.894342	valid-auc:0.735031
[96]	train-auc:0.895058	valid-auc:0.735199
[97]	train-auc:0.895373	valid-auc:0.73535
[98]	train-auc:0.896076	valid-auc:0.735503
[99]	train-auc:0.896327	valid-auc:0.735615
[100]	train-auc:0.896901	valid-auc:0.735841
[101]	train-auc:0.897559	valid-auc:0.736233
[102]	train-auc:0.89812	valid-auc:0.736561
[103]	train-auc:0.898192	valid-auc:0.736606
[104]	train-auc:0.898981	valid-auc:0.736808
[105]	train-auc:0.899346	valid-auc:0.736918
[106]	train-auc:0.899806	valid-auc:0.73686
[107]	train-auc:0.90039	valid-auc:0.73691
[108]	train-auc:0.900821	valid-auc:0.737058
[109]	train-auc:0.900992	valid-auc:0.737121
[110]	train-auc:0.901807	valid-auc:0.737261
[111]	train-auc:0.902048	valid-auc:0.737225
[112]	train-auc:0.902219	valid-auc:0.737295
[113]	train-auc:0.90307	valid-auc:0.737538
[114]	train-auc:0.903693	valid-auc:0.73745
[115]	train-auc:0.904056	valid-auc:0.737653
[116]	train-auc:0.904803	valid-auc:0.73792
[117]	train-auc:0.905414	valid-auc:0.737993
[118]	train-auc:0.905864	valid-auc:0.738206
[119]	train-auc:0.907027	valid-auc:0.738618
[120]	train-auc:0.90755	valid-auc:0.738698
[121]	train-auc:0.908001	valid-auc:0.738774
[122]	train-auc:0.908598	valid-auc:0.739032
[123]	train-auc:0.909095	valid-auc:0.739175
[124]	train-auc:0.910014	valid-auc:0.739376
[125]	train-auc:0.910877	valid-auc:0.739439
[126]	train-auc:0.910906	valid-auc:0.739462
[127]	train-auc:0.911249	valid-auc:0.739547
[128]	train-auc:0.911324	valid-auc:0.739591
[129]	train-auc:0.911559	valid-auc:0.739612
[130]	train-auc:0.9117	valid-auc:0.739679
[131]	train-auc:0.912137	valid-auc:0.739789
[132]	train-auc:0.913025	valid-auc:0.740051
[133]	train-auc:0.913253	valid-auc:0.739986
[134]	train-auc:0.913526	valid-auc:0.740082
[135]	train-auc:0.913829	valid-auc:0.740157
[136]	train-auc:0.914268	valid-auc:0.740243
[137]	train-auc:0.914995	valid-auc:0.740705
[138]	train-auc:0.915577	valid-auc:0.74087
[139]	train-auc:0.915898	valid-auc:0.741024
[140]	train-auc:0.916279	valid-auc:0.740845
[141]	train-auc:0.916314	valid-auc:0.74084
[142]	train-auc:0.916645	valid-auc:0.74102
[143]	train-auc:0.916709	valid-auc:0.741054
[144]	train-auc:0.917171	valid-auc:0.741066
[145]	train-auc:0.917325	valid-auc:0.741018
[146]	train-auc:0.918072	valid-auc:0.74124
[147]	train-auc:0.918537	valid-auc:0.74124
[148]	train-auc:0.918913	valid-auc:0.741514
[149]	train-auc:0.919569	valid-auc:0.741607
[150]	train-auc:0.919895	valid-auc:0.741599
[151]	train-auc:0.920372	valid-auc:0.741776
[152]	train-auc:0.920869	valid-auc:0.741917
[153]	train-auc:0.92133	valid-auc:0.741987
[154]	train-auc:0.921811	valid-auc:0.742064
[155]	train-auc:0.922464	valid-auc:0.742248
[156]	train-auc:0.922854	valid-auc:0.742402
[157]	train-auc:0.922968	valid-auc:0.742535
[158]	train-auc:0.92344	valid-auc:0.742819
[159]	train-auc:0.923801	valid-auc:0.742982
[160]	train-auc:0.924011	valid-auc:0.742872
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[1480]	train-auc:0.997654	valid-auc:0.772161
[1481]	train-auc:0.997658	valid-auc:0.77215
[1482]	train-auc:0.997665	valid-auc:0.772193
[1483]	train-auc:0.997669	valid-auc:0.772216
[1484]	train-auc:0.997671	valid-auc:0.772191
[1485]	train-auc:0.997672	valid-auc:0.77219
[1486]	train-auc:0.997676	valid-auc:0.772202
[1487]	train-auc:0.997681	valid-auc:0.772171
[1488]	train-auc:0.997696	valid-auc:0.772162
[1489]	train-auc:0.9977	valid-auc:0.772153
[1490]	train-auc:0.997706	valid-auc:0.772148
[1491]	train-auc:0.997708	valid-auc:0.772142
[1492]	train-auc:0.997716	valid-auc:0.772091
[1493]	train-auc:0.99772	valid-auc:0.772054
[1494]	train-auc:0.997721	valid-auc:0.772089
[1495]	train-auc:0.997729	valid-auc:0.772058
[1496]	train-auc:0.997739	valid-auc:0.772075
[1497]	train-auc:0.997746	valid-auc:0.772134
[1498]	train-auc:0.997752	valid-auc:0.772172
[1499]	train-auc:0.997758	valid-auc:0.772209
[1500]	train-auc:0.99776	valid-auc:0.772197
[1501]	train-auc:0.997761	valid-auc:0.77219
[1502]	train-auc:0.997765	valid-auc:0.772178
[1503]	train-auc:0.997765	valid-auc:0.772196
[1504]	train-auc:0.997769	valid-auc:0.772223
[1505]	train-auc:0.99777	valid-auc:0.772197
[1506]	train-auc:0.997774	valid-auc:0.772223
[1507]	train-auc:0.997776	valid-auc:0.772227
[1508]	train-auc:0.99778	valid-auc:0.772217
[1509]	train-auc:0.997786	valid-auc:0.772226
[1510]	train-auc:0.997786	valid-auc:0.772222
[1511]	train-auc:0.997793	valid-auc:0.772231
[1512]	train-auc:0.997795	valid-auc:0.772234
[1513]	train-auc:0.997798	valid-auc:0.772233
[1514]	train-auc:0.997803	valid-auc:0.772242
[1515]	train-auc:0.997804	valid-auc:0.772266
[1516]	train-auc:0.997804	valid-auc:0.772276
[1517]	train-auc:0.997805	valid-auc:0.772301
[1518]	train-auc:0.997808	valid-auc:0.772328
[1519]	train-auc:0.99781	valid-auc:0.772352
[1520]	train-auc:0.997813	valid-auc:0.772365
[1521]	train-auc:0.997815	valid-auc:0.77236
[1522]	train-auc:0.997817	valid-auc:0.772358
[1523]	train-auc:0.997819	valid-auc:0.772351
[1524]	train-auc:0.997824	valid-auc:0.772377
[1525]	train-auc:0.997828	valid-auc:0.772395
[1526]	train-auc:0.997828	valid-auc:0.772388
[1527]	train-auc:0.99783	valid-auc:0.772376
[1528]	train-auc:0.99783	valid-auc:0.772392
[1529]	train-auc:0.997837	valid-auc:0.772397
[1530]	train-auc:0.99784	valid-auc:0.772417
[1531]	train-auc:0.997841	valid-auc:0.772422
[1532]	train-auc:0.997844	valid-auc:0.772431
[1533]	train-auc:0.997848	valid-auc:0.772456
[1534]	train-auc:0.997851	valid-auc:0.772453
[1535]	train-auc:0.997855	valid-auc:0.772471
[1536]	train-auc:0.997859	valid-auc:0.772471
[1537]	train-auc:0.997859	valid-auc:0.772475
[1538]	train-auc:0.997859	valid-auc:0.772479
[1539]	train-auc:0.99786	valid-auc:0.772478
[1540]	train-auc:0.997864	valid-auc:0.772481
[1541]	train-auc:0.997866	valid-auc:0.772479
[1542]	train-auc:0.997867	valid-auc:0.772479
[1543]	train-auc:0.997869	valid-auc:0.772489
[1544]	train-auc:0.997873	valid-auc:0.772485
[1545]	train-auc:0.997876	valid-auc:0.772505
[1546]	train-auc:0.997878	valid-auc:0.772506
[1547]	train-auc:0.997881	valid-auc:0.772513
[1548]	train-auc:0.997886	valid-auc:0.772537
[1549]	train-auc:0.99789	valid-auc:0.772536
[1550]	train-auc:0.997893	valid-auc:0.772509
[1551]	train-auc:0.997894	valid-auc:0.772473
[1552]	train-auc:0.997893	valid-auc:0.772477
[1553]	train-auc:0.997896	valid-auc:0.772489
[1554]	train-auc:0.9979	valid-auc:0.77248
[1555]	train-auc:0.997901	valid-auc:0.772478
[1556]	train-auc:0.997902	valid-auc:0.772497
[1557]	train-auc:0.997906	valid-auc:0.772489
[1558]	train-auc:0.997907	valid-auc:0.772496
[1559]	train-auc:0.997909	valid-auc:0.772508
[1560]	train-auc:0.997911	valid-auc:0.772498
[1561]	train-auc:0.997916	valid-auc:0.772488
[1562]	train-auc:0.997919	valid-auc:0.772492
[1563]	train-auc:0.997922	valid-auc:0.772494
[1564]	train-auc:0.997927	valid-auc:0.772514
[1565]	train-auc:0.997928	valid-auc:0.772505
[1566]	train-auc:0.997937	valid-auc:0.77246
[1567]	train-auc:0.99794	valid-auc:0.772484
[1568]	train-auc:0.997942	valid-auc:0.772474
[1569]	train-auc:0.997943	valid-auc:0.772515
[1570]	train-auc:0.997952	valid-auc:0.772508
[1571]	train-auc:0.997958	valid-auc:0.772497
[1572]	train-auc:0.997964	valid-auc:0.772484
[1573]	train-auc:0.997968	valid-auc:0.772478
[1574]	train-auc:0.997968	valid-auc:0.772467
[1575]	train-auc:0.997974	valid-auc:0.772478
[1576]	train-auc:0.997976	valid-auc:0.772478
[1577]	train-auc:0.997982	valid-auc:0.77247
[1578]	train-auc:0.997982	valid-auc:0.772444
[1579]	train-auc:0.997984	valid-auc:0.772475
[1580]	train-auc:0.997985	valid-auc:0.772445
[1581]	train-auc:0.997987	valid-auc:0.772442
[1582]	train-auc:0.997988	valid-auc:0.77244
[1583]	train-auc:0.997992	valid-auc:0.772444
[1584]	train-auc:0.998	valid-auc:0.772431
[1585]	train-auc:0.998006	valid-auc:0.77246
[1586]	train-auc:0.998006	valid-auc:0.772493
[1587]	train-auc:0.99801	valid-auc:0.772465
[1588]	train-auc:0.998011	valid-auc:0.772448
[1589]	train-auc:0.998018	valid-auc:0.772462
[1590]	train-auc:0.998018	valid-auc:0.772473
[1591]	train-auc:0.998024	valid-auc:0.772525
[1592]	train-auc:0.998027	valid-auc:0.77252
[1593]	train-auc:0.998028	valid-auc:0.772551
[1594]	train-auc:0.998034	valid-auc:0.772544
[1595]	train-auc:0.998036	valid-auc:0.772529
[1596]	train-auc:0.998036	valid-auc:0.772526
[1597]	train-auc:0.998041	valid-auc:0.772554
[1598]	train-auc:0.99804	valid-auc:0.77256
[1599]	train-auc:0.998041	valid-auc:0.772551
[1600]	train-auc:0.998046	valid-auc:0.772584
[1601]	train-auc:0.998049	valid-auc:0.772592
[1602]	train-auc:0.998052	valid-auc:0.772584
[1603]	train-auc:0.998062	valid-auc:0.772573
[1604]	train-auc:0.998066	valid-auc:0.77255
[1605]	train-auc:0.998069	valid-auc:0.772562
[1606]	train-auc:0.998072	valid-auc:0.772584
[1607]	train-auc:0.998074	valid-auc:0.77258
[1608]	train-auc:0.998078	valid-auc:0.772584
[1609]	train-auc:0.998082	valid-auc:0.772594
[1610]	train-auc:0.998083	valid-auc:0.772603
[1611]	train-auc:0.998087	valid-auc:0.772587
[1612]	train-auc:0.998088	valid-auc:0.772589
[1613]	train-auc:0.998092	valid-auc:0.77261
[1614]	train-auc:0.998092	valid-auc:0.772594
[1615]	train-auc:0.998094	valid-auc:0.772558
[1616]	train-auc:0.998095	valid-auc:0.772566
[1617]	train-auc:0.998098	valid-auc:0.772522
[1618]	train-auc:0.998101	valid-auc:0.772502
[1619]	train-auc:0.998103	valid-auc:0.772478
[1620]	train-auc:0.998104	valid-auc:0.77248
[1621]	train-auc:0.998105	valid-auc:0.772452
[1622]	train-auc:0.998107	valid-auc:0.772432
[1623]	train-auc:0.99811	valid-auc:0.772445
[1624]	train-auc:0.998113	valid-auc:0.772496
[1625]	train-auc:0.998115	valid-auc:0.772506
[1626]	train-auc:0.998116	valid-auc:0.772516
[1627]	train-auc:0.99812	valid-auc:0.772545
[1628]	train-auc:0.998121	valid-auc:0.772539
[1629]	train-auc:0.998122	valid-auc:0.772548
[1630]	train-auc:0.998127	valid-auc:0.772582
[1631]	train-auc:0.998128	valid-auc:0.772604
[1632]	train-auc:0.998131	valid-auc:0.772556
[1633]	train-auc:0.998133	valid-auc:0.772552
[1634]	train-auc:0.998134	valid-auc:0.772581
[1635]	train-auc:0.998137	valid-auc:0.772608
[1636]	train-auc:0.99814	valid-auc:0.77262
[1637]	train-auc:0.998147	valid-auc:0.772603
[1638]	train-auc:0.998149	valid-auc:0.772591
[1639]	train-auc:0.998149	valid-auc:0.772587
[1640]	train-auc:0.998152	valid-auc:0.772569
[1641]	train-auc:0.998154	valid-auc:0.772586
[1642]	train-auc:0.998155	valid-auc:0.772589
[1643]	train-auc:0.99816	valid-auc:0.772612
[1644]	train-auc:0.998164	valid-auc:0.772606
[1645]	train-auc:0.998165	valid-auc:0.772627
[1646]	train-auc:0.998169	valid-auc:0.772634
[1647]	train-auc:0.998174	valid-auc:0.772632
[1648]	train-auc:0.998176	valid-auc:0.772635
[1649]	train-auc:0.998178	valid-auc:0.772655
[1650]	train-auc:0.998184	valid-auc:0.772643
[1651]	train-auc:0.998185	valid-auc:0.772664
[1652]	train-auc:0.998185	valid-auc:0.772657
[1653]	train-auc:0.998186	valid-auc:0.772667
[1654]	train-auc:0.99819	valid-auc:0.772676
[1655]	train-auc:0.998192	valid-auc:0.772681
[1656]	train-auc:0.998197	valid-auc:0.772681
[1657]	train-auc:0.998198	valid-auc:0.77268
[1658]	train-auc:0.998201	valid-auc:0.772664
[1659]	train-auc:0.998206	valid-auc:0.772672
[1660]	train-auc:0.998207	valid-auc:0.772672
[1661]	train-auc:0.998212	valid-auc:0.772667
[1662]	train-auc:0.998215	valid-auc:0.772659
[1663]	train-auc:0.998217	valid-auc:0.772651
[1664]	train-auc:0.998219	valid-auc:0.77264
[1665]	train-auc:0.998222	valid-auc:0.772652
[1666]	train-auc:0.998221	valid-auc:0.772656
[1667]	train-auc:0.998222	valid-auc:0.772662
[1668]	train-auc:0.998226	valid-auc:0.772649
[1669]	train-auc:0.998229	valid-auc:0.772633
[1670]	train-auc:0.99823	valid-auc:0.772598
[1671]	train-auc:0.998232	valid-auc:0.772592
[1672]	train-auc:0.998233	valid-auc:0.772605
[1673]	train-auc:0.998237	valid-auc:0.772607
[1674]	train-auc:0.99824	valid-auc:0.772612
[1675]	train-auc:0.998241	valid-auc:0.772621
[1676]	train-auc:0.998242	valid-auc:0.772592
[1677]	train-auc:0.998244	valid-auc:0.772574
[1678]	train-auc:0.998246	valid-auc:0.772595
[1679]	train-auc:0.99825	valid-auc:0.772596
[1680]	train-auc:0.998253	valid-auc:0.772595
[1681]	train-auc:0.998259	valid-auc:0.772595
[1682]	train-auc:0.998261	valid-auc:0.77258
[1683]	train-auc:0.998263	valid-auc:0.772588
[1684]	train-auc:0.998264	valid-auc:0.772584
[1685]	train-auc:0.998265	valid-auc:0.772582
[1686]	train-auc:0.998268	valid-auc:0.7726
[1687]	train-auc:0.99827	valid-auc:0.772628
[1688]	train-auc:0.998273	valid-auc:0.772621
[1689]	train-auc:0.998277	valid-auc:0.772625
[1690]	train-auc:0.998278	valid-auc:0.772632
[1691]	train-auc:0.99828	valid-auc:0.772641
[1692]	train-auc:0.998282	valid-auc:0.772672
[1693]	train-auc:0.998286	valid-auc:0.772686
[1694]	train-auc:0.998289	valid-auc:0.772673
[1695]	train-auc:0.99829	valid-auc:0.772682
[1696]	train-auc:0.998292	valid-auc:0.77269
[1697]	train-auc:0.998295	valid-auc:0.772695
[1698]	train-auc:0.998299	valid-auc:0.772711
[1699]	train-auc:0.9983	valid-auc:0.772728
[1700]	train-auc:0.998299	valid-auc:0.772725
[1701]	train-auc:0.9983	valid-auc:0.772728
[1702]	train-auc:0.998304	valid-auc:0.772775
[1703]	train-auc:0.998305	valid-auc:0.77277
[1704]	train-auc:0.998306	valid-auc:0.772756
[1705]	train-auc:0.998307	valid-auc:0.77271
[1706]	train-auc:0.998307	valid-auc:0.772702
[1707]	train-auc:0.998309	valid-auc:0.772731
[1708]	train-auc:0.998311	valid-auc:0.772713
[1709]	train-auc:0.998314	valid-auc:0.772697
[1710]	train-auc:0.998315	valid-auc:0.772644
[1711]	train-auc:0.998317	valid-auc:0.772627
[1712]	train-auc:0.99832	valid-auc:0.772647
[1713]	train-auc:0.998321	valid-auc:0.772684
[1714]	train-auc:0.998323	valid-auc:0.772701
[1715]	train-auc:0.998325	valid-auc:0.772713
[1716]	train-auc:0.998327	valid-auc:0.772701
[1717]	train-auc:0.99833	valid-auc:0.772717
[1718]	train-auc:0.998332	valid-auc:0.772699
[1719]	train-auc:0.998333	valid-auc:0.772723
[1720]	train-auc:0.998333	valid-auc:0.77272
[1721]	train-auc:0.998337	valid-auc:0.77271
[1722]	train-auc:0.998339	valid-auc:0.772721
[1723]	train-auc:0.998339	valid-auc:0.772715
[1724]	train-auc:0.998345	valid-auc:0.772714
[1725]	train-auc:0.998347	valid-auc:0.772732
[1726]	train-auc:0.998351	valid-auc:0.772722
[1727]	train-auc:0.998352	valid-auc:0.772729
[1728]	train-auc:0.998357	valid-auc:0.772748
[1729]	train-auc:0.998363	valid-auc:0.772702
[1730]	train-auc:0.998368	valid-auc:0.772681
[1731]	train-auc:0.99837	valid-auc:0.772664
[1732]	train-auc:0.99837	valid-auc:0.772694
[1733]	train-auc:0.998371	valid-auc:0.772668
[1734]	train-auc:0.998373	valid-auc:0.772674
[1735]	train-auc:0.998376	valid-auc:0.77265
[1736]	train-auc:0.998381	valid-auc:0.772631
[1737]	train-auc:0.998383	valid-auc:0.772647
[1738]	train-auc:0.998388	valid-auc:0.772656
[1739]	train-auc:0.998393	valid-auc:0.772635
[1740]	train-auc:0.998396	valid-auc:0.772679
[1741]	train-auc:0.998397	valid-auc:0.77269
[1742]	train-auc:0.998398	valid-auc:0.772681
[1743]	train-auc:0.998402	valid-auc:0.772644
[1744]	train-auc:0.998404	valid-auc:0.772635
[1745]	train-auc:0.998407	valid-auc:0.77263
[1746]	train-auc:0.998407	valid-auc:0.772606
[1747]	train-auc:0.99841	valid-auc:0.772627
[1748]	train-auc:0.998408	valid-auc:0.772613
[1749]	train-auc:0.998409	valid-auc:0.772632
[1750]	train-auc:0.99841	valid-auc:0.772647
[1751]	train-auc:0.99841	valid-auc:0.772635
[1752]	train-auc:0.998414	valid-auc:0.772603
Stopping. Best iteration:
[1702]	train-auc:0.998304	valid-auc:0.772775







    




C:\ProgramData\Anaconda3\lib\site-packages\mlcrate\backend.py:7: UserWarning: Timer.format_elapsed() has been deprecated in favour of Timer.fsince() and will be removed soon
  warn(message)






    




[mlcrate] Finished training fold 0 - took 3m10s - running score 0.772775
[mlcrate] Running fold 1, 62697 train samples, 10450 validation samples
[0]	train-auc:0.744933	valid-auc:0.67904
Multiple eval metrics have been passed: 'valid-auc' will be used for early stopping.

Will train until valid-auc hasn't improved in 50 rounds.
[1]	train-auc:0.766391	valid-auc:0.699385
[2]	train-auc:0.779451	valid-auc:0.711188
[3]	train-auc:0.786683	valid-auc:0.715513
[4]	train-auc:0.790587	valid-auc:0.71753
[5]	train-auc:0.79464	valid-auc:0.717742
[6]	train-auc:0.796438	valid-auc:0.71918
[7]	train-auc:0.799781	valid-auc:0.721123
[8]	train-auc:0.801824	valid-auc:0.722705
[9]	train-auc:0.8035	valid-auc:0.723673
[10]	train-auc:0.80534	valid-auc:0.724567
[11]	train-auc:0.808482	valid-auc:0.725905
[12]	train-auc:0.812039	valid-auc:0.727542
[13]	train-auc:0.813407	valid-auc:0.727025
[14]	train-auc:0.814963	valid-auc:0.727013
[15]	train-auc:0.816887	valid-auc:0.727607
[16]	train-auc:0.81856	valid-auc:0.728137
[17]	train-auc:0.819067	valid-auc:0.72855
[18]	train-auc:0.821979	valid-auc:0.729146
[19]	train-auc:0.82353	valid-auc:0.729494
[20]	train-auc:0.825082	valid-auc:0.730174
[21]	train-auc:0.826749	valid-auc:0.730157
[22]	train-auc:0.828687	valid-auc:0.730535
[23]	train-auc:0.830409	valid-auc:0.731004
[24]	train-auc:0.831378	valid-auc:0.731493
[25]	train-auc:0.832957	valid-auc:0.732218
[26]	train-auc:0.833961	valid-auc:0.732236
[27]	train-auc:0.835319	valid-auc:0.732534
[28]	train-auc:0.836731	valid-auc:0.733108
[29]	train-auc:0.838272	valid-auc:0.733391
[30]	train-auc:0.839708	valid-auc:0.734019
[31]	train-auc:0.840835	valid-auc:0.734027
[32]	train-auc:0.841867	valid-auc:0.734594
[33]	train-auc:0.843679	valid-auc:0.735267
[34]	train-auc:0.844864	valid-auc:0.735677
[35]	train-auc:0.84621	valid-auc:0.73593
[36]	train-auc:0.84747	valid-auc:0.736474
[37]	train-auc:0.848366	valid-auc:0.736706
[38]	train-auc:0.84966	valid-auc:0.736877
[39]	train-auc:0.850682	valid-auc:0.737045
[40]	train-auc:0.851535	valid-auc:0.737289
[41]	train-auc:0.853201	valid-auc:0.738093
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[1039]	train-auc:0.995109	valid-auc:0.784108
[1040]	train-auc:0.995111	valid-auc:0.784123
[1041]	train-auc:0.995121	valid-auc:0.784141
[1042]	train-auc:0.995126	valid-auc:0.784168
[1043]	train-auc:0.995146	valid-auc:0.784216
[1044]	train-auc:0.995163	valid-auc:0.784215
[1045]	train-auc:0.995168	valid-auc:0.784278
[1046]	train-auc:0.99518	valid-auc:0.784292
[1047]	train-auc:0.995191	valid-auc:0.784315
[1048]	train-auc:0.995202	valid-auc:0.78427
[1049]	train-auc:0.99521	valid-auc:0.784261
[1050]	train-auc:0.995217	valid-auc:0.784253
[1051]	train-auc:0.995222	valid-auc:0.784215
[1052]	train-auc:0.995225	valid-auc:0.784242
[1053]	train-auc:0.995239	valid-auc:0.784252
[1054]	train-auc:0.995241	valid-auc:0.784255
[1055]	train-auc:0.995255	valid-auc:0.784311
[1056]	train-auc:0.995262	valid-auc:0.784341
[1057]	train-auc:0.995277	valid-auc:0.784333
[1058]	train-auc:0.995289	valid-auc:0.784347
[1059]	train-auc:0.995301	valid-auc:0.784327
[1060]	train-auc:0.995315	valid-auc:0.784368
[1061]	train-auc:0.995328	valid-auc:0.784413
[1062]	train-auc:0.995339	valid-auc:0.784447
[1063]	train-auc:0.995344	valid-auc:0.784422
[1064]	train-auc:0.995348	valid-auc:0.784405
[1065]	train-auc:0.995352	valid-auc:0.784402
[1066]	train-auc:0.995358	valid-auc:0.784447
[1067]	train-auc:0.99536	valid-auc:0.784466
[1068]	train-auc:0.995365	valid-auc:0.784468
[1069]	train-auc:0.995375	valid-auc:0.784525
[1070]	train-auc:0.995384	valid-auc:0.784525
[1071]	train-auc:0.995393	valid-auc:0.784562
[1072]	train-auc:0.9954	valid-auc:0.784556
[1073]	train-auc:0.995401	valid-auc:0.784545
[1074]	train-auc:0.995408	valid-auc:0.78461
[1075]	train-auc:0.995426	valid-auc:0.784585
[1076]	train-auc:0.995433	valid-auc:0.784589
[1077]	train-auc:0.995444	valid-auc:0.784622
[1078]	train-auc:0.995455	valid-auc:0.784632
[1079]	train-auc:0.995457	valid-auc:0.784659
[1080]	train-auc:0.995466	valid-auc:0.784682
[1081]	train-auc:0.995477	valid-auc:0.784679
[1082]	train-auc:0.995483	valid-auc:0.784647
[1083]	train-auc:0.995495	valid-auc:0.784671
[1084]	train-auc:0.995496	valid-auc:0.784673
[1085]	train-auc:0.995509	valid-auc:0.784696
[1086]	train-auc:0.995521	valid-auc:0.78471
[1087]	train-auc:0.995528	valid-auc:0.784718
[1088]	train-auc:0.995531	valid-auc:0.784733
[1089]	train-auc:0.995537	valid-auc:0.784773
[1090]	train-auc:0.995552	valid-auc:0.784833
[1091]	train-auc:0.99556	valid-auc:0.784815
[1092]	train-auc:0.99557	valid-auc:0.784827
[1093]	train-auc:0.995586	valid-auc:0.784794
[1094]	train-auc:0.995608	valid-auc:0.784851
[1095]	train-auc:0.995618	valid-auc:0.78486
[1096]	train-auc:0.995627	valid-auc:0.784931
[1097]	train-auc:0.995635	valid-auc:0.784974
[1098]	train-auc:0.995652	valid-auc:0.784986
[1099]	train-auc:0.995664	valid-auc:0.785
[1100]	train-auc:0.995666	valid-auc:0.785
[1101]	train-auc:0.99567	valid-auc:0.784957
[1102]	train-auc:0.995682	valid-auc:0.784912
[1103]	train-auc:0.995685	valid-auc:0.784901
[1104]	train-auc:0.995688	valid-auc:0.784894
[1105]	train-auc:0.995702	valid-auc:0.784946
[1106]	train-auc:0.995706	valid-auc:0.784939
[1107]	train-auc:0.995715	valid-auc:0.784903
[1108]	train-auc:0.995718	valid-auc:0.784917
[1109]	train-auc:0.995729	valid-auc:0.784977
[1110]	train-auc:0.995744	valid-auc:0.784979
[1111]	train-auc:0.995758	valid-auc:0.784979
[1112]	train-auc:0.995762	valid-auc:0.784942
[1113]	train-auc:0.99577	valid-auc:0.784933
[1114]	train-auc:0.995776	valid-auc:0.78495
[1115]	train-auc:0.99579	valid-auc:0.784943
[1116]	train-auc:0.995796	valid-auc:0.784947
[1117]	train-auc:0.995808	valid-auc:0.784971
[1118]	train-auc:0.995826	valid-auc:0.785006
[1119]	train-auc:0.995841	valid-auc:0.784987
[1120]	train-auc:0.995845	valid-auc:0.78498
[1121]	train-auc:0.995849	valid-auc:0.784994
[1122]	train-auc:0.995861	valid-auc:0.785
[1123]	train-auc:0.995869	valid-auc:0.784992
[1124]	train-auc:0.99587	valid-auc:0.784995
[1125]	train-auc:0.995873	valid-auc:0.785001
[1126]	train-auc:0.995878	valid-auc:0.785008
[1127]	train-auc:0.995884	valid-auc:0.784982
[1128]	train-auc:0.995886	valid-auc:0.784938
[1129]	train-auc:0.995893	valid-auc:0.784933
[1130]	train-auc:0.995899	valid-auc:0.784948
[1131]	train-auc:0.995905	valid-auc:0.784942
[1132]	train-auc:0.995916	valid-auc:0.78493
[1133]	train-auc:0.995925	valid-auc:0.784914
[1134]	train-auc:0.995926	valid-auc:0.784909
[1135]	train-auc:0.995933	valid-auc:0.784945
[1136]	train-auc:0.995944	valid-auc:0.784958
[1137]	train-auc:0.995947	valid-auc:0.785007
[1138]	train-auc:0.995963	valid-auc:0.784956
[1139]	train-auc:0.995967	valid-auc:0.784945
[1140]	train-auc:0.995978	valid-auc:0.78496
[1141]	train-auc:0.995986	valid-auc:0.784944
[1142]	train-auc:0.995987	valid-auc:0.784964
[1143]	train-auc:0.996001	valid-auc:0.784974
[1144]	train-auc:0.996009	valid-auc:0.784959
[1145]	train-auc:0.996019	valid-auc:0.784945
[1146]	train-auc:0.996021	valid-auc:0.784951
[1147]	train-auc:0.996039	valid-auc:0.784935
[1148]	train-auc:0.996046	valid-auc:0.78494
[1149]	train-auc:0.996057	valid-auc:0.784952
[1150]	train-auc:0.996068	valid-auc:0.784923
[1151]	train-auc:0.996079	valid-auc:0.784927
[1152]	train-auc:0.996079	valid-auc:0.784928
[1153]	train-auc:0.996081	valid-auc:0.784921
[1154]	train-auc:0.996085	valid-auc:0.784964
[1155]	train-auc:0.996087	valid-auc:0.78496
[1156]	train-auc:0.996096	valid-auc:0.784977
[1157]	train-auc:0.9961	valid-auc:0.784985
[1158]	train-auc:0.996102	valid-auc:0.785
[1159]	train-auc:0.996105	valid-auc:0.784982
[1160]	train-auc:0.996108	valid-auc:0.78494
[1161]	train-auc:0.996113	valid-auc:0.785002
[1162]	train-auc:0.996127	valid-auc:0.784989
[1163]	train-auc:0.996135	valid-auc:0.785011
[1164]	train-auc:0.996139	valid-auc:0.785033
[1165]	train-auc:0.996138	valid-auc:0.785038
[1166]	train-auc:0.996147	valid-auc:0.785084
[1167]	train-auc:0.996167	valid-auc:0.785058
[1168]	train-auc:0.996174	valid-auc:0.785067
[1169]	train-auc:0.996179	valid-auc:0.785005
[1170]	train-auc:0.99618	valid-auc:0.784998
[1171]	train-auc:0.99619	valid-auc:0.78497
[1172]	train-auc:0.996202	valid-auc:0.785014
[1173]	train-auc:0.996213	valid-auc:0.785011
[1174]	train-auc:0.996216	valid-auc:0.784997
[1175]	train-auc:0.996226	valid-auc:0.784985
[1176]	train-auc:0.996234	valid-auc:0.785013
[1177]	train-auc:0.996243	valid-auc:0.785002
[1178]	train-auc:0.996241	valid-auc:0.785027
[1179]	train-auc:0.996248	valid-auc:0.78503
[1180]	train-auc:0.996252	valid-auc:0.785015
[1181]	train-auc:0.996256	valid-auc:0.78501
[1182]	train-auc:0.996265	valid-auc:0.785072
[1183]	train-auc:0.996271	valid-auc:0.785076
[1184]	train-auc:0.996273	valid-auc:0.78506
[1185]	train-auc:0.996284	valid-auc:0.785044
[1186]	train-auc:0.996288	valid-auc:0.785036
[1187]	train-auc:0.996296	valid-auc:0.785043
[1188]	train-auc:0.996304	valid-auc:0.785097
[1189]	train-auc:0.996312	valid-auc:0.785097
[1190]	train-auc:0.996317	valid-auc:0.785123
[1191]	train-auc:0.996323	valid-auc:0.785138
[1192]	train-auc:0.996325	valid-auc:0.785179
[1193]	train-auc:0.996329	valid-auc:0.785181
[1194]	train-auc:0.996329	valid-auc:0.785212
[1195]	train-auc:0.996331	valid-auc:0.78521
[1196]	train-auc:0.996346	valid-auc:0.785166
[1197]	train-auc:0.996352	valid-auc:0.785134
[1198]	train-auc:0.996366	valid-auc:0.785178
[1199]	train-auc:0.996371	valid-auc:0.785179
[1200]	train-auc:0.996382	valid-auc:0.785099
[1201]	train-auc:0.996395	valid-auc:0.785083
[1202]	train-auc:0.996398	valid-auc:0.78514
[1203]	train-auc:0.996409	valid-auc:0.785163
[1204]	train-auc:0.996418	valid-auc:0.785118
[1205]	train-auc:0.996431	valid-auc:0.785109
[1206]	train-auc:0.996437	valid-auc:0.785082
[1207]	train-auc:0.996444	valid-auc:0.785052
[1208]	train-auc:0.996459	valid-auc:0.78504
[1209]	train-auc:0.996465	valid-auc:0.78504
[1210]	train-auc:0.996474	valid-auc:0.785008
[1211]	train-auc:0.99648	valid-auc:0.785047
[1212]	train-auc:0.996485	valid-auc:0.785069
[1213]	train-auc:0.99649	valid-auc:0.785059
[1214]	train-auc:0.996499	valid-auc:0.785047
[1215]	train-auc:0.996502	valid-auc:0.785052
[1216]	train-auc:0.996508	valid-auc:0.785096
[1217]	train-auc:0.996512	valid-auc:0.785094
[1218]	train-auc:0.996526	valid-auc:0.785104
[1219]	train-auc:0.996529	valid-auc:0.785143
[1220]	train-auc:0.996537	valid-auc:0.785169
[1221]	train-auc:0.996548	valid-auc:0.785152
[1222]	train-auc:0.996556	valid-auc:0.785166
[1223]	train-auc:0.996567	valid-auc:0.785136
[1224]	train-auc:0.996576	valid-auc:0.785118
[1225]	train-auc:0.996581	valid-auc:0.785114
[1226]	train-auc:0.996604	valid-auc:0.785139
[1227]	train-auc:0.996608	valid-auc:0.785126
[1228]	train-auc:0.99661	valid-auc:0.785105
[1229]	train-auc:0.996613	valid-auc:0.785155
[1230]	train-auc:0.996621	valid-auc:0.785135
[1231]	train-auc:0.996631	valid-auc:0.785097
[1232]	train-auc:0.996634	valid-auc:0.785121
[1233]	train-auc:0.996641	valid-auc:0.785157
[1234]	train-auc:0.996643	valid-auc:0.785145
[1235]	train-auc:0.996649	valid-auc:0.785159
[1236]	train-auc:0.996659	valid-auc:0.785132
[1237]	train-auc:0.996669	valid-auc:0.785107
[1238]	train-auc:0.996684	valid-auc:0.785114
[1239]	train-auc:0.996689	valid-auc:0.78514
[1240]	train-auc:0.996696	valid-auc:0.785173
[1241]	train-auc:0.996707	valid-auc:0.78516
[1242]	train-auc:0.996714	valid-auc:0.785151
[1243]	train-auc:0.996722	valid-auc:0.785166
[1244]	train-auc:0.996735	valid-auc:0.785144
Stopping. Best iteration:
[1194]	train-auc:0.996329	valid-auc:0.785212

[mlcrate] Finished training fold 1 - took 2m18s - running score 0.7789935
[mlcrate] Running fold 2, 62697 train samples, 10450 validation samples
[0]	train-auc:0.747058	valid-auc:0.673365
Multiple eval metrics have been passed: 'valid-auc' will be used for early stopping.

Will train until valid-auc hasn't improved in 50 rounds.
[1]	train-auc:0.769251	valid-auc:0.688796
[2]	train-auc:0.779839	valid-auc:0.695148
[3]	train-auc:0.787004	valid-auc:0.699104
[4]	train-auc:0.789632	valid-auc:0.699089
[5]	train-auc:0.792319	valid-auc:0.700706
[6]	train-auc:0.795142	valid-auc:0.70373
[7]	train-auc:0.798357	valid-auc:0.705066
[8]	train-auc:0.801109	valid-auc:0.705731
[9]	train-auc:0.803068	valid-auc:0.705377
[10]	train-auc:0.805914	valid-auc:0.706494
[11]	train-auc:0.807472	valid-auc:0.707169
[12]	train-auc:0.810557	valid-auc:0.707821
[13]	train-auc:0.812229	valid-auc:0.708438
[14]	train-auc:0.813169	valid-auc:0.709021
[15]	train-auc:0.815658	valid-auc:0.709031
[16]	train-auc:0.817065	valid-auc:0.709766
[17]	train-auc:0.818068	valid-auc:0.710142
[18]	train-auc:0.821225	valid-auc:0.710311
[19]	train-auc:0.822999	valid-auc:0.710709
[20]	train-auc:0.825394	valid-auc:0.711716
[21]	train-auc:0.827035	valid-auc:0.712419
[22]	train-auc:0.828171	valid-auc:0.71278
[23]	train-auc:0.829794	valid-auc:0.713147
[24]	train-auc:0.830806	valid-auc:0.713211
[25]	train-auc:0.831994	valid-auc:0.713266
[26]	train-auc:0.833829	valid-auc:0.714
[27]	train-auc:0.835674	valid-auc:0.714607
[28]	train-auc:0.836672	valid-auc:0.71485
[29]	train-auc:0.838136	valid-auc:0.715509
[30]	train-auc:0.83964	valid-auc:0.716429
[31]	train-auc:0.841583	valid-auc:0.716952
[32]	train-auc:0.842595	valid-auc:0.717209
[33]	train-auc:0.844912	valid-auc:0.717892
[34]	train-auc:0.845606	valid-auc:0.71794
[35]	train-auc:0.846893	valid-auc:0.718531
[36]	train-auc:0.848564	valid-auc:0.719668
[37]	train-auc:0.84992	valid-auc:0.719923
[38]	train-auc:0.85128	valid-auc:0.720169
[39]	train-auc:0.852451	valid-auc:0.720587
[40]	train-auc:0.853334	valid-auc:0.721088
[41]	train-auc:0.854593	valid-auc:0.721705
[42]	train-auc:0.855504	valid-auc:0.722305
[43]	train-auc:0.856295	valid-auc:0.722308
[44]	train-auc:0.857821	valid-auc:0.72291
[45]	train-auc:0.859091	valid-auc:0.723538
[46]	train-auc:0.86017	valid-auc:0.724162
[47]	train-auc:0.861305	valid-auc:0.724546
[48]	train-auc:0.862222	valid-auc:0.724773
[49]	train-auc:0.863129	valid-auc:0.724915
[50]	train-auc:0.864119	valid-auc:0.725467
[51]	train-auc:0.865363	valid-auc:0.725989
[52]	train-auc:0.865749	valid-auc:0.726085
[53]	train-auc:0.866917	valid-auc:0.726629
[54]	train-auc:0.867694	valid-auc:0.726924
[55]	train-auc:0.868695	valid-auc:0.727108
[56]	train-auc:0.869443	valid-auc:0.727583
[57]	train-auc:0.870916	valid-auc:0.728306
[58]	train-auc:0.871874	valid-auc:0.728535
[59]	train-auc:0.872508	valid-auc:0.728611
[60]	train-auc:0.873307	valid-auc:0.728978
[61]	train-auc:0.874016	valid-auc:0.728954
[62]	train-auc:0.874614	valid-auc:0.729324
[63]	train-auc:0.874967	valid-auc:0.729405
[64]	train-auc:0.875508	valid-auc:0.729675
[65]	train-auc:0.876394	valid-auc:0.72999
[66]	train-auc:0.877323	valid-auc:0.73045
[67]	train-auc:0.878498	valid-auc:0.730783
[68]	train-auc:0.87891	valid-auc:0.731006
[69]	train-auc:0.879329	valid-auc:0.731119
[70]	train-auc:0.880075	valid-auc:0.731216
[71]	train-auc:0.880672	valid-auc:0.731364
[72]	train-auc:0.881183	valid-auc:0.731315
[73]	train-auc:0.88152	valid-auc:0.731474
[74]	train-auc:0.882362	valid-auc:0.731842
[75]	train-auc:0.882734	valid-auc:0.731965
[76]	train-auc:0.883166	valid-auc:0.732139
[77]	train-auc:0.883846	valid-auc:0.732275
[78]	train-auc:0.884535	valid-auc:0.732411
[79]	train-auc:0.885728	valid-auc:0.732638
[80]	train-auc:0.886053	valid-auc:0.732728
[81]	train-auc:0.886445	valid-auc:0.732729
[82]	train-auc:0.886717	valid-auc:0.732755
[83]	train-auc:0.887248	valid-auc:0.732853
[84]	train-auc:0.888072	valid-auc:0.73296
[85]	train-auc:0.888544	valid-auc:0.733165
[86]	train-auc:0.889465	valid-auc:0.733421
[87]	train-auc:0.889829	valid-auc:0.733663
[88]	train-auc:0.890358	valid-auc:0.733968
[89]	train-auc:0.890812	valid-auc:0.734228
[90]	train-auc:0.890874	valid-auc:0.734214
[91]	train-auc:0.891453	valid-auc:0.734404
[92]	train-auc:0.891794	valid-auc:0.73442
[93]	train-auc:0.892119	valid-auc:0.734559
[94]	train-auc:0.892703	valid-auc:0.734774
[95]	train-auc:0.893169	valid-auc:0.734971
[96]	train-auc:0.893652	valid-auc:0.735225
[97]	train-auc:0.893874	valid-auc:0.73527
[98]	train-auc:0.894573	valid-auc:0.735392
[99]	train-auc:0.894946	valid-auc:0.735505
[100]	train-auc:0.895483	valid-auc:0.735685
[101]	train-auc:0.895766	valid-auc:0.735803
[102]	train-auc:0.895929	valid-auc:0.735758
[103]	train-auc:0.896312	valid-auc:0.736041
[104]	train-auc:0.896617	valid-auc:0.736219
[105]	train-auc:0.897225	valid-auc:0.73653
[106]	train-auc:0.897689	valid-auc:0.736825
[107]	train-auc:0.898823	valid-auc:0.737457
[108]	train-auc:0.899353	valid-auc:0.737627
[109]	train-auc:0.899754	valid-auc:0.737874
[110]	train-auc:0.900779	valid-auc:0.73833
[111]	train-auc:0.901767	valid-auc:0.738648
[112]	train-auc:0.902539	valid-auc:0.738941
[113]	train-auc:0.903309	valid-auc:0.739416
[114]	train-auc:0.903518	valid-auc:0.739505
[115]	train-auc:0.90421	valid-auc:0.739771
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[784]	train-auc:0.990393	valid-auc:0.770571
[785]	train-auc:0.99044	valid-auc:0.770574
[786]	train-auc:0.990441	valid-auc:0.770588
[787]	train-auc:0.990469	valid-auc:0.770615
[788]	train-auc:0.990478	valid-auc:0.770565
[789]	train-auc:0.99051	valid-auc:0.770499
[790]	train-auc:0.990524	valid-auc:0.770528
[791]	train-auc:0.990544	valid-auc:0.77052
[792]	train-auc:0.990575	valid-auc:0.770548
[793]	train-auc:0.99058	valid-auc:0.770545
[794]	train-auc:0.990627	valid-auc:0.770628
[795]	train-auc:0.99066	valid-auc:0.770748
[796]	train-auc:0.990693	valid-auc:0.770744
[797]	train-auc:0.990699	valid-auc:0.770732
[798]	train-auc:0.990715	valid-auc:0.770707
[799]	train-auc:0.990721	valid-auc:0.770715
[800]	train-auc:0.990743	valid-auc:0.770744
[801]	train-auc:0.990766	valid-auc:0.770849
[802]	train-auc:0.990809	valid-auc:0.770918
[803]	train-auc:0.990848	valid-auc:0.770986
[804]	train-auc:0.990893	valid-auc:0.771003
[805]	train-auc:0.990932	valid-auc:0.771027
[806]	train-auc:0.990968	valid-auc:0.771005
[807]	train-auc:0.990991	valid-auc:0.771063
[808]	train-auc:0.991024	valid-auc:0.771123
[809]	train-auc:0.991041	valid-auc:0.77111
[810]	train-auc:0.991062	valid-auc:0.771175
[811]	train-auc:0.9911	valid-auc:0.771169
[812]	train-auc:0.991144	valid-auc:0.771151
[813]	train-auc:0.991162	valid-auc:0.771125
[814]	train-auc:0.991193	valid-auc:0.771163
[815]	train-auc:0.991221	valid-auc:0.771175
[816]	train-auc:0.991222	valid-auc:0.771221
[817]	train-auc:0.991241	valid-auc:0.77122
[818]	train-auc:0.991247	valid-auc:0.771215
[819]	train-auc:0.991283	valid-auc:0.771273
[820]	train-auc:0.991303	valid-auc:0.771259
[821]	train-auc:0.991311	valid-auc:0.771312
[822]	train-auc:0.991352	valid-auc:0.771371
[823]	train-auc:0.991393	valid-auc:0.771355
[824]	train-auc:0.991406	valid-auc:0.771409
[825]	train-auc:0.991424	valid-auc:0.771431
[826]	train-auc:0.991455	valid-auc:0.771373
[827]	train-auc:0.99147	valid-auc:0.771342
[828]	train-auc:0.991483	valid-auc:0.771335
[829]	train-auc:0.991505	valid-auc:0.771309
[830]	train-auc:0.991532	valid-auc:0.77137
[831]	train-auc:0.991549	valid-auc:0.771455
[832]	train-auc:0.99156	valid-auc:0.771416
[833]	train-auc:0.991583	valid-auc:0.77143
[834]	train-auc:0.991606	valid-auc:0.771461
[835]	train-auc:0.991614	valid-auc:0.771458
[836]	train-auc:0.991626	valid-auc:0.771463
[837]	train-auc:0.991642	valid-auc:0.771557
[838]	train-auc:0.991674	valid-auc:0.771618
[839]	train-auc:0.991681	valid-auc:0.771599
[840]	train-auc:0.991712	valid-auc:0.771637
[841]	train-auc:0.991728	valid-auc:0.771652
[842]	train-auc:0.991731	valid-auc:0.771649
[843]	train-auc:0.991754	valid-auc:0.771651
[844]	train-auc:0.991765	valid-auc:0.771668
[845]	train-auc:0.991791	valid-auc:0.771718
[846]	train-auc:0.991808	valid-auc:0.771743
[847]	train-auc:0.991845	valid-auc:0.771713
[848]	train-auc:0.991862	valid-auc:0.771771
[849]	train-auc:0.991881	valid-auc:0.77174
[850]	train-auc:0.991894	valid-auc:0.771771
[851]	train-auc:0.991909	valid-auc:0.771814
[852]	train-auc:0.991916	valid-auc:0.771769
[853]	train-auc:0.991946	valid-auc:0.771831
[854]	train-auc:0.991963	valid-auc:0.771829
[855]	train-auc:0.991976	valid-auc:0.771792
[856]	train-auc:0.992	valid-auc:0.77179
[857]	train-auc:0.992009	valid-auc:0.7718
[858]	train-auc:0.992022	valid-auc:0.771846
[859]	train-auc:0.992038	valid-auc:0.771854
[860]	train-auc:0.992045	valid-auc:0.771873
[861]	train-auc:0.992063	valid-auc:0.771879
[862]	train-auc:0.992091	valid-auc:0.771828
[863]	train-auc:0.992118	valid-auc:0.771868
[864]	train-auc:0.992142	valid-auc:0.771885
[865]	train-auc:0.992161	valid-auc:0.771921
[866]	train-auc:0.992199	valid-auc:0.772069
[867]	train-auc:0.99222	valid-auc:0.772106
[868]	train-auc:0.992233	valid-auc:0.772077
[869]	train-auc:0.992243	valid-auc:0.772066
[870]	train-auc:0.992265	valid-auc:0.772075
[871]	train-auc:0.992271	valid-auc:0.772086
[872]	train-auc:0.992281	valid-auc:0.772106
[873]	train-auc:0.992298	valid-auc:0.772079
[874]	train-auc:0.99233	valid-auc:0.772096
[875]	train-auc:0.992335	valid-auc:0.77208
[876]	train-auc:0.992336	valid-auc:0.772056
[877]	train-auc:0.992348	valid-auc:0.772074
[878]	train-auc:0.992349	valid-auc:0.772069
[879]	train-auc:0.992374	valid-auc:0.772096
[880]	train-auc:0.992399	valid-auc:0.772065
[881]	train-auc:0.99242	valid-auc:0.772032
[882]	train-auc:0.992435	valid-auc:0.772054
[883]	train-auc:0.992442	valid-auc:0.772071
[884]	train-auc:0.992454	valid-auc:0.772058
[885]	train-auc:0.992468	valid-auc:0.77208
[886]	train-auc:0.992489	valid-auc:0.772122
[887]	train-auc:0.992501	valid-auc:0.772114
[888]	train-auc:0.992516	valid-auc:0.772155
[889]	train-auc:0.99252	valid-auc:0.772134
[890]	train-auc:0.992547	valid-auc:0.772141
[891]	train-auc:0.992563	valid-auc:0.772143
[892]	train-auc:0.992599	valid-auc:0.772085
[893]	train-auc:0.992621	valid-auc:0.77216
[894]	train-auc:0.992642	valid-auc:0.772117
[895]	train-auc:0.992657	valid-auc:0.772132
[896]	train-auc:0.992671	valid-auc:0.772126
[897]	train-auc:0.99268	valid-auc:0.772143
[898]	train-auc:0.992684	valid-auc:0.772137
[899]	train-auc:0.992698	valid-auc:0.772189
[900]	train-auc:0.992714	valid-auc:0.772236
[901]	train-auc:0.992759	valid-auc:0.772259
[902]	train-auc:0.99277	valid-auc:0.772265
[903]	train-auc:0.992773	valid-auc:0.772276
[904]	train-auc:0.992767	valid-auc:0.772286
[905]	train-auc:0.992769	valid-auc:0.772288
[906]	train-auc:0.992782	valid-auc:0.772348
[907]	train-auc:0.99279	valid-auc:0.772279
[908]	train-auc:0.992802	valid-auc:0.7723
[909]	train-auc:0.992815	valid-auc:0.772305
[910]	train-auc:0.992842	valid-auc:0.772333
[911]	train-auc:0.992868	valid-auc:0.772345
[912]	train-auc:0.992887	valid-auc:0.772311
[913]	train-auc:0.9929	valid-auc:0.77233
[914]	train-auc:0.992912	valid-auc:0.772359
[915]	train-auc:0.992922	valid-auc:0.772407
[916]	train-auc:0.992928	valid-auc:0.772441
[917]	train-auc:0.99294	valid-auc:0.772423
[918]	train-auc:0.992945	valid-auc:0.772421
[919]	train-auc:0.992954	valid-auc:0.772468
[920]	train-auc:0.992965	valid-auc:0.772541
[921]	train-auc:0.992979	valid-auc:0.772599
[922]	train-auc:0.992998	valid-auc:0.772597
[923]	train-auc:0.992999	valid-auc:0.772606
[924]	train-auc:0.99301	valid-auc:0.772637
[925]	train-auc:0.993016	valid-auc:0.772624
[926]	train-auc:0.993022	valid-auc:0.77258
[927]	train-auc:0.993037	valid-auc:0.772569
[928]	train-auc:0.99307	valid-auc:0.772634
[929]	train-auc:0.993085	valid-auc:0.772661
[930]	train-auc:0.993116	valid-auc:0.772691
[931]	train-auc:0.993133	valid-auc:0.772719
[932]	train-auc:0.993167	valid-auc:0.77275
[933]	train-auc:0.9932	valid-auc:0.772782
[934]	train-auc:0.993201	valid-auc:0.772793
[935]	train-auc:0.993218	valid-auc:0.772833
[936]	train-auc:0.993216	valid-auc:0.77282
[937]	train-auc:0.99323	valid-auc:0.772854
[938]	train-auc:0.993236	valid-auc:0.772875
[939]	train-auc:0.993264	valid-auc:0.772896
[940]	train-auc:0.993277	valid-auc:0.772882
[941]	train-auc:0.993299	valid-auc:0.772879
[942]	train-auc:0.993331	valid-auc:0.772934
[943]	train-auc:0.993336	valid-auc:0.772915
[944]	train-auc:0.993352	valid-auc:0.772915
[945]	train-auc:0.993358	valid-auc:0.772888
[946]	train-auc:0.993384	valid-auc:0.772943
[947]	train-auc:0.993393	valid-auc:0.772879
[948]	train-auc:0.993435	valid-auc:0.77286
[949]	train-auc:0.993449	valid-auc:0.772912
[950]	train-auc:0.993475	valid-auc:0.772929
[951]	train-auc:0.993493	valid-auc:0.772942
[952]	train-auc:0.993512	valid-auc:0.772918
[953]	train-auc:0.993517	valid-auc:0.772909
[954]	train-auc:0.993534	valid-auc:0.772959
[955]	train-auc:0.993551	valid-auc:0.772955
[956]	train-auc:0.993567	valid-auc:0.773003
[957]	train-auc:0.993585	valid-auc:0.773012
[958]	train-auc:0.993602	valid-auc:0.773029
[959]	train-auc:0.993631	valid-auc:0.773049
[960]	train-auc:0.993636	valid-auc:0.773063
[961]	train-auc:0.99365	valid-auc:0.773085
[962]	train-auc:0.993655	valid-auc:0.773102
[963]	train-auc:0.993665	valid-auc:0.773067
[964]	train-auc:0.993675	valid-auc:0.773068
[965]	train-auc:0.993707	valid-auc:0.773052
[966]	train-auc:0.993717	valid-auc:0.77304
[967]	train-auc:0.993732	valid-auc:0.773022
[968]	train-auc:0.993742	valid-auc:0.772971
[969]	train-auc:0.993768	valid-auc:0.772995
[970]	train-auc:0.993774	valid-auc:0.773005
[971]	train-auc:0.993789	valid-auc:0.773041
[972]	train-auc:0.993821	valid-auc:0.772957
[973]	train-auc:0.99384	valid-auc:0.772989
[974]	train-auc:0.993855	valid-auc:0.772945
[975]	train-auc:0.993862	valid-auc:0.77294
[976]	train-auc:0.993872	valid-auc:0.772967
[977]	train-auc:0.993887	valid-auc:0.772988
[978]	train-auc:0.993926	valid-auc:0.772971
[979]	train-auc:0.993943	valid-auc:0.772936
[980]	train-auc:0.993954	valid-auc:0.772996
[981]	train-auc:0.993956	valid-auc:0.77305
[982]	train-auc:0.99398	valid-auc:0.773079
[983]	train-auc:0.993995	valid-auc:0.773052
[984]	train-auc:0.994007	valid-auc:0.773057
[985]	train-auc:0.994025	valid-auc:0.773053
[986]	train-auc:0.994032	valid-auc:0.773091
[987]	train-auc:0.994041	valid-auc:0.773135
[988]	train-auc:0.994045	valid-auc:0.773117
[989]	train-auc:0.99406	valid-auc:0.773128
[990]	train-auc:0.994075	valid-auc:0.773108
[991]	train-auc:0.99409	valid-auc:0.773141
[992]	train-auc:0.9941	valid-auc:0.773126
[993]	train-auc:0.994114	valid-auc:0.773088
[994]	train-auc:0.994139	valid-auc:0.773099
[995]	train-auc:0.994154	valid-auc:0.773104
[996]	train-auc:0.994157	valid-auc:0.773126
[997]	train-auc:0.994181	valid-auc:0.773081
[998]	train-auc:0.994206	valid-auc:0.773028
[999]	train-auc:0.99421	valid-auc:0.773039
[1000]	train-auc:0.994221	valid-auc:0.773002
[1001]	train-auc:0.994233	valid-auc:0.773017
[1002]	train-auc:0.994251	valid-auc:0.772998
[1003]	train-auc:0.994268	valid-auc:0.773067
[1004]	train-auc:0.994271	valid-auc:0.773099
[1005]	train-auc:0.994273	valid-auc:0.773102
[1006]	train-auc:0.994276	valid-auc:0.773108
[1007]	train-auc:0.994294	valid-auc:0.773105
[1008]	train-auc:0.994323	valid-auc:0.773156
[1009]	train-auc:0.994338	valid-auc:0.773075
[1010]	train-auc:0.994347	valid-auc:0.77311
[1011]	train-auc:0.994353	valid-auc:0.77311
[1012]	train-auc:0.994365	valid-auc:0.773128
[1013]	train-auc:0.994384	valid-auc:0.773126
[1014]	train-auc:0.994413	valid-auc:0.773135
[1015]	train-auc:0.994427	valid-auc:0.773153
[1016]	train-auc:0.994448	valid-auc:0.773074
[1017]	train-auc:0.994477	valid-auc:0.77306
[1018]	train-auc:0.994493	valid-auc:0.773056
[1019]	train-auc:0.994513	valid-auc:0.773101
[1020]	train-auc:0.99452	valid-auc:0.773066
[1021]	train-auc:0.994519	valid-auc:0.773067
[1022]	train-auc:0.994529	valid-auc:0.773032
[1023]	train-auc:0.99454	valid-auc:0.773078
[1024]	train-auc:0.994562	valid-auc:0.773126
[1025]	train-auc:0.994567	valid-auc:0.773152
[1026]	train-auc:0.994586	valid-auc:0.773132
[1027]	train-auc:0.994601	valid-auc:0.773226
[1028]	train-auc:0.99462	valid-auc:0.773208
[1029]	train-auc:0.994625	valid-auc:0.773229
[1030]	train-auc:0.994635	valid-auc:0.77323
[1031]	train-auc:0.994644	valid-auc:0.773229
[1032]	train-auc:0.994667	valid-auc:0.773284
[1033]	train-auc:0.994676	valid-auc:0.773317
[1034]	train-auc:0.994699	valid-auc:0.773426
[1035]	train-auc:0.994712	valid-auc:0.773331
[1036]	train-auc:0.994715	valid-auc:0.773315
[1037]	train-auc:0.994713	valid-auc:0.773339
[1038]	train-auc:0.994727	valid-auc:0.773335
[1039]	train-auc:0.99474	valid-auc:0.773316
[1040]	train-auc:0.994742	valid-auc:0.773272
[1041]	train-auc:0.99475	valid-auc:0.773282
[1042]	train-auc:0.994772	valid-auc:0.773287
[1043]	train-auc:0.994788	valid-auc:0.773294
[1044]	train-auc:0.994802	valid-auc:0.773368
[1045]	train-auc:0.994808	valid-auc:0.773345
[1046]	train-auc:0.994813	valid-auc:0.773352
[1047]	train-auc:0.994819	valid-auc:0.773368
[1048]	train-auc:0.994828	valid-auc:0.773375
[1049]	train-auc:0.994827	valid-auc:0.773381
[1050]	train-auc:0.994844	valid-auc:0.773311
[1051]	train-auc:0.99485	valid-auc:0.773313
[1052]	train-auc:0.994858	valid-auc:0.773341
[1053]	train-auc:0.994878	valid-auc:0.773286
[1054]	train-auc:0.994904	valid-auc:0.773244
[1055]	train-auc:0.994912	valid-auc:0.773207
[1056]	train-auc:0.994921	valid-auc:0.773187
[1057]	train-auc:0.994932	valid-auc:0.773129
[1058]	train-auc:0.99494	valid-auc:0.773132
[1059]	train-auc:0.994954	valid-auc:0.773107
[1060]	train-auc:0.99496	valid-auc:0.773126
[1061]	train-auc:0.994968	valid-auc:0.773124
[1062]	train-auc:0.994978	valid-auc:0.773162
[1063]	train-auc:0.994984	valid-auc:0.773167
[1064]	train-auc:0.994991	valid-auc:0.773171
[1065]	train-auc:0.995015	valid-auc:0.773127
[1066]	train-auc:0.995035	valid-auc:0.773104
[1067]	train-auc:0.995047	valid-auc:0.773097
[1068]	train-auc:0.995059	valid-auc:0.773113
[1069]	train-auc:0.995065	valid-auc:0.773129
[1070]	train-auc:0.995079	valid-auc:0.773125
[1071]	train-auc:0.995092	valid-auc:0.773197
[1072]	train-auc:0.995106	valid-auc:0.773149
[1073]	train-auc:0.995111	valid-auc:0.773166
[1074]	train-auc:0.995122	valid-auc:0.773158
[1075]	train-auc:0.995125	valid-auc:0.773191
[1076]	train-auc:0.995131	valid-auc:0.773224
[1077]	train-auc:0.995136	valid-auc:0.773195
[1078]	train-auc:0.995158	valid-auc:0.77323
[1079]	train-auc:0.995175	valid-auc:0.773215
[1080]	train-auc:0.995187	valid-auc:0.773199
[1081]	train-auc:0.995198	valid-auc:0.773214
[1082]	train-auc:0.995213	valid-auc:0.773173
[1083]	train-auc:0.995225	valid-auc:0.773179
[1084]	train-auc:0.995237	valid-auc:0.773264
Stopping. Best iteration:
[1034]	train-auc:0.994699	valid-auc:0.773426

[mlcrate] Finished training fold 2 - took 2m00s - running score 0.7771376666666666
[mlcrate] Running fold 3, 62697 train samples, 10450 validation samples
[0]	train-auc:0.74394	valid-auc:0.674659
Multiple eval metrics have been passed: 'valid-auc' will be used for early stopping.

Will train until valid-auc hasn't improved in 50 rounds.
[1]	train-auc:0.769401	valid-auc:0.684772
[2]	train-auc:0.778472	valid-auc:0.692356
[3]	train-auc:0.786982	valid-auc:0.69754
[4]	train-auc:0.790916	valid-auc:0.700989
[5]	train-auc:0.796801	valid-auc:0.703469
[6]	train-auc:0.798634	valid-auc:0.704617
[7]	train-auc:0.801395	valid-auc:0.706028
[8]	train-auc:0.803032	valid-auc:0.707215
[9]	train-auc:0.804638	valid-auc:0.707463
[10]	train-auc:0.80594	valid-auc:0.707835
[11]	train-auc:0.807492	valid-auc:0.708533
[12]	train-auc:0.811231	valid-auc:0.709993
[13]	train-auc:0.813265	valid-auc:0.710701
[14]	train-auc:0.814706	valid-auc:0.711384
[15]	train-auc:0.816613	valid-auc:0.712163
[16]	train-auc:0.818455	valid-auc:0.713107
[17]	train-auc:0.819046	valid-auc:0.713727
[18]	train-auc:0.82129	valid-auc:0.714254
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[1341]	train-auc:0.996976	valid-auc:0.776193
[1342]	train-auc:0.99699	valid-auc:0.776225
[1343]	train-auc:0.996997	valid-auc:0.776231
[1344]	train-auc:0.997	valid-auc:0.776251
[1345]	train-auc:0.997008	valid-auc:0.776302
[1346]	train-auc:0.997014	valid-auc:0.776353
[1347]	train-auc:0.997017	valid-auc:0.776306
[1348]	train-auc:0.997025	valid-auc:0.77631
[1349]	train-auc:0.997043	valid-auc:0.776267
[1350]	train-auc:0.997047	valid-auc:0.776271
[1351]	train-auc:0.997052	valid-auc:0.776258
[1352]	train-auc:0.997058	valid-auc:0.776257
[1353]	train-auc:0.997059	valid-auc:0.776254
[1354]	train-auc:0.997069	valid-auc:0.776219
[1355]	train-auc:0.997079	valid-auc:0.776239
[1356]	train-auc:0.99708	valid-auc:0.776237
[1357]	train-auc:0.99708	valid-auc:0.776245
[1358]	train-auc:0.997081	valid-auc:0.776277
[1359]	train-auc:0.997085	valid-auc:0.776291
[1360]	train-auc:0.997087	valid-auc:0.776303
[1361]	train-auc:0.997092	valid-auc:0.776289
[1362]	train-auc:0.997098	valid-auc:0.776283
[1363]	train-auc:0.997101	valid-auc:0.776331
[1364]	train-auc:0.997107	valid-auc:0.776323
[1365]	train-auc:0.997111	valid-auc:0.776342
[1366]	train-auc:0.997119	valid-auc:0.776365
[1367]	train-auc:0.997122	valid-auc:0.77639
[1368]	train-auc:0.997122	valid-auc:0.776382
[1369]	train-auc:0.997126	valid-auc:0.776392
[1370]	train-auc:0.997131	valid-auc:0.776377
[1371]	train-auc:0.997132	valid-auc:0.77638
[1372]	train-auc:0.997137	valid-auc:0.776356
[1373]	train-auc:0.997142	valid-auc:0.776346
[1374]	train-auc:0.997144	valid-auc:0.77634
[1375]	train-auc:0.997155	valid-auc:0.776311
[1376]	train-auc:0.997155	valid-auc:0.776313
[1377]	train-auc:0.997169	valid-auc:0.776273
[1378]	train-auc:0.997176	valid-auc:0.776306
[1379]	train-auc:0.997183	valid-auc:0.776329
[1380]	train-auc:0.997183	valid-auc:0.776269
[1381]	train-auc:0.997186	valid-auc:0.776296
[1382]	train-auc:0.997192	valid-auc:0.776309
[1383]	train-auc:0.997196	valid-auc:0.776341
[1384]	train-auc:0.997197	valid-auc:0.776349
[1385]	train-auc:0.997203	valid-auc:0.776375
[1386]	train-auc:0.997206	valid-auc:0.776342
[1387]	train-auc:0.997213	valid-auc:0.776345
[1388]	train-auc:0.997215	valid-auc:0.776326
[1389]	train-auc:0.99722	valid-auc:0.776331
[1390]	train-auc:0.997223	valid-auc:0.77632
[1391]	train-auc:0.997232	valid-auc:0.776307
[1392]	train-auc:0.997237	valid-auc:0.776313
[1393]	train-auc:0.997242	valid-auc:0.776346
[1394]	train-auc:0.997243	valid-auc:0.77638
[1395]	train-auc:0.997255	valid-auc:0.776383
[1396]	train-auc:0.997262	valid-auc:0.776373
[1397]	train-auc:0.997262	valid-auc:0.776378
[1398]	train-auc:0.997265	valid-auc:0.776386
[1399]	train-auc:0.997266	valid-auc:0.776396
[1400]	train-auc:0.997265	valid-auc:0.776386
[1401]	train-auc:0.99727	valid-auc:0.776352
[1402]	train-auc:0.997277	valid-auc:0.77633
[1403]	train-auc:0.997279	valid-auc:0.776341
[1404]	train-auc:0.997284	valid-auc:0.776368
[1405]	train-auc:0.997284	valid-auc:0.776361
[1406]	train-auc:0.997289	valid-auc:0.776358
[1407]	train-auc:0.997294	valid-auc:0.776352
[1408]	train-auc:0.997299	valid-auc:0.776347
[1409]	train-auc:0.997305	valid-auc:0.776364
[1410]	train-auc:0.997306	valid-auc:0.776372
[1411]	train-auc:0.997314	valid-auc:0.776357
[1412]	train-auc:0.997318	valid-auc:0.776341
[1413]	train-auc:0.997323	valid-auc:0.77636
[1414]	train-auc:0.997325	valid-auc:0.776375
[1415]	train-auc:0.997336	valid-auc:0.776391
[1416]	train-auc:0.997338	valid-auc:0.776403
[1417]	train-auc:0.997342	valid-auc:0.776388
[1418]	train-auc:0.997344	valid-auc:0.776391
[1419]	train-auc:0.997351	valid-auc:0.776384
[1420]	train-auc:0.997353	valid-auc:0.776401
[1421]	train-auc:0.997359	valid-auc:0.776369
[1422]	train-auc:0.997363	valid-auc:0.776379
[1423]	train-auc:0.997367	valid-auc:0.776326
[1424]	train-auc:0.997371	valid-auc:0.776333
[1425]	train-auc:0.997376	valid-auc:0.776322
[1426]	train-auc:0.997376	valid-auc:0.776319
[1427]	train-auc:0.99739	valid-auc:0.776319
[1428]	train-auc:0.997394	valid-auc:0.776352
[1429]	train-auc:0.997394	valid-auc:0.776354
[1430]	train-auc:0.997399	valid-auc:0.776326
[1431]	train-auc:0.997402	valid-auc:0.77634
[1432]	train-auc:0.997406	valid-auc:0.776375
[1433]	train-auc:0.997408	valid-auc:0.776357
[1434]	train-auc:0.997417	valid-auc:0.776315
[1435]	train-auc:0.99742	valid-auc:0.776332
[1436]	train-auc:0.997428	valid-auc:0.776336
[1437]	train-auc:0.997437	valid-auc:0.776341
[1438]	train-auc:0.997437	valid-auc:0.776371
[1439]	train-auc:0.997442	valid-auc:0.776381
[1440]	train-auc:0.997448	valid-auc:0.776391
[1441]	train-auc:0.997449	valid-auc:0.776359
[1442]	train-auc:0.99745	valid-auc:0.77634
[1443]	train-auc:0.997456	valid-auc:0.776315
[1444]	train-auc:0.997468	valid-auc:0.776305
[1445]	train-auc:0.997474	valid-auc:0.776281
[1446]	train-auc:0.99748	valid-auc:0.776295
[1447]	train-auc:0.997488	valid-auc:0.776259
[1448]	train-auc:0.997491	valid-auc:0.776249
[1449]	train-auc:0.997497	valid-auc:0.776265
[1450]	train-auc:0.997502	valid-auc:0.776259
[1451]	train-auc:0.997502	valid-auc:0.776254
[1452]	train-auc:0.997502	valid-auc:0.776245
[1453]	train-auc:0.997506	valid-auc:0.776251
[1454]	train-auc:0.997507	valid-auc:0.776268
[1455]	train-auc:0.997514	valid-auc:0.776268
[1456]	train-auc:0.997519	valid-auc:0.77624
[1457]	train-auc:0.997521	valid-auc:0.776243
[1458]	train-auc:0.997527	valid-auc:0.776227
[1459]	train-auc:0.99753	valid-auc:0.776237
[1460]	train-auc:0.997536	valid-auc:0.776239
[1461]	train-auc:0.997537	valid-auc:0.776232
[1462]	train-auc:0.997539	valid-auc:0.776225
[1463]	train-auc:0.99755	valid-auc:0.776195
[1464]	train-auc:0.997555	valid-auc:0.776167
[1465]	train-auc:0.997561	valid-auc:0.776192
[1466]	train-auc:0.997566	valid-auc:0.776174
Stopping. Best iteration:
[1416]	train-auc:0.997338	valid-auc:0.776403

[mlcrate] Finished training fold 3 - took 2m43s - running score 0.7769539999999999
[mlcrate] Running fold 4, 62697 train samples, 10450 validation samples
[0]	train-auc:0.746956	valid-auc:0.670988
Multiple eval metrics have been passed: 'valid-auc' will be used for early stopping.

Will train until valid-auc hasn't improved in 50 rounds.
[1]	train-auc:0.768093	valid-auc:0.689788
[2]	train-auc:0.781885	valid-auc:0.696252
[3]	train-auc:0.789254	valid-auc:0.699535
[4]	train-auc:0.791979	valid-auc:0.702691
[5]	train-auc:0.795511	valid-auc:0.704037
[6]	train-auc:0.798946	valid-auc:0.705737
[7]	train-auc:0.801555	valid-auc:0.7085
[8]	train-auc:0.803305	valid-auc:0.708046
[9]	train-auc:0.805449	valid-auc:0.708825
[10]	train-auc:0.808168	valid-auc:0.709625
[11]	train-auc:0.810817	valid-auc:0.710687
[12]	train-auc:0.814694	valid-auc:0.711064
[13]	train-auc:0.815728	valid-auc:0.711811
[14]	train-auc:0.816911	valid-auc:0.712447
[15]	train-auc:0.818454	valid-auc:0.71362
[16]	train-auc:0.820292	valid-auc:0.714172
[17]	train-auc:0.821234	valid-auc:0.715081
[18]	train-auc:0.824223	valid-auc:0.715677
[19]	train-auc:0.825214	valid-auc:0.716321
[20]	train-auc:0.826482	valid-auc:0.717077
[21]	train-auc:0.828018	valid-auc:0.717773
[22]	train-auc:0.829016	valid-auc:0.71845
[23]	train-auc:0.830553	valid-auc:0.719168
[24]	train-auc:0.832202	valid-auc:0.72064
[25]	train-auc:0.833488	valid-auc:0.721154
[26]	train-auc:0.835565	valid-auc:0.721623
[27]	train-auc:0.837284	valid-auc:0.722047
[28]	train-auc:0.838729	valid-auc:0.722826
[29]	train-auc:0.840526	valid-auc:0.723185
[30]	train-auc:0.842274	valid-auc:0.723836
[31]	train-auc:0.843466	valid-auc:0.724047
[32]	train-auc:0.844264	valid-auc:0.72456
[33]	train-auc:0.846057	valid-auc:0.725069
[34]	train-auc:0.846573	valid-auc:0.725653
[35]	train-auc:0.84801	valid-auc:0.726208
[36]	train-auc:0.849825	valid-auc:0.727088
[37]	train-auc:0.85143	valid-auc:0.727694
[38]	train-auc:0.852254	valid-auc:0.72802
[39]	train-auc:0.853194	valid-auc:0.72851
[40]	train-auc:0.854126	valid-auc:0.728883
[41]	train-auc:0.855231	valid-auc:0.729051
[42]	train-auc:0.856984	valid-auc:0.729632
[43]	train-auc:0.857999	valid-auc:0.729937
[44]	train-auc:0.858967	valid-auc:0.730207
[45]	train-auc:0.860186	valid-auc:0.730803
[46]	train-auc:0.860971	valid-auc:0.731048
[47]	train-auc:0.862652	valid-auc:0.731794
[48]	train-auc:0.863212	valid-auc:0.73206
[49]	train-auc:0.864006	valid-auc:0.731853
[50]	train-auc:0.864848	valid-auc:0.732273
[51]	train-auc:0.865702	valid-auc:0.732431
[52]	train-auc:0.866636	valid-auc:0.732634
[53]	train-auc:0.867116	valid-auc:0.732769
[54]	train-auc:0.867451	valid-auc:0.732712
[55]	train-auc:0.869223	valid-auc:0.733469
[56]	train-auc:0.869864	valid-auc:0.733857
[57]	train-auc:0.871344	valid-auc:0.734292
[58]	train-auc:0.872213	valid-auc:0.734305
[59]	train-auc:0.872502	valid-auc:0.73431
[60]	train-auc:0.873134	valid-auc:0.734475
[61]	train-auc:0.87413	valid-auc:0.734596
[62]	train-auc:0.87494	valid-auc:0.734697
[63]	train-auc:0.875694	valid-auc:0.735023
[64]	train-auc:0.876536	valid-auc:0.735477
[65]	train-auc:0.87764	valid-auc:0.735784
[66]	train-auc:0.87883	valid-auc:0.736349
[67]	train-auc:0.880101	valid-auc:0.736832
[68]	train-auc:0.880554	valid-auc:0.737164
[69]	train-auc:0.881263	valid-auc:0.737407
[70]	train-auc:0.881657	valid-auc:0.737571
[71]	train-auc:0.882047	valid-auc:0.737736
[72]	train-auc:0.882573	valid-auc:0.738004
[73]	train-auc:0.883163	valid-auc:0.73836
[74]	train-auc:0.883801	valid-auc:0.738668
[75]	train-auc:0.884622	valid-auc:0.738956
[76]	train-auc:0.885164	valid-auc:0.73931
[77]	train-auc:0.88581	valid-auc:0.739606
[78]	train-auc:0.886512	valid-auc:0.739891
[79]	train-auc:0.8868	valid-auc:0.739921
[80]	train-auc:0.887305	valid-auc:0.739921
[81]	train-auc:0.888002	valid-auc:0.740251
[82]	train-auc:0.88802	valid-auc:0.74035
[83]	train-auc:0.888781	valid-auc:0.740904
[84]	train-auc:0.889132	valid-auc:0.741049
[85]	train-auc:0.889522	valid-auc:0.74115
[86]	train-auc:0.890295	valid-auc:0.741182
[87]	train-auc:0.890676	valid-auc:0.741205
[88]	train-auc:0.891491	valid-auc:0.741502
[89]	train-auc:0.89213	valid-auc:0.741608
[90]	train-auc:0.892854	valid-auc:0.741702
[91]	train-auc:0.893153	valid-auc:0.741764
[92]	train-auc:0.89338	valid-auc:0.741911
[93]	train-auc:0.893764	valid-auc:0.742068
[94]	train-auc:0.893958	valid-auc:0.742181
[95]	train-auc:0.89437	valid-auc:0.742161
[96]	train-auc:0.894542	valid-auc:0.742084
[97]	train-auc:0.894876	valid-auc:0.742381
[98]	train-auc:0.89537	valid-auc:0.742502
[99]	train-auc:0.89546	valid-auc:0.742601
[100]	train-auc:0.89572	valid-auc:0.742621
[101]	train-auc:0.896421	valid-auc:0.743002
[102]	train-auc:0.897528	valid-auc:0.743341
[103]	train-auc:0.897941	valid-auc:0.743415
[104]	train-auc:0.898437	valid-auc:0.743663
[105]	train-auc:0.898545	valid-auc:0.743657
[106]	train-auc:0.899266	valid-auc:0.743819
[107]	train-auc:0.89957	valid-auc:0.74397
[108]	train-auc:0.899783	valid-auc:0.744067
[109]	train-auc:0.900797	valid-auc:0.74418
[110]	train-auc:0.901136	valid-auc:0.744314
[111]	train-auc:0.901612	valid-auc:0.744699
[112]	train-auc:0.902411	valid-auc:0.744849
[113]	train-auc:0.902888	valid-auc:0.744983
[114]	train-auc:0.903351	valid-auc:0.745133
[115]	train-auc:0.903761	valid-auc:0.745028
[116]	train-auc:0.905014	valid-auc:0.745537
[117]	train-auc:0.905619	valid-auc:0.745606
[118]	train-auc:0.905924	valid-auc:0.74578
[119]	train-auc:0.906691	valid-auc:0.746042
[120]	train-auc:0.906982	valid-auc:0.746146
[121]	train-auc:0.907394	valid-auc:0.746134
[122]	train-auc:0.907872	valid-auc:0.746306
[123]	train-auc:0.908166	valid-auc:0.746402
[124]	train-auc:0.908533	valid-auc:0.74653
[125]	train-auc:0.909063	valid-auc:0.746722
[126]	train-auc:0.909579	valid-auc:0.746784
[127]	train-auc:0.910107	valid-auc:0.746934
[128]	train-auc:0.910619	valid-auc:0.747121
[129]	train-auc:0.911242	valid-auc:0.747542
[130]	train-auc:0.911975	valid-auc:0.747676
[131]	train-auc:0.912102	valid-auc:0.747703
[132]	train-auc:0.912309	valid-auc:0.747868
[133]	train-auc:0.912633	valid-auc:0.747838
[134]	train-auc:0.91285	valid-auc:0.747949
[135]	train-auc:0.913234	valid-auc:0.748092
[136]	train-auc:0.913458	valid-auc:0.748178
[137]	train-auc:0.913523	valid-auc:0.748207
[138]	train-auc:0.914064	valid-auc:0.748531
[139]	train-auc:0.914311	valid-auc:0.748704
[140]	train-auc:0.914659	valid-auc:0.748708
[141]	train-auc:0.91504	valid-auc:0.74884
[142]	train-auc:0.915433	valid-auc:0.749069
[143]	train-auc:0.915692	valid-auc:0.749137
[144]	train-auc:0.916208	valid-auc:0.749286
[145]	train-auc:0.916679	valid-auc:0.749299
[146]	train-auc:0.916958	valid-auc:0.749246
[147]	train-auc:0.917408	valid-auc:0.749514
[148]	train-auc:0.917608	valid-auc:0.749633
[149]	train-auc:0.918254	valid-auc:0.749862
[150]	train-auc:0.918323	valid-auc:0.749906
[151]	train-auc:0.918631	valid-auc:0.750031
[152]	train-auc:0.91895	valid-auc:0.750078
[153]	train-auc:0.919529	valid-auc:0.750211
[154]	train-auc:0.920473	valid-auc:0.750377
[155]	train-auc:0.920937	valid-auc:0.750512
[156]	train-auc:0.921574	valid-auc:0.7508
[157]	train-auc:0.921769	valid-auc:0.750794
[158]	train-auc:0.922118	valid-auc:0.75077
[159]	train-auc:0.922365	valid-auc:0.750988
[160]	train-auc:0.922789	valid-auc:0.75115
[161]	train-auc:0.923407	valid-auc:0.751212
[162]	train-auc:0.923862	valid-auc:0.751439
[163]	train-auc:0.924294	valid-auc:0.751814
[164]	train-auc:0.924749	valid-auc:0.751889
[165]	train-auc:0.925131	valid-auc:0.751843
[166]	train-auc:0.925354	valid-auc:0.751666
[167]	train-auc:0.925628	valid-auc:0.751771
[168]	train-auc:0.926004	valid-auc:0.751797
[169]	train-auc:0.926228	valid-auc:0.751915
[170]	train-auc:0.926623	valid-auc:0.751936
[171]	train-auc:0.927059	valid-auc:0.75216
[172]	train-auc:0.927273	valid-auc:0.752293
[173]	train-auc:0.927616	valid-auc:0.752386
[174]	train-auc:0.927897	valid-auc:0.752353
[175]	train-auc:0.928152	valid-auc:0.752443
[176]	train-auc:0.928279	valid-auc:0.752466
[177]	train-auc:0.928812	valid-auc:0.752614
[178]	train-auc:0.929162	valid-auc:0.752619
[179]	train-auc:0.929516	valid-auc:0.752733
[180]	train-auc:0.930101	valid-auc:0.753016
[181]	train-auc:0.930614	valid-auc:0.75302
[182]	train-auc:0.930868	valid-auc:0.753033
[183]	train-auc:0.931218	valid-auc:0.753137
[184]	train-auc:0.93137	valid-auc:0.753009
[185]	train-auc:0.931694	valid-auc:0.752858
[186]	train-auc:0.931899	valid-auc:0.752896
[187]	train-auc:0.932335	valid-auc:0.752879
[188]	train-auc:0.932788	valid-auc:0.752948
[189]	train-auc:0.932968	valid-auc:0.753066
[190]	train-auc:0.933275	valid-auc:0.753257
[191]	train-auc:0.933816	valid-auc:0.753457
[192]	train-auc:0.933988	valid-auc:0.753572
[193]	train-auc:0.934192	valid-auc:0.753508
[194]	train-auc:0.93453	valid-auc:0.753395
[195]	train-auc:0.93499	valid-auc:0.753557
[196]	train-auc:0.935072	valid-auc:0.75357
[197]	train-auc:0.935261	valid-auc:0.753597
[198]	train-auc:0.935264	valid-auc:0.753572
[199]	train-auc:0.935559	valid-auc:0.753592
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[1851]	train-auc:0.998597	valid-auc:0.779603
[1852]	train-auc:0.9986	valid-auc:0.779609
[1853]	train-auc:0.998602	valid-auc:0.779607
[1854]	train-auc:0.998603	valid-auc:0.779616
[1855]	train-auc:0.998602	valid-auc:0.779615
[1856]	train-auc:0.998607	valid-auc:0.77964
[1857]	train-auc:0.998611	valid-auc:0.779617
[1858]	train-auc:0.998612	valid-auc:0.779623
[1859]	train-auc:0.998612	valid-auc:0.779608
[1860]	train-auc:0.998614	valid-auc:0.779621
[1861]	train-auc:0.998615	valid-auc:0.779619
[1862]	train-auc:0.998615	valid-auc:0.779611
[1863]	train-auc:0.998616	valid-auc:0.779623
[1864]	train-auc:0.998617	valid-auc:0.779612
[1865]	train-auc:0.998618	valid-auc:0.779611
[1866]	train-auc:0.998617	valid-auc:0.779621
[1867]	train-auc:0.998619	valid-auc:0.779612
[1868]	train-auc:0.99862	valid-auc:0.779621
[1869]	train-auc:0.998621	valid-auc:0.779587
[1870]	train-auc:0.998621	valid-auc:0.779582
[1871]	train-auc:0.998624	valid-auc:0.779563
[1872]	train-auc:0.998625	valid-auc:0.779608
[1873]	train-auc:0.998626	valid-auc:0.779592
[1874]	train-auc:0.998626	valid-auc:0.77959
[1875]	train-auc:0.998629	valid-auc:0.779586
[1876]	train-auc:0.998634	valid-auc:0.779618
[1877]	train-auc:0.998636	valid-auc:0.779616
[1878]	train-auc:0.998638	valid-auc:0.779612
[1879]	train-auc:0.998639	valid-auc:0.779602
[1880]	train-auc:0.998641	valid-auc:0.779616
[1881]	train-auc:0.998643	valid-auc:0.779622
[1882]	train-auc:0.998643	valid-auc:0.779637
[1883]	train-auc:0.998644	valid-auc:0.779624
[1884]	train-auc:0.998645	valid-auc:0.779612
[1885]	train-auc:0.998646	valid-auc:0.779616
[1886]	train-auc:0.998647	valid-auc:0.779638
[1887]	train-auc:0.998648	valid-auc:0.779653
[1888]	train-auc:0.998648	valid-auc:0.779654
[1889]	train-auc:0.998653	valid-auc:0.7797
[1890]	train-auc:0.998653	valid-auc:0.779717
[1891]	train-auc:0.998653	valid-auc:0.779739
[1892]	train-auc:0.998655	valid-auc:0.779754
[1893]	train-auc:0.998656	valid-auc:0.779747
[1894]	train-auc:0.998658	valid-auc:0.779775
[1895]	train-auc:0.998661	valid-auc:0.779796
[1896]	train-auc:0.998661	valid-auc:0.779786
[1897]	train-auc:0.998664	valid-auc:0.779756
[1898]	train-auc:0.998668	valid-auc:0.779762
[1899]	train-auc:0.998671	valid-auc:0.779744
[1900]	train-auc:0.998673	valid-auc:0.77973
[1901]	train-auc:0.998675	valid-auc:0.779712
[1902]	train-auc:0.998677	valid-auc:0.77972
[1903]	train-auc:0.998679	valid-auc:0.779714
[1904]	train-auc:0.998682	valid-auc:0.779708
[1905]	train-auc:0.998684	valid-auc:0.779712
[1906]	train-auc:0.998685	valid-auc:0.779716
[1907]	train-auc:0.998685	valid-auc:0.779709
[1908]	train-auc:0.998686	valid-auc:0.779714
[1909]	train-auc:0.998689	valid-auc:0.779697
[1910]	train-auc:0.998693	valid-auc:0.779709
[1911]	train-auc:0.998693	valid-auc:0.779722
[1912]	train-auc:0.998698	valid-auc:0.77972
[1913]	train-auc:0.9987	valid-auc:0.7797
[1914]	train-auc:0.998702	valid-auc:0.779714
[1915]	train-auc:0.998704	valid-auc:0.779717
[1916]	train-auc:0.998708	valid-auc:0.779726
[1917]	train-auc:0.998709	valid-auc:0.779716
[1918]	train-auc:0.99871	valid-auc:0.779695
[1919]	train-auc:0.998709	valid-auc:0.779693
[1920]	train-auc:0.998711	valid-auc:0.779682
[1921]	train-auc:0.998715	valid-auc:0.779739
[1922]	train-auc:0.998716	valid-auc:0.77972
[1923]	train-auc:0.998717	valid-auc:0.7797
[1924]	train-auc:0.998721	valid-auc:0.779738
[1925]	train-auc:0.998722	valid-auc:0.779729
[1926]	train-auc:0.998721	valid-auc:0.779711
[1927]	train-auc:0.998722	valid-auc:0.779704
[1928]	train-auc:0.998722	valid-auc:0.779689
[1929]	train-auc:0.998724	valid-auc:0.779694
[1930]	train-auc:0.998725	valid-auc:0.779694
[1931]	train-auc:0.998726	valid-auc:0.779695
[1932]	train-auc:0.998726	valid-auc:0.779688
[1933]	train-auc:0.998727	valid-auc:0.779682
[1934]	train-auc:0.998731	valid-auc:0.779734
[1935]	train-auc:0.998733	valid-auc:0.779736
[1936]	train-auc:0.998737	valid-auc:0.779735
[1937]	train-auc:0.99874	valid-auc:0.779682
[1938]	train-auc:0.998742	valid-auc:0.779669
[1939]	train-auc:0.998741	valid-auc:0.779673
[1940]	train-auc:0.998744	valid-auc:0.779679
[1941]	train-auc:0.998745	valid-auc:0.779682
[1942]	train-auc:0.998749	valid-auc:0.779675
[1943]	train-auc:0.998751	valid-auc:0.779679
[1944]	train-auc:0.998753	valid-auc:0.779682
[1945]	train-auc:0.998752	valid-auc:0.779698
Stopping. Best iteration:
[1895]	train-auc:0.998661	valid-auc:0.779796

[mlcrate] Finished training fold 4 - took 3m35s - running score 0.7775224
[mlcrate] Running fold 5, 62698 train samples, 10449 validation samples
[0]	train-auc:0.736592	valid-auc:0.665014
Multiple eval metrics have been passed: 'valid-auc' will be used for early stopping.

Will train until valid-auc hasn't improved in 50 rounds.
[1]	train-auc:0.766466	valid-auc:0.683679
[2]	train-auc:0.781141	valid-auc:0.692959
[3]	train-auc:0.789097	valid-auc:0.696946
[4]	train-auc:0.793632	valid-auc:0.700147
[5]	train-auc:0.797379	valid-auc:0.701855
[6]	train-auc:0.798892	valid-auc:0.704726
[7]	train-auc:0.804521	valid-auc:0.707012
[8]	train-auc:0.806353	valid-auc:0.707768
[9]	train-auc:0.806158	valid-auc:0.707511
[10]	train-auc:0.808138	valid-auc:0.708194
[11]	train-auc:0.81007	valid-auc:0.709482
[12]	train-auc:0.813218	valid-auc:0.710088
[13]	train-auc:0.81409	valid-auc:0.710837
[14]	train-auc:0.81693	valid-auc:0.711655
[15]	train-auc:0.819106	valid-auc:0.711094
[16]	train-auc:0.820592	valid-auc:0.711529
[17]	train-auc:0.821266	valid-auc:0.711572
[18]	train-auc:0.823153	valid-auc:0.713108
[19]	train-auc:0.824802	valid-auc:0.713073
[20]	train-auc:0.825208	valid-auc:0.713546
[21]	train-auc:0.827063	valid-auc:0.714013
[22]	train-auc:0.828904	valid-auc:0.71489
[23]	train-auc:0.831054	valid-auc:0.715311
[24]	train-auc:0.832539	valid-auc:0.715929
[25]	train-auc:0.834018	valid-auc:0.716402
[26]	train-auc:0.836029	valid-auc:0.71679
[27]	train-auc:0.838066	valid-auc:0.717923
[28]	train-auc:0.839473	valid-auc:0.718147
[29]	train-auc:0.841674	valid-auc:0.718675
[30]	train-auc:0.842324	valid-auc:0.718935
[31]	train-auc:0.843717	valid-auc:0.719899
[32]	train-auc:0.845705	valid-auc:0.719934
[33]	train-auc:0.846422	valid-auc:0.720298
[34]	train-auc:0.847971	valid-auc:0.721042
[35]	train-auc:0.849029	valid-auc:0.721372
[36]	train-auc:0.849739	valid-auc:0.721815
[37]	train-auc:0.851154	valid-auc:0.722151
[38]	train-auc:0.852319	valid-auc:0.722488
[39]	train-auc:0.853741	valid-auc:0.722538
[40]	train-auc:0.854213	valid-auc:0.722583
[41]	train-auc:0.85559	valid-auc:0.723006
[42]	train-auc:0.856515	valid-auc:0.723532
[43]	train-auc:0.857514	valid-auc:0.723692
[44]	train-auc:0.858437	valid-auc:0.723926
[45]	train-auc:0.859183	valid-auc:0.724167
[46]	train-auc:0.860263	valid-auc:0.724585
[47]	train-auc:0.860907	valid-auc:0.724901
[48]	train-auc:0.862478	valid-auc:0.725039
[49]	train-auc:0.86388	valid-auc:0.725361
[50]	train-auc:0.864956	valid-auc:0.725627
[51]	train-auc:0.865762	valid-auc:0.725854
[52]	train-auc:0.866442	valid-auc:0.725893
[53]	train-auc:0.867304	valid-auc:0.725879
[54]	train-auc:0.868362	valid-auc:0.726053
[55]	train-auc:0.868991	valid-auc:0.726339
[56]	train-auc:0.869556	valid-auc:0.726359
[57]	train-auc:0.87025	valid-auc:0.726572
[58]	train-auc:0.870686	valid-auc:0.726781
[59]	train-auc:0.871047	valid-auc:0.726855
[60]	train-auc:0.871945	valid-auc:0.726968
[61]	train-auc:0.873088	valid-auc:0.727341
[62]	train-auc:0.874037	valid-auc:0.727899
[63]	train-auc:0.874988	valid-auc:0.728543
[64]	train-auc:0.875386	valid-auc:0.728865
[65]	train-auc:0.87613	valid-auc:0.729221
[66]	train-auc:0.876905	valid-auc:0.729338
[67]	train-auc:0.877173	valid-auc:0.729407
[68]	train-auc:0.877932	valid-auc:0.729556
[69]	train-auc:0.878647	valid-auc:0.729558
[70]	train-auc:0.8791	valid-auc:0.729621
[71]	train-auc:0.879478	valid-auc:0.729728
[72]	train-auc:0.880159	valid-auc:0.729989
[73]	train-auc:0.881033	valid-auc:0.730296
[74]	train-auc:0.881039	valid-auc:0.7304
[75]	train-auc:0.881326	valid-auc:0.730582
[76]	train-auc:0.882187	valid-auc:0.730984
[77]	train-auc:0.882567	valid-auc:0.730882
[78]	train-auc:0.883037	valid-auc:0.730917
[79]	train-auc:0.883799	valid-auc:0.731254
[80]	train-auc:0.8847	valid-auc:0.731561
[81]	train-auc:0.88549	valid-auc:0.731806
[82]	train-auc:0.886159	valid-auc:0.732007
[83]	train-auc:0.886428	valid-auc:0.732158
[84]	train-auc:0.886998	valid-auc:0.732355
[85]	train-auc:0.888062	valid-auc:0.732812
[86]	train-auc:0.888469	valid-auc:0.73299
[87]	train-auc:0.888735	valid-auc:0.732906
[88]	train-auc:0.889375	valid-auc:0.733118
[89]	train-auc:0.889612	valid-auc:0.733065
[90]	train-auc:0.890302	valid-auc:0.733363
[91]	train-auc:0.890834	valid-auc:0.733587
[92]	train-auc:0.891507	valid-auc:0.733756
[93]	train-auc:0.892287	valid-auc:0.734111
[94]	train-auc:0.892515	valid-auc:0.73408
[95]	train-auc:0.893371	valid-auc:0.734451
[96]	train-auc:0.893743	valid-auc:0.734607
[97]	train-auc:0.894088	valid-auc:0.73468
[98]	train-auc:0.894643	valid-auc:0.73496
[99]	train-auc:0.894671	valid-auc:0.734955
[100]	train-auc:0.894965	valid-auc:0.735228
[101]	train-auc:0.895437	valid-auc:0.735465
[102]	train-auc:0.896494	valid-auc:0.735987
[103]	train-auc:0.89667	valid-auc:0.735969
[104]	train-auc:0.89709	valid-auc:0.736212
[105]	train-auc:0.897568	valid-auc:0.73625
[106]	train-auc:0.898192	valid-auc:0.736503
[107]	train-auc:0.898887	valid-auc:0.736645
[108]	train-auc:0.899677	valid-auc:0.736833
[109]	train-auc:0.899994	valid-auc:0.736997
[110]	train-auc:0.900144	valid-auc:0.737136
[111]	train-auc:0.901024	valid-auc:0.73751
[112]	train-auc:0.901834	valid-auc:0.737771
[113]	train-auc:0.901972	valid-auc:0.737764
[114]	train-auc:0.902595	valid-auc:0.738066
[115]	train-auc:0.903516	valid-auc:0.738422
[116]	train-auc:0.903839	valid-auc:0.738584
[117]	train-auc:0.904156	valid-auc:0.738651
[118]	train-auc:0.904252	valid-auc:0.738709
[119]	train-auc:0.90539	valid-auc:0.739151
[120]	train-auc:0.905764	valid-auc:0.739421
[121]	train-auc:0.90596	valid-auc:0.7394
[122]	train-auc:0.906482	valid-auc:0.739621
[123]	train-auc:0.906956	valid-auc:0.739743
[124]	train-auc:0.907577	valid-auc:0.740031
[125]	train-auc:0.907885	valid-auc:0.74014
[126]	train-auc:0.90824	valid-auc:0.740349
[127]	train-auc:0.908593	valid-auc:0.740509
[128]	train-auc:0.909079	valid-auc:0.740605
[129]	train-auc:0.910072	valid-auc:0.74112
[130]	train-auc:0.91074	valid-auc:0.741456
[131]	train-auc:0.910818	valid-auc:0.741504
[132]	train-auc:0.911511	valid-auc:0.741843
[133]	train-auc:0.911961	valid-auc:0.74191
[134]	train-auc:0.912277	valid-auc:0.742096
[135]	train-auc:0.912886	valid-auc:0.742362
[136]	train-auc:0.913346	valid-auc:0.74252
[137]	train-auc:0.913964	valid-auc:0.742708
[138]	train-auc:0.913987	valid-auc:0.742671
[139]	train-auc:0.914723	valid-auc:0.743078
[140]	train-auc:0.915079	valid-auc:0.743275
[141]	train-auc:0.915328	valid-auc:0.74341
[142]	train-auc:0.915879	valid-auc:0.743454
[143]	train-auc:0.916352	valid-auc:0.743678
[144]	train-auc:0.916924	valid-auc:0.743811
[145]	train-auc:0.91756	valid-auc:0.743971
[146]	train-auc:0.91801	valid-auc:0.743992
[147]	train-auc:0.918491	valid-auc:0.744154
[148]	train-auc:0.918509	valid-auc:0.744211
[149]	train-auc:0.918816	valid-auc:0.74427
[150]	train-auc:0.919069	valid-auc:0.744387
[151]	train-auc:0.919379	valid-auc:0.744456
[152]	train-auc:0.91983	valid-auc:0.744498
[153]	train-auc:0.920076	valid-auc:0.74454
[154]	train-auc:0.920354	valid-auc:0.744564
[155]	train-auc:0.920943	valid-auc:0.744784
[156]	train-auc:0.921147	valid-auc:0.744765
[157]	train-auc:0.921489	valid-auc:0.744825
[158]	train-auc:0.921974	valid-auc:0.745048
[159]	train-auc:0.922527	valid-auc:0.745232
[160]	train-auc:0.922834	valid-auc:0.745331
[161]	train-auc:0.923192	valid-auc:0.745425
[162]	train-auc:0.923272	valid-auc:0.745476
[163]	train-auc:0.923644	valid-auc:0.745564
[164]	train-auc:0.923773	valid-auc:0.745643
[165]	train-auc:0.924052	valid-auc:0.745759
[166]	train-auc:0.924396	valid-auc:0.745985
[167]	train-auc:0.924885	valid-auc:0.746191
[168]	train-auc:0.925212	valid-auc:0.746324
[169]	train-auc:0.925685	valid-auc:0.746573
[170]	train-auc:0.925931	valid-auc:0.74654
[171]	train-auc:0.926361	valid-auc:0.746823
[172]	train-auc:0.926851	valid-auc:0.746809
[173]	train-auc:0.926986	valid-auc:0.746834
[174]	train-auc:0.927342	valid-auc:0.747008
[175]	train-auc:0.927714	valid-auc:0.747146
[176]	train-auc:0.928029	valid-auc:0.747253
[177]	train-auc:0.928288	valid-auc:0.74729
[178]	train-auc:0.928392	valid-auc:0.747319
[179]	train-auc:0.92874	valid-auc:0.747571
[180]	train-auc:0.928939	valid-auc:0.74763
[181]	train-auc:0.929441	valid-auc:0.747788
[182]	train-auc:0.929757	valid-auc:0.74785
[183]	train-auc:0.930198	valid-auc:0.748003
[184]	train-auc:0.930781	valid-auc:0.748246
[185]	train-auc:0.931294	valid-auc:0.748483
[186]	train-auc:0.931729	valid-auc:0.748641
[187]	train-auc:0.931929	valid-auc:0.748729
[188]	train-auc:0.932196	valid-auc:0.748843
[189]	train-auc:0.932463	valid-auc:0.748997
[190]	train-auc:0.932626	valid-auc:0.749147
[191]	train-auc:0.933049	valid-auc:0.749367
[192]	train-auc:0.933593	valid-auc:0.749634
[193]	train-auc:0.933796	valid-auc:0.749777
[194]	train-auc:0.934164	valid-auc:0.749782
[195]	train-auc:0.934401	valid-auc:0.749741
[196]	train-auc:0.93452	valid-auc:0.74979
[197]	train-auc:0.934752	valid-auc:0.749822
[198]	train-auc:0.935234	valid-auc:0.749995
[199]	train-auc:0.935441	valid-auc:0.750027
[200]	train-auc:0.935497	valid-auc:0.750125
[201]	train-auc:0.935902	valid-auc:0.750269
[202]	train-auc:0.936111	valid-auc:0.750259
[203]	train-auc:0.936217	valid-auc:0.750284
[204]	train-auc:0.936677	valid-auc:0.750347
[205]	train-auc:0.936953	valid-auc:0.750373
[206]	train-auc:0.937275	valid-auc:0.750495
[207]	train-auc:0.937849	valid-auc:0.750674
[208]	train-auc:0.938007	valid-auc:0.750753
[209]	train-auc:0.938262	valid-auc:0.750722
[210]	train-auc:0.938342	valid-auc:0.750719
[211]	train-auc:0.938454	valid-auc:0.750649
[212]	train-auc:0.938725	valid-auc:0.750706
[213]	train-auc:0.938876	valid-auc:0.750962
[214]	train-auc:0.939136	valid-auc:0.750881
[215]	train-auc:0.939452	valid-auc:0.751031
[216]	train-auc:0.940084	valid-auc:0.751194
[217]	train-auc:0.94037	valid-auc:0.75142
[218]	train-auc:0.940775	valid-auc:0.751551
[219]	train-auc:0.941141	valid-auc:0.751804
[220]	train-auc:0.941398	valid-auc:0.751886
[221]	train-auc:0.941615	valid-auc:0.751757
[222]	train-auc:0.941861	valid-auc:0.751859
[223]	train-auc:0.941925	valid-auc:0.751909
[224]	train-auc:0.942002	valid-auc:0.752007
[225]	train-auc:0.942347	valid-auc:0.752161
[226]	train-auc:0.942585	valid-auc:0.752208
[227]	train-auc:0.942809	valid-auc:0.752212
[228]	train-auc:0.942838	valid-auc:0.752218
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[564]	train-auc:0.982028	valid-auc:0.766938
[565]	train-auc:0.982116	valid-auc:0.767082
[566]	train-auc:0.982175	valid-auc:0.76712
[567]	train-auc:0.982255	valid-auc:0.76727
[568]	train-auc:0.982302	valid-auc:0.767311
[569]	train-auc:0.982336	valid-auc:0.76727
[570]	train-auc:0.982356	valid-auc:0.767258
[571]	train-auc:0.982384	valid-auc:0.767275
[572]	train-auc:0.982427	valid-auc:0.76734
[573]	train-auc:0.982522	valid-auc:0.767397
[574]	train-auc:0.98257	valid-auc:0.767491
[575]	train-auc:0.982624	valid-auc:0.767458
[576]	train-auc:0.982706	valid-auc:0.767611
[577]	train-auc:0.982744	valid-auc:0.767653
[578]	train-auc:0.98279	valid-auc:0.767733
[579]	train-auc:0.982818	valid-auc:0.767728
[580]	train-auc:0.982862	valid-auc:0.767754
[581]	train-auc:0.982899	valid-auc:0.767803
[582]	train-auc:0.982994	valid-auc:0.767925
[583]	train-auc:0.98306	valid-auc:0.767951
[584]	train-auc:0.983104	valid-auc:0.767985
[585]	train-auc:0.983155	valid-auc:0.768013
[586]	train-auc:0.98321	valid-auc:0.76805
[587]	train-auc:0.983284	valid-auc:0.768046
[588]	train-auc:0.983336	valid-auc:0.768066
[589]	train-auc:0.983377	valid-auc:0.768043
[590]	train-auc:0.983417	valid-auc:0.768038
[591]	train-auc:0.983424	valid-auc:0.768039
[592]	train-auc:0.983527	valid-auc:0.768016
[593]	train-auc:0.983609	valid-auc:0.768053
[594]	train-auc:0.983647	valid-auc:0.768067
[595]	train-auc:0.983699	valid-auc:0.768034
[596]	train-auc:0.983711	valid-auc:0.768026
[597]	train-auc:0.983793	valid-auc:0.768091
[598]	train-auc:0.983804	valid-auc:0.768085
[599]	train-auc:0.983847	valid-auc:0.768165
[600]	train-auc:0.983927	valid-auc:0.768157
[601]	train-auc:0.983994	valid-auc:0.768207
[602]	train-auc:0.984047	valid-auc:0.768255
[603]	train-auc:0.984086	valid-auc:0.768345
[604]	train-auc:0.984113	valid-auc:0.768325
[605]	train-auc:0.984169	valid-auc:0.768376
[606]	train-auc:0.98417	valid-auc:0.768372
[607]	train-auc:0.984223	valid-auc:0.768402
[608]	train-auc:0.984261	valid-auc:0.768488
[609]	train-auc:0.984354	valid-auc:0.768544
[610]	train-auc:0.984372	valid-auc:0.76859
[611]	train-auc:0.984421	valid-auc:0.768615
[612]	train-auc:0.984443	valid-auc:0.768638
[613]	train-auc:0.984489	valid-auc:0.768624
[614]	train-auc:0.984514	valid-auc:0.768614
[615]	train-auc:0.984568	valid-auc:0.768642
[616]	train-auc:0.984588	valid-auc:0.768656
[617]	train-auc:0.9846	valid-auc:0.768625
[618]	train-auc:0.984666	valid-auc:0.768608
[619]	train-auc:0.9847	valid-auc:0.76865
[620]	train-auc:0.98479	valid-auc:0.768779
[621]	train-auc:0.98485	valid-auc:0.768852
[622]	train-auc:0.984878	valid-auc:0.768918
[623]	train-auc:0.984936	valid-auc:0.769042
[624]	train-auc:0.984938	valid-auc:0.769029
[625]	train-auc:0.984985	valid-auc:0.769065
[626]	train-auc:0.985017	valid-auc:0.769061
[627]	train-auc:0.985068	valid-auc:0.76907
[628]	train-auc:0.98508	valid-auc:0.769085
[629]	train-auc:0.985115	valid-auc:0.769094
[630]	train-auc:0.985189	valid-auc:0.769099
[631]	train-auc:0.985199	valid-auc:0.769131
[632]	train-auc:0.985257	valid-auc:0.769242
[633]	train-auc:0.985275	valid-auc:0.769267
[634]	train-auc:0.985303	valid-auc:0.769306
[635]	train-auc:0.985334	valid-auc:0.769304
[636]	train-auc:0.98538	valid-auc:0.769271
[637]	train-auc:0.985423	valid-auc:0.769186
[638]	train-auc:0.98547	valid-auc:0.769206
[639]	train-auc:0.985512	valid-auc:0.769177
[640]	train-auc:0.985568	valid-auc:0.769246
[641]	train-auc:0.985632	valid-auc:0.769425
[642]	train-auc:0.985676	valid-auc:0.769481
[643]	train-auc:0.985729	valid-auc:0.769589
[644]	train-auc:0.985784	valid-auc:0.769585
[645]	train-auc:0.985884	valid-auc:0.769581
[646]	train-auc:0.985878	valid-auc:0.769583
[647]	train-auc:0.985895	valid-auc:0.769562
[648]	train-auc:0.985934	valid-auc:0.769602
[649]	train-auc:0.985979	valid-auc:0.7696
[650]	train-auc:0.986025	valid-auc:0.769613
[651]	train-auc:0.986064	valid-auc:0.769608
[652]	train-auc:0.986075	valid-auc:0.769626
[653]	train-auc:0.986144	valid-auc:0.769638
[654]	train-auc:0.986192	valid-auc:0.769562
[655]	train-auc:0.986248	valid-auc:0.769537
[656]	train-auc:0.986267	valid-auc:0.769563
[657]	train-auc:0.986288	valid-auc:0.769557
[658]	train-auc:0.986327	valid-auc:0.769542
[659]	train-auc:0.986329	valid-auc:0.769536
[660]	train-auc:0.986409	valid-auc:0.769533
[661]	train-auc:0.98643	valid-auc:0.769572
[662]	train-auc:0.986472	valid-auc:0.769608
[663]	train-auc:0.986549	valid-auc:0.769692
[664]	train-auc:0.986626	valid-auc:0.769702
[665]	train-auc:0.986688	valid-auc:0.769743
[666]	train-auc:0.986712	valid-auc:0.769811
[667]	train-auc:0.986749	valid-auc:0.769813
[668]	train-auc:0.986761	valid-auc:0.769773
[669]	train-auc:0.986812	valid-auc:0.769705
[670]	train-auc:0.98688	valid-auc:0.769726
[671]	train-auc:0.986915	valid-auc:0.769774
[672]	train-auc:0.986942	valid-auc:0.769766
[673]	train-auc:0.987031	valid-auc:0.769884
[674]	train-auc:0.987083	valid-auc:0.770006
[675]	train-auc:0.987099	valid-auc:0.770047
[676]	train-auc:0.987125	valid-auc:0.770063
[677]	train-auc:0.987196	valid-auc:0.770055
[678]	train-auc:0.987205	valid-auc:0.770064
[679]	train-auc:0.987219	valid-auc:0.770099
[680]	train-auc:0.987251	valid-auc:0.770198
[681]	train-auc:0.98728	valid-auc:0.770181
[682]	train-auc:0.9873	valid-auc:0.770243
[683]	train-auc:0.987348	valid-auc:0.77021
[684]	train-auc:0.987351	valid-auc:0.770196
[685]	train-auc:0.987449	valid-auc:0.770257
[686]	train-auc:0.987459	valid-auc:0.770258
[687]	train-auc:0.987481	valid-auc:0.770318
[688]	train-auc:0.987504	valid-auc:0.770301
[689]	train-auc:0.987544	valid-auc:0.770303
[690]	train-auc:0.987563	valid-auc:0.770317
[691]	train-auc:0.987578	valid-auc:0.770317
[692]	train-auc:0.987606	valid-auc:0.770344
[693]	train-auc:0.987673	valid-auc:0.770336
[694]	train-auc:0.987707	valid-auc:0.770331
[695]	train-auc:0.987732	valid-auc:0.77035
[696]	train-auc:0.987777	valid-auc:0.770444
[697]	train-auc:0.987794	valid-auc:0.770447
[698]	train-auc:0.987797	valid-auc:0.770435
[699]	train-auc:0.987826	valid-auc:0.770449
[700]	train-auc:0.987846	valid-auc:0.770484
[701]	train-auc:0.987857	valid-auc:0.770482
[702]	train-auc:0.987898	valid-auc:0.770553
[703]	train-auc:0.987933	valid-auc:0.770588
[704]	train-auc:0.987959	valid-auc:0.770597
[705]	train-auc:0.987991	valid-auc:0.770588
[706]	train-auc:0.987999	valid-auc:0.770592
[707]	train-auc:0.98805	valid-auc:0.770602
[708]	train-auc:0.988077	valid-auc:0.770559
[709]	train-auc:0.988094	valid-auc:0.770553
[710]	train-auc:0.98811	valid-auc:0.770548
[711]	train-auc:0.988138	valid-auc:0.770597
[712]	train-auc:0.988164	valid-auc:0.770626
[713]	train-auc:0.988193	valid-auc:0.770633
[714]	train-auc:0.988215	valid-auc:0.770617
[715]	train-auc:0.98828	valid-auc:0.770675
[716]	train-auc:0.988369	valid-auc:0.770735
[717]	train-auc:0.98844	valid-auc:0.770682
[718]	train-auc:0.988474	valid-auc:0.770692
[719]	train-auc:0.98851	valid-auc:0.770716
[720]	train-auc:0.988539	valid-auc:0.770738
[721]	train-auc:0.988548	valid-auc:0.770762
[722]	train-auc:0.988585	valid-auc:0.770753
[723]	train-auc:0.988618	valid-auc:0.770731
[724]	train-auc:0.988647	valid-auc:0.770713
[725]	train-auc:0.988653	valid-auc:0.770711
[726]	train-auc:0.988698	valid-auc:0.770761
[727]	train-auc:0.988728	valid-auc:0.770835
[728]	train-auc:0.988755	valid-auc:0.770947
[729]	train-auc:0.988798	valid-auc:0.770981
[730]	train-auc:0.988807	valid-auc:0.771038
[731]	train-auc:0.988849	valid-auc:0.771086
[732]	train-auc:0.988858	valid-auc:0.771056
[733]	train-auc:0.988897	valid-auc:0.771052
[734]	train-auc:0.98897	valid-auc:0.771167
[735]	train-auc:0.988999	valid-auc:0.771125
[736]	train-auc:0.989011	valid-auc:0.771095
[737]	train-auc:0.98902	valid-auc:0.771091
[738]	train-auc:0.989069	valid-auc:0.771014
[739]	train-auc:0.989078	valid-auc:0.771054
[740]	train-auc:0.989102	valid-auc:0.77113
[741]	train-auc:0.989117	valid-auc:0.771154
[742]	train-auc:0.989157	valid-auc:0.771191
[743]	train-auc:0.989199	valid-auc:0.771278
[744]	train-auc:0.989245	valid-auc:0.771296
[745]	train-auc:0.989258	valid-auc:0.771287
[746]	train-auc:0.989269	valid-auc:0.771259
[747]	train-auc:0.989299	valid-auc:0.771246
[748]	train-auc:0.989332	valid-auc:0.771269
[749]	train-auc:0.989355	valid-auc:0.771244
[750]	train-auc:0.989375	valid-auc:0.771204
[751]	train-auc:0.98939	valid-auc:0.771213
[752]	train-auc:0.989426	valid-auc:0.771211
[753]	train-auc:0.989442	valid-auc:0.771172
[754]	train-auc:0.989447	valid-auc:0.771234
[755]	train-auc:0.98947	valid-auc:0.771264
[756]	train-auc:0.98949	valid-auc:0.771222
[757]	train-auc:0.989506	valid-auc:0.771182
[758]	train-auc:0.989561	valid-auc:0.771128
[759]	train-auc:0.989569	valid-auc:0.771074
[760]	train-auc:0.989586	valid-auc:0.771115
[761]	train-auc:0.989604	valid-auc:0.771089
[762]	train-auc:0.989611	valid-auc:0.771028
[763]	train-auc:0.989633	valid-auc:0.771056
[764]	train-auc:0.989668	valid-auc:0.771072
[765]	train-auc:0.989721	valid-auc:0.771102
[766]	train-auc:0.98974	valid-auc:0.771103
[767]	train-auc:0.989796	valid-auc:0.771064
[768]	train-auc:0.989833	valid-auc:0.771039
[769]	train-auc:0.98985	valid-auc:0.771013
[770]	train-auc:0.989882	valid-auc:0.771036
[771]	train-auc:0.989891	valid-auc:0.771001
[772]	train-auc:0.989948	valid-auc:0.771044
[773]	train-auc:0.989979	valid-auc:0.77107
[774]	train-auc:0.990002	valid-auc:0.771063
[775]	train-auc:0.990016	valid-auc:0.771084
[776]	train-auc:0.990037	valid-auc:0.771059
[777]	train-auc:0.990057	valid-auc:0.771102
[778]	train-auc:0.990081	valid-auc:0.771116
[779]	train-auc:0.990133	valid-auc:0.771092
[780]	train-auc:0.990146	valid-auc:0.771043
[781]	train-auc:0.990162	valid-auc:0.771079
[782]	train-auc:0.990185	valid-auc:0.771053
[783]	train-auc:0.990201	valid-auc:0.771064
[784]	train-auc:0.990215	valid-auc:0.771041
[785]	train-auc:0.990251	valid-auc:0.771075
[786]	train-auc:0.990262	valid-auc:0.771081
[787]	train-auc:0.990272	valid-auc:0.771107
[788]	train-auc:0.990331	valid-auc:0.771088
[789]	train-auc:0.990357	valid-auc:0.771114
[790]	train-auc:0.990391	valid-auc:0.771103
[791]	train-auc:0.990439	valid-auc:0.771155
[792]	train-auc:0.99045	valid-auc:0.771135
[793]	train-auc:0.990482	valid-auc:0.771205
[794]	train-auc:0.990504	valid-auc:0.77129
Stopping. Best iteration:
[744]	train-auc:0.989245	valid-auc:0.771296

[mlcrate] Finished training fold 5 - took 1m28s - running score 0.7764846666666667
[mlcrate] Running fold 6, 62699 train samples, 10448 validation samples
[0]	train-auc:0.743067	valid-auc:0.677507
Multiple eval metrics have been passed: 'valid-auc' will be used for early stopping.

Will train until valid-auc hasn't improved in 50 rounds.
[1]	train-auc:0.766071	valid-auc:0.692311
[2]	train-auc:0.772378	valid-auc:0.701529
[3]	train-auc:0.78299	valid-auc:0.705493
[4]	train-auc:0.787034	valid-auc:0.708906
[5]	train-auc:0.78847	valid-auc:0.711902
[6]	train-auc:0.790769	valid-auc:0.712879
[7]	train-auc:0.795695	valid-auc:0.715997
[8]	train-auc:0.796571	valid-auc:0.717465
[9]	train-auc:0.799307	valid-auc:0.717479
[10]	train-auc:0.802216	valid-auc:0.718341
[11]	train-auc:0.804251	valid-auc:0.719143
[12]	train-auc:0.807719	valid-auc:0.719385
[13]	train-auc:0.809882	valid-auc:0.72012
[14]	train-auc:0.810474	valid-auc:0.720731
[15]	train-auc:0.812238	valid-auc:0.721938
[16]	train-auc:0.814804	valid-auc:0.723415
[17]	train-auc:0.816459	valid-auc:0.72392
[18]	train-auc:0.817834	valid-auc:0.723829
[19]	train-auc:0.819038	valid-auc:0.724036
[20]	train-auc:0.820708	valid-auc:0.724855
[21]	train-auc:0.822719	valid-auc:0.725673
[22]	train-auc:0.82399	valid-auc:0.726361
[23]	train-auc:0.824512	valid-auc:0.726554
[24]	train-auc:0.825901	valid-auc:0.727039
[25]	train-auc:0.828212	valid-auc:0.727678
[26]	train-auc:0.829908	valid-auc:0.728398
[27]	train-auc:0.830737	valid-auc:0.728913
[28]	train-auc:0.832616	valid-auc:0.729648
[29]	train-auc:0.834158	valid-auc:0.730002
[30]	train-auc:0.835915	valid-auc:0.730722
[31]	train-auc:0.83624	valid-auc:0.731073
[32]	train-auc:0.83824	valid-auc:0.731304
[33]	train-auc:0.839817	valid-auc:0.731602
[34]	train-auc:0.84106	valid-auc:0.731764
[35]	train-auc:0.842346	valid-auc:0.73215
[36]	train-auc:0.843724	valid-auc:0.732584
[37]	train-auc:0.845672	valid-auc:0.733031
[38]	train-auc:0.84671	valid-auc:0.733182
[39]	train-auc:0.847273	valid-auc:0.733469
[40]	train-auc:0.848512	valid-auc:0.734051
[41]	train-auc:0.849526	valid-auc:0.734183
[42]	train-auc:0.850369	valid-auc:0.734835
[43]	train-auc:0.851673	valid-auc:0.735313
[44]	train-auc:0.852481	valid-auc:0.735383
[45]	train-auc:0.852981	valid-auc:0.735505
[46]	train-auc:0.853317	valid-auc:0.735777
[47]	train-auc:0.854628	valid-auc:0.736145
[48]	train-auc:0.855426	valid-auc:0.736284
[49]	train-auc:0.855929	valid-auc:0.736444
[50]	train-auc:0.856755	valid-auc:0.736817
[51]	train-auc:0.857676	valid-auc:0.737009
[52]	train-auc:0.859025	valid-auc:0.737376
[53]	train-auc:0.86031	valid-auc:0.737613
[54]	train-auc:0.861635	valid-auc:0.738067
[55]	train-auc:0.862615	valid-auc:0.73881
[56]	train-auc:0.863437	valid-auc:0.739241
[57]	train-auc:0.864012	valid-auc:0.739499
[58]	train-auc:0.865026	valid-auc:0.73965
[59]	train-auc:0.86602	valid-auc:0.740062
[60]	train-auc:0.866484	valid-auc:0.740336
[61]	train-auc:0.867346	valid-auc:0.740577
[62]	train-auc:0.868106	valid-auc:0.740629
[63]	train-auc:0.868934	valid-auc:0.741134
[64]	train-auc:0.870031	valid-auc:0.741539
[65]	train-auc:0.870743	valid-auc:0.741761
[66]	train-auc:0.871661	valid-auc:0.741847
[67]	train-auc:0.872699	valid-auc:0.742117
[68]	train-auc:0.873136	valid-auc:0.742281
[69]	train-auc:0.873368	valid-auc:0.742319
[70]	train-auc:0.874509	valid-auc:0.74246
[71]	train-auc:0.87495	valid-auc:0.742472
[72]	train-auc:0.875913	valid-auc:0.742909
[73]	train-auc:0.876994	valid-auc:0.743078
[74]	train-auc:0.877546	valid-auc:0.743174
[75]	train-auc:0.878448	valid-auc:0.743499
[76]	train-auc:0.879188	valid-auc:0.74359
[77]	train-auc:0.879591	valid-auc:0.743774
[78]	train-auc:0.880062	valid-auc:0.743892
[79]	train-auc:0.880817	valid-auc:0.744173
[80]	train-auc:0.881099	valid-auc:0.744137
[81]	train-auc:0.881824	valid-auc:0.744509
[82]	train-auc:0.882727	valid-auc:0.744913
[83]	train-auc:0.883144	valid-auc:0.745078
[84]	train-auc:0.884145	valid-auc:0.74559
[85]	train-auc:0.884495	valid-auc:0.745628
[86]	train-auc:0.884955	valid-auc:0.745774
[87]	train-auc:0.885778	valid-auc:0.746164
[88]	train-auc:0.886101	valid-auc:0.746276
[89]	train-auc:0.886527	valid-auc:0.746191
[90]	train-auc:0.88672	valid-auc:0.746241
[91]	train-auc:0.886896	valid-auc:0.746254
[92]	train-auc:0.887312	valid-auc:0.746411
[93]	train-auc:0.887567	valid-auc:0.746468
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[430]	train-auc:0.97148	valid-auc:0.773311
[431]	train-auc:0.971581	valid-auc:0.773318
[432]	train-auc:0.971788	valid-auc:0.773398
[433]	train-auc:0.971965	valid-auc:0.773406
[434]	train-auc:0.971991	valid-auc:0.773319
[435]	train-auc:0.97209	valid-auc:0.773361
[436]	train-auc:0.972159	valid-auc:0.773432
[437]	train-auc:0.972352	valid-auc:0.773508
[438]	train-auc:0.972511	valid-auc:0.773585
[439]	train-auc:0.972584	valid-auc:0.773639
[440]	train-auc:0.972636	valid-auc:0.773663
[441]	train-auc:0.972679	valid-auc:0.773609
[442]	train-auc:0.972802	valid-auc:0.773558
[443]	train-auc:0.972891	valid-auc:0.773562
[444]	train-auc:0.972923	valid-auc:0.773619
[445]	train-auc:0.973183	valid-auc:0.773646
[446]	train-auc:0.973263	valid-auc:0.773651
[447]	train-auc:0.973401	valid-auc:0.773678
[448]	train-auc:0.973425	valid-auc:0.773648
[449]	train-auc:0.973468	valid-auc:0.773572
[450]	train-auc:0.973509	valid-auc:0.773539
[451]	train-auc:0.973601	valid-auc:0.7736
[452]	train-auc:0.973681	valid-auc:0.773643
[453]	train-auc:0.973762	valid-auc:0.773672
[454]	train-auc:0.973929	valid-auc:0.773854
[455]	train-auc:0.974014	valid-auc:0.773932
[456]	train-auc:0.974089	valid-auc:0.773972
[457]	train-auc:0.974122	valid-auc:0.774028
[458]	train-auc:0.974139	valid-auc:0.774043
[459]	train-auc:0.974186	valid-auc:0.774067
[460]	train-auc:0.974254	valid-auc:0.77416
[461]	train-auc:0.974307	valid-auc:0.774104
[462]	train-auc:0.974472	valid-auc:0.774116
[463]	train-auc:0.974539	valid-auc:0.774188
[464]	train-auc:0.974741	valid-auc:0.774234
[465]	train-auc:0.974813	valid-auc:0.774184
[466]	train-auc:0.974883	valid-auc:0.774254
[467]	train-auc:0.974901	valid-auc:0.774238
[468]	train-auc:0.974982	valid-auc:0.774257
[469]	train-auc:0.975087	valid-auc:0.77429
[470]	train-auc:0.975146	valid-auc:0.774346
[471]	train-auc:0.975346	valid-auc:0.774461
[472]	train-auc:0.975382	valid-auc:0.774512
[473]	train-auc:0.975469	valid-auc:0.774562
[474]	train-auc:0.975661	valid-auc:0.774619
[475]	train-auc:0.97575	valid-auc:0.774618
[476]	train-auc:0.975789	valid-auc:0.774629
[477]	train-auc:0.975879	valid-auc:0.774634
[478]	train-auc:0.976001	valid-auc:0.774736
[479]	train-auc:0.976085	valid-auc:0.774709
[480]	train-auc:0.976158	valid-auc:0.774712
[481]	train-auc:0.976197	valid-auc:0.77481
[482]	train-auc:0.976234	valid-auc:0.774765
[483]	train-auc:0.976284	valid-auc:0.77469
[484]	train-auc:0.976429	valid-auc:0.774682
[485]	train-auc:0.976475	valid-auc:0.774758
[486]	train-auc:0.97658	valid-auc:0.774745
[487]	train-auc:0.976648	valid-auc:0.774726
[488]	train-auc:0.976697	valid-auc:0.774802
[489]	train-auc:0.976756	valid-auc:0.774929
[490]	train-auc:0.976879	valid-auc:0.774994
[491]	train-auc:0.976933	valid-auc:0.775083
[492]	train-auc:0.976999	valid-auc:0.775115
[493]	train-auc:0.977079	valid-auc:0.775141
[494]	train-auc:0.977194	valid-auc:0.775209
[495]	train-auc:0.977261	valid-auc:0.775243
[496]	train-auc:0.977274	valid-auc:0.77525
[497]	train-auc:0.977345	valid-auc:0.77527
[498]	train-auc:0.977421	valid-auc:0.775353
[499]	train-auc:0.977569	valid-auc:0.775218
[500]	train-auc:0.977699	valid-auc:0.775254
[501]	train-auc:0.977695	valid-auc:0.775276
[502]	train-auc:0.977749	valid-auc:0.775253
[503]	train-auc:0.977818	valid-auc:0.775276
[504]	train-auc:0.977876	valid-auc:0.775348
[505]	train-auc:0.977883	valid-auc:0.775368
[506]	train-auc:0.977931	valid-auc:0.775414
[507]	train-auc:0.978004	valid-auc:0.775416
[508]	train-auc:0.978034	valid-auc:0.775483
[509]	train-auc:0.978076	valid-auc:0.775544
[510]	train-auc:0.978132	valid-auc:0.775559
[511]	train-auc:0.978256	valid-auc:0.77566
[512]	train-auc:0.978389	valid-auc:0.775795
[513]	train-auc:0.978461	valid-auc:0.775816
[514]	train-auc:0.978551	valid-auc:0.775935
[515]	train-auc:0.978606	valid-auc:0.775985
[516]	train-auc:0.978679	valid-auc:0.776001
[517]	train-auc:0.978712	valid-auc:0.775982
[518]	train-auc:0.978836	valid-auc:0.775959
[519]	train-auc:0.97888	valid-auc:0.775957
[520]	train-auc:0.979	valid-auc:0.7759
[521]	train-auc:0.9791	valid-auc:0.775869
[522]	train-auc:0.979136	valid-auc:0.775935
[523]	train-auc:0.979175	valid-auc:0.775951
[524]	train-auc:0.979255	valid-auc:0.776052
[525]	train-auc:0.979277	valid-auc:0.776085
[526]	train-auc:0.979306	valid-auc:0.776056
[527]	train-auc:0.979365	valid-auc:0.776131
[528]	train-auc:0.97945	valid-auc:0.776233
[529]	train-auc:0.979499	valid-auc:0.776217
[530]	train-auc:0.979516	valid-auc:0.77614
[531]	train-auc:0.979599	valid-auc:0.776201
[532]	train-auc:0.979612	valid-auc:0.776222
[533]	train-auc:0.979658	valid-auc:0.77629
[534]	train-auc:0.979754	valid-auc:0.776401
[535]	train-auc:0.979878	valid-auc:0.7765
[536]	train-auc:0.979906	valid-auc:0.776483
[537]	train-auc:0.97993	valid-auc:0.776515
[538]	train-auc:0.979989	valid-auc:0.776624
[539]	train-auc:0.980022	valid-auc:0.776668
[540]	train-auc:0.980059	valid-auc:0.776662
[541]	train-auc:0.980231	valid-auc:0.776785
[542]	train-auc:0.980239	valid-auc:0.776802
[543]	train-auc:0.980363	valid-auc:0.776802
[544]	train-auc:0.980401	valid-auc:0.776841
[545]	train-auc:0.980443	valid-auc:0.776883
[546]	train-auc:0.980457	valid-auc:0.776881
[547]	train-auc:0.980486	valid-auc:0.776919
[548]	train-auc:0.980521	valid-auc:0.77693
[549]	train-auc:0.980555	valid-auc:0.776896
[550]	train-auc:0.980632	valid-auc:0.776875
[551]	train-auc:0.980638	valid-auc:0.77693
[552]	train-auc:0.980727	valid-auc:0.777021
[553]	train-auc:0.980788	valid-auc:0.77701
[554]	train-auc:0.980837	valid-auc:0.777107
[555]	train-auc:0.980854	valid-auc:0.777066
[556]	train-auc:0.980928	valid-auc:0.777062
[557]	train-auc:0.980947	valid-auc:0.77708
[558]	train-auc:0.98101	valid-auc:0.77712
[559]	train-auc:0.981078	valid-auc:0.777126
[560]	train-auc:0.981111	valid-auc:0.777132
[561]	train-auc:0.981169	valid-auc:0.777168
[562]	train-auc:0.981259	valid-auc:0.777238
[563]	train-auc:0.981341	valid-auc:0.777304
[564]	train-auc:0.981369	valid-auc:0.777342
[565]	train-auc:0.981431	valid-auc:0.777332
[566]	train-auc:0.98151	valid-auc:0.777277
[567]	train-auc:0.981559	valid-auc:0.77729
[568]	train-auc:0.981607	valid-auc:0.777287
[569]	train-auc:0.981622	valid-auc:0.777284
[570]	train-auc:0.981637	valid-auc:0.777244
[571]	train-auc:0.981701	valid-auc:0.777267
[572]	train-auc:0.981718	valid-auc:0.777221
[573]	train-auc:0.98177	valid-auc:0.777243
[574]	train-auc:0.981866	valid-auc:0.777267
[575]	train-auc:0.981901	valid-auc:0.777305
[576]	train-auc:0.981967	valid-auc:0.777333
[577]	train-auc:0.982051	valid-auc:0.777265
[578]	train-auc:0.982141	valid-auc:0.777245
[579]	train-auc:0.982218	valid-auc:0.777217
[580]	train-auc:0.982237	valid-auc:0.777248
[581]	train-auc:0.982264	valid-auc:0.777264
[582]	train-auc:0.982347	valid-auc:0.777367
[583]	train-auc:0.98242	valid-auc:0.777471
[584]	train-auc:0.982473	valid-auc:0.777528
[585]	train-auc:0.982499	valid-auc:0.777554
[586]	train-auc:0.982582	valid-auc:0.777629
[587]	train-auc:0.982606	valid-auc:0.777621
[588]	train-auc:0.98263	valid-auc:0.777561
[589]	train-auc:0.982729	valid-auc:0.777604
[590]	train-auc:0.982738	valid-auc:0.777656
[591]	train-auc:0.982768	valid-auc:0.777692
[592]	train-auc:0.982814	valid-auc:0.777698
[593]	train-auc:0.982844	valid-auc:0.777698
[594]	train-auc:0.982882	valid-auc:0.7777
[595]	train-auc:0.982906	valid-auc:0.77776
[596]	train-auc:0.982962	valid-auc:0.77772
[597]	train-auc:0.983055	valid-auc:0.777776
[598]	train-auc:0.983087	valid-auc:0.777727
[599]	train-auc:0.983104	valid-auc:0.777755
[600]	train-auc:0.983131	valid-auc:0.777773
[601]	train-auc:0.983218	valid-auc:0.777731
[602]	train-auc:0.983247	valid-auc:0.777721
[603]	train-auc:0.983318	valid-auc:0.777773
[604]	train-auc:0.983351	valid-auc:0.777837
[605]	train-auc:0.983428	valid-auc:0.777921
[606]	train-auc:0.983475	valid-auc:0.778067
[607]	train-auc:0.983551	valid-auc:0.778001
[608]	train-auc:0.98359	valid-auc:0.777938
[609]	train-auc:0.983696	valid-auc:0.777985
[610]	train-auc:0.983729	valid-auc:0.777997
[611]	train-auc:0.983743	valid-auc:0.777949
[612]	train-auc:0.983768	valid-auc:0.777966
[613]	train-auc:0.983782	valid-auc:0.777951
[614]	train-auc:0.983814	valid-auc:0.777895
[615]	train-auc:0.983901	valid-auc:0.777913
[616]	train-auc:0.983952	valid-auc:0.777993
[617]	train-auc:0.983993	valid-auc:0.778007
[618]	train-auc:0.984044	valid-auc:0.777999
[619]	train-auc:0.98409	valid-auc:0.778082
[620]	train-auc:0.984125	valid-auc:0.778103
[621]	train-auc:0.984165	valid-auc:0.778143
[622]	train-auc:0.984211	valid-auc:0.778184
[623]	train-auc:0.984221	valid-auc:0.778202
[624]	train-auc:0.984258	valid-auc:0.778202
[625]	train-auc:0.98429	valid-auc:0.77823
[626]	train-auc:0.984329	valid-auc:0.778221
[627]	train-auc:0.984385	valid-auc:0.778171
[628]	train-auc:0.984418	valid-auc:0.778139
[629]	train-auc:0.984451	valid-auc:0.77811
[630]	train-auc:0.984465	valid-auc:0.77811
[631]	train-auc:0.984532	valid-auc:0.778166
[632]	train-auc:0.9846	valid-auc:0.778162
[633]	train-auc:0.984621	valid-auc:0.778204
[634]	train-auc:0.984657	valid-auc:0.778176
[635]	train-auc:0.984753	valid-auc:0.778114
[636]	train-auc:0.984772	valid-auc:0.778112
[637]	train-auc:0.98481	valid-auc:0.778105
[638]	train-auc:0.984869	valid-auc:0.778106
[639]	train-auc:0.984889	valid-auc:0.778122
[640]	train-auc:0.984923	valid-auc:0.778077
[641]	train-auc:0.984963	valid-auc:0.778068
[642]	train-auc:0.985007	valid-auc:0.777983
[643]	train-auc:0.985039	valid-auc:0.777913
[644]	train-auc:0.985065	valid-auc:0.777918
[645]	train-auc:0.985118	valid-auc:0.777942
[646]	train-auc:0.985157	valid-auc:0.777936
[647]	train-auc:0.985247	valid-auc:0.778025
[648]	train-auc:0.985276	valid-auc:0.777994
[649]	train-auc:0.985287	valid-auc:0.777998
[650]	train-auc:0.985305	valid-auc:0.777994
[651]	train-auc:0.98534	valid-auc:0.777999
[652]	train-auc:0.985398	valid-auc:0.777956
[653]	train-auc:0.985427	valid-auc:0.777985
[654]	train-auc:0.985475	valid-auc:0.777987
[655]	train-auc:0.985528	valid-auc:0.777977
[656]	train-auc:0.985605	valid-auc:0.777976
[657]	train-auc:0.985619	valid-auc:0.778021
[658]	train-auc:0.98564	valid-auc:0.778035
[659]	train-auc:0.985717	valid-auc:0.778042
[660]	train-auc:0.985762	valid-auc:0.778062
[661]	train-auc:0.985783	valid-auc:0.778107
[662]	train-auc:0.985839	valid-auc:0.778104
[663]	train-auc:0.985918	valid-auc:0.778039
[664]	train-auc:0.98598	valid-auc:0.778018
[665]	train-auc:0.985999	valid-auc:0.777976
[666]	train-auc:0.986046	valid-auc:0.777986
[667]	train-auc:0.986098	valid-auc:0.77798
[668]	train-auc:0.986145	valid-auc:0.777977
[669]	train-auc:0.986215	valid-auc:0.777991
[670]	train-auc:0.986265	valid-auc:0.777976
[671]	train-auc:0.986268	valid-auc:0.777974
[672]	train-auc:0.986302	valid-auc:0.778024
[673]	train-auc:0.986333	valid-auc:0.777999
[674]	train-auc:0.986386	valid-auc:0.778044
[675]	train-auc:0.986424	valid-auc:0.778095
Stopping. Best iteration:
[625]	train-auc:0.98429	valid-auc:0.77823

[mlcrate] Finished training fold 6 - took 1m15s - running score 0.776734
[mlcrate] Finished training 7 XGBoost models, took 17m32s
[mlcrate] Overall feature importances: [('trainee_id', 596416), ('age', 212316), ('test_id', 193748), ('avg_prog_dur', 92361), ('col_2', 68374), ('col_8', 67540), ('col_1', 58319), ('city_tier', 57974), ('col_9', 57079), ('col_6', 51011), ('col_5', 50159), ('col_4', 49314), ('col_3', 42165), ('col_0', 40328), ('total_programs_enrolled', 40141), ('col_7', 38553), ('program_duration', 35709), ('is_age_39_45', 10727), ('is_age_39', 2910), ('is_age_45', 2194)]

In [53]:

    

imps


    

    
Out[53]:





defaultdict(int,
            {'age': 212316,
             'avg_prog_dur': 92361,
             'city_tier': 57974,
             'col_0': 40328,
             'col_1': 58319,
             'col_2': 68374,
             'col_3': 42165,
             'col_4': 49314,
             'col_5': 50159,
             'col_6': 51011,
             'col_7': 38553,
             'col_8': 67540,
             'col_9': 57079,
             'is_age_39': 2910,
             'is_age_39_45': 10727,
             'is_age_45': 2194,
             'program_duration': 35709,
             'test_id': 193748,
             'total_programs_enrolled': 40141,
             'trainee_id': 596416})

In [54]:

    

p_train.shape


    

    
Out[54]:





(73147,)

In [55]:

    

p_test.shape


    

    
Out[55]:





(31349,)

In [56]:

    

def make_submission(probs):
    sample = pd.read_csv(f'{PATH}\\AV_Stud\\sample_submission_vaSxamm.csv')
    submit = sample.copy()
    submit['is_pass'] = probs
    return submit


    

In [57]:

    

#submit = make_submission(prediction_proba[:,1]);
submit = make_submission(p_test)


    

In [58]:

    

submit.head(2)


    

    
Out[58]:







  

    

      

      
id
      
is_pass
    
  
  

    

      
0
      
1626_45
      
0.265089
    
    

      
1
      
11020_130
      
0.933593
    
  

In [59]:

    

submit.to_csv(f'{PATH}\\AV_Stud\\08_06_18_v2.csv', index=False)


    

In [ ]:

    




    

In [6]:

    

X_train, X_validation, y_train, y_validation = train_test_split(df_raw, target, train_size=0.8, random_state=1234)


    

In [7]:

    

params = {'boosting_type': 'gbdt',
          'max_depth' : 11,
          'objective': 'binary', 
          'num_leaves': 64, 
          'learning_rate': 0.05, 
          'max_bin': 512, 
          'subsample': 0.9, 
          'subsample_freq': 1, 
          'colsample_bytree': 0.9, 
          'min_split_gain': 0.5,  
          'scale_pos_weight': 0.4380049934141978,
          'metric' : 'log_loss'}


    

In [8]:

    

d_train = lgb.Dataset(X_train, y_train)
d_valid = lgb.Dataset(X_validation, y_validation)
watchlist = [d_train, d_valid]


    

In [9]:

    

model = lgb.train(params, train_set=d_train, num_boost_round=2000)


    

In [10]:

    

preds = model.predict(df_test)


    

In [11]:

    

lgb.plot_importance(model)


    

    
Out[11]:





<matplotlib.axes._subplots.AxesSubplot at 0x2bff6c8e160>






    

In [ ]:

    




    

In [ ]:

    




    

In [ ]:

    




    

In [26]:

    

df1 = pd.read_csv(f'{PATH}\\AV_Stud\\imp_subs\\01_05_18.csv')
df2 = submit.copy()


    

In [27]:

    

col = 'is_pass'
new_preds  = (df1[col] + df2[col])/2.


    

In [28]:

    

submit = make_submission(new_preds)


    

In [29]:

    

submit.head(2)


    

    
Out[29]:







  

    

      

      
id
      
is_pass
    
  
  

    

      
0
      
1626_45
      
0.340114
    
    

      
1
      
11020_130
      
0.930512
    
  

In [30]:

    

submit.to_csv(f'{PATH}\\AV_Stud\\01_05_18+02_05_18.csv', index=False)


    

In [31]:

    

df_raw['is_pass'] = target
df_raw.to_csv(f'{PATH}\\AV_Stud\\01_05_18_category_encoder_df_train.csv', index=False)
df_test.to_csv(f'{PATH}\\AV_Stud\\01_05_18_category_encoder_df_test.csv', index=False)
df_raw = df_raw.drop('is_pass', axis=1)


    

In [35]:

    

c = 0
for i in model:
    i.save_model(f'{PATH}\\AV_Stud\\xgb_{c}'.format(c))
    c+=1