av_student_datafest_embed



In [31]:
%load_ext autoreload
%autoreload 2
%matplotlib inline


The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

In [32]:
import time, h5py
import xgboost as xgb
import lightgbm as lgb
import category_encoders as cat_ed
import gc, mlcrate, shap

from fastai.imports import *
from fastai.structured import *
from gplearn.genetic import SymbolicTransformer
from pandas_summary import DataFrameSummary
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from IPython.display import display
from catboost import CatBoostClassifier
from collections import Counter
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 KFold, StratifiedKFold
from sklearn.preprocessing import LabelEncoder
from collections import Counter

from keras.models import Sequential
from keras.layers.core import Dense, Activation, Reshape
from keras.layers import Merge
from keras.layers.embeddings import Embedding
from keras.callbacks import ModelCheckpoint

# will ignore all warning from sklearn, seaborn etc..
def ignore_warn(*args, **kwargs):
    pass
warnings.warn = ignore_warn

pd.option_context("display.max_rows", 1000);
pd.option_context("display.max_columns", 250);

In [3]:
PATH = os.getcwd();
PATH


Out[3]:
'D:\\Github\\fastai\\courses\\ml1'

In [4]:
df_raw  = pd.read_csv(f'{PATH}\\AV_Stud\\models\\train_140618.csv', low_memory= False)
df_test = pd.read_csv(f'{PATH}\\AV_Stud\\models\\test_140618.csv', low_memory=False)

In [5]:
target = df_raw.is_pass.values
df_raw.drop(['is_pass'],axis=1, inplace=True)

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

for dtype, feature in zip(df_raw.dtypes, df_raw.columns):
    if dtype == object:
        categorical_features.append(feature)
    else:
        numeric_features.append(feature)
        
categorical_features;

X_train_cats = df_raw[categorical_features] 
X_test_cats  = df_test[categorical_features]

In [6]:
X_train_dnn = X_train_cats.values
X_test_dnn = X_test_cats.values

idx = X_train_cats.shape[0]; idx


Out[6]:
73147

In [7]:
data = pd.concat([X_train_cats, X_test_cats], axis = 0)
data.shape, idx


Out[7]:
((104496, 14), 73147)

In [8]:
features_idx = [i for i in range(14)];
features_idx


Out[8]:
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]

In [9]:
les = []
for i in range(X_train_dnn.shape[1]):
    le = LabelEncoder()
    le.fit(data.iloc[:,features_idx].iloc[:, i])
    les.append(le)
    X_train_dnn[:, i] = le.transform(X_train_dnn[:, i])
    X_test_dnn[:, i]  = le.transform(X_test_dnn[:, i])

In [10]:
def split_features(X):
    
    X_list = []
    
    C1 = X[..., [0]]
    X_list.append(C1)
    
    program_type = X[..., [1]]
    X_list.append(program_type)
    
    test_type = X[..., [2]]
    X_list.append(test_type)
    
    difficulty_level = X[..., [3]]
    X_list.append(difficulty_level)
    
    gender = X[..., [4]]
    X_list.append(gender)
    
    education = X[..., [5]]
    X_list.append(education)
    
    is_handicapped = X[..., [6]]
    X_list.append(is_handicapped)
    
    program_type__program_duration = X[..., [7]]
    X_list.append(program_type__program_duration)
    
    test_id__program_duration = X[..., [8]]
    X_list.append(test_id__program_duration)
    
    test_id__test_type = X[..., [9]]
    X_list.append(test_id__test_type)
    
    test_type__difficulty_level = X[..., [10]]
    X_list.append(test_type__difficulty_level)
    
    education__gender = X[..., [11]]
    X_list.append(education__gender)
    
    C14 = X[..., [12]]#education__city_tier
    X_list.append(C14)
    
    C15 = X[..., [13]]#gender__city_tier
    X_list.append(C15)
    
    return X_list

In [11]:
for i in X_train_cats.columns:
    print(i, len(np.unique(df_raw[i].values)))


program_id 22
program_type 7
test_type 2
difficulty_level 4
gender 2
education 5
is_handicapped 2
program_type__program_duration 22
test_id__program_duration 188
test_id__test_type 188
test_type__difficulty_level 5
education__gender 10
education__city_tier 19
gender__city_tier 8

In [12]:
X_train, X_validation, y_train, y_validation = train_test_split(X_train_dnn, target, train_size=0.8, random_state=1234)

In [16]:
class NN_with_EntityEmbedding(object):
    
    def __init__(self, X_train, y_train, X_val, y_val):
        self.nb_epoch = 10
        self.__build_keras_model()
        self.fit(X_train, y_train, X_val, y_val)
        
    def preprocessing(self, X):
        X_list = split_features(X)
        return X_list
    
    def __build_keras_model(self):
        
        '''
        program_id 22
        program_type 7
        test_type 2
        difficulty_level 4
        gender 2
        education 5
        is_handicapped 2
        program_type__program_duration 22
        test_id__program_duration 188
        test_id__test_type 188
        test_type__difficulty_level 5
        education__gender 10
        education__city_tier 19
        gender__city_tier 8
       '''
        models = []
        model_C1= Sequential() #program_id
        model_C1.add(Embedding(len(les[0].classes_), 22, input_length=1))
        model_C1.add(Reshape(target_shape=(22,)))
        models.append(model_C1)
        
        model_program_type = Sequential()
        model_program_type.add(Embedding(len(les[1].classes_), 7, input_length=1))
        model_program_type.add(Reshape(target_shape=(7,)))
        models.append(model_program_type)
        
        model_test_type = Sequential()
        model_test_type.add(Embedding(len(les[2].classes_), 3, input_length=1))
        model_test_type.add(Reshape(target_shape=(3,)))
        models.append(model_test_type)
        
        difficulty_level = Sequential()
        difficulty_level.add(Embedding(len(les[3].classes_), 5, input_length=1))
        difficulty_level.add(Reshape(target_shape=(5,)))
        models.append(difficulty_level)
        
        gender = Sequential()
        gender.add(Embedding(len(les[4].classes_), 3, input_length=1))
        gender.add(Reshape(target_shape=(3,)))
        models.append(gender)
        
        education = Sequential()
        education.add(Embedding(len(les[5].classes_), 6, input_length=1))
        education.add(Reshape(target_shape=(6,)))
        models.append(education)
        
        is_handicapped = Sequential()
        is_handicapped.add(Embedding(len(les[6].classes_), 3, input_length=1))
        is_handicapped.add(Reshape(target_shape=(3,)))
        models.append(is_handicapped)
        
        program_type__program_duration = Sequential()
        program_type__program_duration.add(Embedding(len(les[7].classes_), 23, input_length=1))
        program_type__program_duration.add(Reshape(target_shape=(23,)))
        models.append(program_type__program_duration)
        
        test_id__program_duration = Sequential()
        test_id__program_duration.add(Embedding(len(les[8].classes_), 30, input_length=1))
        test_id__program_duration.add(Reshape(target_shape=(30,)))
        models.append(test_id__program_duration)
        
        test_id__test_type = Sequential()
        test_id__test_type.add(Embedding(len(les[9].classes_), 30, input_length=1))
        test_id__test_type.add(Reshape(target_shape=(30,)))
        models.append(test_id__test_type)
        
        test_type__difficulty_level = Sequential()
        test_type__difficulty_level.add(Embedding(len(les[10].classes_), 6, input_length=1))
        test_type__difficulty_level.add(Reshape(target_shape=(6,)))
        models.append(test_type__difficulty_level)
        
        education__gender = Sequential()
        education__gender.add(Embedding(len(les[11].classes_), 10, input_length=1))
        education__gender.add(Reshape(target_shape=(10,)))
        models.append(education__gender)
        
        C14 = Sequential()#education__city_tier'
        C14.add(Embedding(len(les[12].classes_), 20, input_length=1))
        C14.add(Reshape(target_shape=(20,)))
        models.append(C14)
        
        C15 = Sequential()#gender__city_tier
        C15.add(Embedding(len(les[13].classes_), 9, input_length=1))
        C15.add(Reshape(target_shape=(9,)))
        models.append(C15)
        
        self.model = Sequential()
        self.model.add(Merge(models, mode='concat'))
        self.model.add(Dense(128, kernel_initializer='uniform'))
        self.model.add(Activation('relu'))
        
        self.model.add(Dense(256, kernel_initializer='uniform'))
        self.model.add(Activation('relu'))
        
        self.model.add(Dense(64, kernel_initializer='uniform'))
        self.model.add(Activation('relu'))
        self.model.add(Dense(1))
        self.model.add(Activation('sigmoid'))
        self.model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['acc'])
        
    def fit(self, X_train, y_train, X_val, y_val):
        self.model.fit(self.preprocessing(X_train), y_train,validation_data=(self.preprocessing(X_val), y_val),\
                       epochs=self.nb_epoch, batch_size=64)

In [17]:
#X_train, X_validation, y_train, y_validation
dnn = NN_with_EntityEmbedding(X_train, y_train, X_validation, y_validation)
weights = dnn.model.get_weights()


Train on 58517 samples, validate on 14630 samples
Epoch 1/10
52096/58517 [=========================>....] - ETA: 633s - loss: 0.6933 - acc: 0.421 - ETA: 137s - loss: 0.6906 - acc: 0.621 - ETA: 81s - loss: 0.6841 - acc: 0.651 - ETA: 59s - loss: 0.6743 - acc: 0.67 - ETA: 48s - loss: 0.6595 - acc: 0.67 - ETA: 41s - loss: 0.6587 - acc: 0.67 - ETA: 36s - loss: 0.6403 - acc: 0.68 - ETA: 33s - loss: 0.6359 - acc: 0.68 - ETA: 30s - loss: 0.6288 - acc: 0.68 - ETA: 28s - loss: 0.6195 - acc: 0.69 - ETA: 26s - loss: 0.6208 - acc: 0.68 - ETA: 25s - loss: 0.6192 - acc: 0.69 - ETA: 23s - loss: 0.6163 - acc: 0.69 - ETA: 22s - loss: 0.6155 - acc: 0.68 - ETA: 21s - loss: 0.6128 - acc: 0.69 - ETA: 20s - loss: 0.6099 - acc: 0.69 - ETA: 20s - loss: 0.6105 - acc: 0.69 - ETA: 19s - loss: 0.6067 - acc: 0.69 - ETA: 19s - loss: 0.6058 - acc: 0.69 - ETA: 18s - loss: 0.6052 - acc: 0.69 - ETA: 18s - loss: 0.6021 - acc: 0.69 - ETA: 17s - loss: 0.6030 - acc: 0.69 - ETA: 17s - loss: 0.6023 - acc: 0.69 - ETA: 17s - loss: 0.6036 - acc: 0.68 - ETA: 16s - loss: 0.6036 - acc: 0.68 - ETA: 16s - loss: 0.6036 - acc: 0.68 - ETA: 16s - loss: 0.6026 - acc: 0.68 - ETA: 15s - loss: 0.6028 - acc: 0.68 - ETA: 15s - loss: 0.6020 - acc: 0.68 - ETA: 15s - loss: 0.6008 - acc: 0.68 - ETA: 15s - loss: 0.5995 - acc: 0.68 - ETA: 14s - loss: 0.5983 - acc: 0.68 - ETA: 14s - loss: 0.5980 - acc: 0.68 - ETA: 14s - loss: 0.5963 - acc: 0.68 - ETA: 14s - loss: 0.5943 - acc: 0.69 - ETA: 14s - loss: 0.5943 - acc: 0.68 - ETA: 13s - loss: 0.5939 - acc: 0.69 - ETA: 13s - loss: 0.5929 - acc: 0.69 - ETA: 13s - loss: 0.5915 - acc: 0.69 - ETA: 13s - loss: 0.5915 - acc: 0.69 - ETA: 13s - loss: 0.5900 - acc: 0.69 - ETA: 13s - loss: 0.5895 - acc: 0.69 - ETA: 12s - loss: 0.5897 - acc: 0.69 - ETA: 12s - loss: 0.5893 - acc: 0.69 - ETA: 12s - loss: 0.5893 - acc: 0.69 - ETA: 12s - loss: 0.5884 - acc: 0.69 - ETA: 12s - loss: 0.5881 - acc: 0.69 - ETA: 12s - loss: 0.5877 - acc: 0.69 - ETA: 12s - loss: 0.5860 - acc: 0.69 - ETA: 11s - loss: 0.5852 - acc: 0.69 - ETA: 11s - loss: 0.5847 - acc: 0.69 - ETA: 11s - loss: 0.5841 - 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acc: 0.701 - ETA: 1s - loss: 0.5707 - acc: 0.701 - ETA: 1s - loss: 0.5706 - acc: 0.701558517/58517 [==============================] - ETA: 1s - loss: 0.5707 - acc: 0.701 - ETA: 1s - loss: 0.5704 - acc: 0.701 - ETA: 1s - loss: 0.5703 - acc: 0.701 - ETA: 1s - loss: 0.5702 - acc: 0.701 - ETA: 1s - loss: 0.5703 - acc: 0.701 - ETA: 1s - loss: 0.5704 - acc: 0.701 - ETA: 1s - loss: 0.5705 - acc: 0.701 - ETA: 1s - loss: 0.5703 - acc: 0.701 - ETA: 1s - loss: 0.5703 - acc: 0.701 - ETA: 0s - loss: 0.5701 - acc: 0.702 - ETA: 0s - loss: 0.5700 - acc: 0.702 - ETA: 0s - loss: 0.5703 - acc: 0.701 - ETA: 0s - loss: 0.5703 - acc: 0.701 - ETA: 0s - loss: 0.5703 - acc: 0.701 - ETA: 0s - loss: 0.5703 - acc: 0.701 - ETA: 0s - loss: 0.5703 - acc: 0.701 - ETA: 0s - loss: 0.5704 - acc: 0.701 - ETA: 0s - loss: 0.5704 - acc: 0.702 - ETA: 0s - loss: 0.5704 - acc: 0.702 - ETA: 0s - loss: 0.5704 - acc: 0.702 - ETA: 0s - loss: 0.5702 - acc: 0.702 - ETA: 0s - loss: 0.5701 - acc: 0.702 - ETA: 0s - loss: 0.5702 - acc: 0.702 - ETA: 0s - loss: 0.5701 - acc: 0.702 - ETA: 0s - loss: 0.5701 - acc: 0.702 - ETA: 0s - loss: 0.5699 - acc: 0.702 - 15s - loss: 0.5699 - acc: 0.7025 - val_loss: 0.5633 - val_acc: 0.7102
Epoch 2/10
50496/58517 [========================>.....] - ETA: 14s - loss: 0.6210 - acc: 0.64 - ETA: 13s - loss: 0.5645 - acc: 0.71 - ETA: 14s - loss: 0.5589 - acc: 0.72 - ETA: 14s - loss: 0.5600 - acc: 0.71 - ETA: 14s - loss: 0.5598 - acc: 0.71 - ETA: 14s - loss: 0.5631 - acc: 0.71 - ETA: 14s - loss: 0.5637 - acc: 0.71 - ETA: 14s - loss: 0.5628 - acc: 0.71 - ETA: 14s - loss: 0.5645 - acc: 0.71 - ETA: 14s - loss: 0.5713 - acc: 0.70 - ETA: 14s - loss: 0.5734 - acc: 0.70 - ETA: 13s - loss: 0.5705 - acc: 0.70 - ETA: 13s - loss: 0.5737 - acc: 0.70 - ETA: 13s - loss: 0.5720 - acc: 0.70 - ETA: 13s - loss: 0.5669 - acc: 0.71 - ETA: 13s - loss: 0.5668 - acc: 0.71 - ETA: 13s - loss: 0.5658 - acc: 0.71 - ETA: 13s - loss: 0.5656 - acc: 0.71 - ETA: 13s - loss: 0.5656 - acc: 0.71 - ETA: 13s - loss: 0.5653 - acc: 0.70 - ETA: 13s - loss: 0.5649 - acc: 0.71 - ETA: 13s - loss: 0.5630 - acc: 0.71 - ETA: 13s - loss: 0.5630 - acc: 0.71 - ETA: 13s - loss: 0.5632 - acc: 0.70 - ETA: 13s - loss: 0.5630 - acc: 0.71 - ETA: 13s - loss: 0.5628 - acc: 0.71 - ETA: 12s - loss: 0.5621 - acc: 0.71 - ETA: 12s - loss: 0.5629 - acc: 0.71 - ETA: 12s - loss: 0.5637 - acc: 0.70 - ETA: 12s - loss: 0.5641 - acc: 0.70 - ETA: 12s - loss: 0.5628 - acc: 0.71 - ETA: 12s - loss: 0.5630 - acc: 0.70 - ETA: 12s - loss: 0.5639 - acc: 0.70 - ETA: 12s - loss: 0.5641 - acc: 0.70 - ETA: 12s - loss: 0.5639 - acc: 0.70 - ETA: 12s - loss: 0.5644 - acc: 0.70 - ETA: 12s - loss: 0.5644 - acc: 0.70 - ETA: 12s - loss: 0.5640 - acc: 0.70 - ETA: 12s - loss: 0.5646 - acc: 0.70 - ETA: 12s - loss: 0.5651 - acc: 0.70 - ETA: 12s - loss: 0.5650 - acc: 0.70 - ETA: 12s - loss: 0.5656 - acc: 0.70 - ETA: 11s - loss: 0.5665 - acc: 0.70 - ETA: 11s - loss: 0.5659 - acc: 0.70 - ETA: 11s - loss: 0.5651 - acc: 0.70 - ETA: 11s - loss: 0.5646 - acc: 0.70 - ETA: 11s - loss: 0.5646 - acc: 0.70 - ETA: 11s - loss: 0.5649 - acc: 0.70 - ETA: 11s - loss: 0.5647 - acc: 0.70 - ETA: 11s - loss: 0.5653 - acc: 0.70 - ETA: 11s - loss: 0.5645 - acc: 0.70 - ETA: 11s - loss: 0.5641 - acc: 0.70 - ETA: 11s - loss: 0.5650 - acc: 0.70 - ETA: 11s - loss: 0.5647 - acc: 0.70 - ETA: 11s - loss: 0.5655 - acc: 0.70 - ETA: 11s - loss: 0.5655 - acc: 0.70 - ETA: 11s - loss: 0.5658 - acc: 0.70 - ETA: 11s - loss: 0.5664 - acc: 0.70 - ETA: 11s - loss: 0.5665 - acc: 0.70 - ETA: 10s - loss: 0.5668 - acc: 0.70 - ETA: 10s - loss: 0.5675 - acc: 0.70 - ETA: 10s - loss: 0.5675 - acc: 0.70 - ETA: 10s - loss: 0.5675 - acc: 0.70 - ETA: 10s - loss: 0.5675 - acc: 0.70 - ETA: 10s - loss: 0.5667 - acc: 0.70 - ETA: 10s - loss: 0.5664 - acc: 0.70 - ETA: 10s - loss: 0.5669 - acc: 0.70 - ETA: 10s - loss: 0.5661 - acc: 0.70 - ETA: 10s - loss: 0.5658 - acc: 0.70 - ETA: 10s - loss: 0.5660 - acc: 0.70 - ETA: 10s - loss: 0.5670 - acc: 0.70 - ETA: 10s - loss: 0.5672 - acc: 0.70 - ETA: 10s - loss: 0.5670 - acc: 0.70 - ETA: 10s - loss: 0.5669 - acc: 0.70 - ETA: 9s - loss: 0.5675 - acc: 0.7037 - ETA: 9s - loss: 0.5673 - acc: 0.703 - ETA: 9s - loss: 0.5676 - acc: 0.703 - ETA: 9s - loss: 0.5678 - acc: 0.703 - 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acc: 0.706 - ETA: 5s - loss: 0.5633 - acc: 0.707 - ETA: 5s - loss: 0.5633 - acc: 0.707 - ETA: 5s - loss: 0.5634 - acc: 0.707 - ETA: 5s - loss: 0.5634 - acc: 0.707 - ETA: 5s - loss: 0.5633 - acc: 0.707 - ETA: 4s - loss: 0.5631 - acc: 0.707 - ETA: 4s - loss: 0.5634 - acc: 0.707 - ETA: 4s - loss: 0.5634 - acc: 0.707 - ETA: 4s - loss: 0.5632 - acc: 0.707 - ETA: 4s - loss: 0.5633 - acc: 0.707 - ETA: 4s - loss: 0.5631 - acc: 0.707 - ETA: 4s - loss: 0.5629 - acc: 0.707 - ETA: 4s - loss: 0.5628 - acc: 0.707 - ETA: 4s - loss: 0.5629 - acc: 0.707 - ETA: 4s - loss: 0.5629 - acc: 0.707 - ETA: 4s - loss: 0.5629 - acc: 0.707 - ETA: 4s - loss: 0.5628 - acc: 0.707 - ETA: 4s - loss: 0.5628 - acc: 0.707 - ETA: 4s - loss: 0.5626 - acc: 0.707 - ETA: 4s - loss: 0.5623 - acc: 0.708 - ETA: 4s - loss: 0.5620 - acc: 0.708 - ETA: 4s - loss: 0.5619 - acc: 0.708 - ETA: 3s - loss: 0.5617 - acc: 0.708 - ETA: 3s - loss: 0.5617 - acc: 0.708 - ETA: 3s - loss: 0.5615 - acc: 0.708 - ETA: 3s - loss: 0.5615 - acc: 0.708 - 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acc: 0.708 - ETA: 2s - loss: 0.5613 - acc: 0.709 - ETA: 2s - loss: 0.5612 - acc: 0.708 - ETA: 2s - loss: 0.5612 - acc: 0.709 - ETA: 1s - loss: 0.5612 - acc: 0.708958517/58517 [==============================] - ETA: 1s - loss: 0.5613 - acc: 0.708 - ETA: 1s - loss: 0.5615 - acc: 0.708 - ETA: 1s - loss: 0.5615 - acc: 0.708 - ETA: 1s - loss: 0.5615 - acc: 0.708 - ETA: 1s - loss: 0.5617 - acc: 0.708 - ETA: 1s - loss: 0.5618 - acc: 0.708 - ETA: 1s - loss: 0.5617 - acc: 0.708 - ETA: 1s - loss: 0.5616 - acc: 0.708 - ETA: 1s - loss: 0.5617 - acc: 0.708 - ETA: 1s - loss: 0.5616 - acc: 0.708 - ETA: 1s - loss: 0.5616 - acc: 0.708 - ETA: 1s - loss: 0.5618 - acc: 0.708 - ETA: 1s - loss: 0.5617 - acc: 0.708 - ETA: 1s - loss: 0.5616 - acc: 0.708 - ETA: 1s - loss: 0.5614 - acc: 0.708 - ETA: 1s - loss: 0.5614 - acc: 0.708 - ETA: 0s - loss: 0.5611 - acc: 0.709 - ETA: 0s - loss: 0.5612 - acc: 0.708 - ETA: 0s - loss: 0.5612 - acc: 0.709 - ETA: 0s - loss: 0.5611 - acc: 0.709 - ETA: 0s - loss: 0.5613 - acc: 0.708 - 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Epoch 3/10
44992/58517 [======================>.......] - ETA: 29s - loss: 0.5593 - acc: 0.67 - ETA: 16s - loss: 0.5969 - acc: 0.67 - ETA: 15s - loss: 0.5875 - acc: 0.68 - ETA: 14s - loss: 0.5723 - acc: 0.70 - ETA: 14s - loss: 0.5630 - acc: 0.70 - ETA: 14s - loss: 0.5661 - acc: 0.70 - ETA: 13s - loss: 0.5651 - acc: 0.70 - ETA: 13s - loss: 0.5685 - acc: 0.70 - ETA: 13s - loss: 0.5717 - acc: 0.70 - ETA: 13s - loss: 0.5713 - acc: 0.69 - ETA: 13s - loss: 0.5719 - acc: 0.69 - ETA: 13s - loss: 0.5724 - acc: 0.69 - ETA: 13s - loss: 0.5740 - acc: 0.69 - ETA: 13s - loss: 0.5758 - acc: 0.69 - ETA: 13s - loss: 0.5727 - acc: 0.69 - ETA: 13s - loss: 0.5736 - acc: 0.69 - ETA: 13s - loss: 0.5735 - acc: 0.69 - ETA: 13s - loss: 0.5710 - acc: 0.69 - ETA: 12s - loss: 0.5716 - acc: 0.69 - ETA: 12s - loss: 0.5719 - acc: 0.69 - ETA: 12s - loss: 0.5714 - acc: 0.69 - ETA: 12s - loss: 0.5722 - acc: 0.69 - ETA: 12s - loss: 0.5706 - acc: 0.70 - ETA: 12s - loss: 0.5704 - acc: 0.70 - ETA: 12s - loss: 0.5715 - acc: 0.70 - ETA: 12s - 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acc: 0.713 - ETA: 3s - loss: 0.5597 - acc: 0.713 - ETA: 3s - loss: 0.5595 - acc: 0.713 - ETA: 3s - loss: 0.5595 - acc: 0.713 - ETA: 3s - loss: 0.5598 - acc: 0.713458517/58517 [==============================] - ETA: 3s - loss: 0.5597 - acc: 0.713 - ETA: 3s - loss: 0.5597 - acc: 0.713 - ETA: 3s - loss: 0.5599 - acc: 0.713 - ETA: 3s - loss: 0.5598 - acc: 0.713 - ETA: 3s - loss: 0.5598 - acc: 0.713 - ETA: 3s - loss: 0.5596 - acc: 0.713 - ETA: 3s - loss: 0.5595 - acc: 0.713 - ETA: 3s - loss: 0.5593 - acc: 0.713 - ETA: 3s - loss: 0.5592 - acc: 0.713 - ETA: 3s - loss: 0.5593 - acc: 0.713 - ETA: 3s - loss: 0.5590 - acc: 0.714 - ETA: 3s - loss: 0.5591 - acc: 0.713 - ETA: 2s - loss: 0.5593 - acc: 0.713 - ETA: 2s - loss: 0.5595 - acc: 0.713 - ETA: 2s - loss: 0.5595 - acc: 0.713 - ETA: 2s - loss: 0.5596 - acc: 0.713 - ETA: 2s - loss: 0.5596 - acc: 0.713 - ETA: 2s - loss: 0.5597 - acc: 0.713 - ETA: 2s - loss: 0.5597 - acc: 0.713 - ETA: 2s - loss: 0.5596 - acc: 0.713 - ETA: 2s - loss: 0.5598 - acc: 0.712 - 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Epoch 4/10
51840/58517 [=========================>....] - ETA: 14s - loss: 0.5921 - acc: 0.65 - ETA: 13s - loss: 0.5616 - acc: 0.70 - ETA: 13s - loss: 0.5732 - acc: 0.67 - ETA: 13s - loss: 0.5590 - acc: 0.69 - ETA: 13s - loss: 0.5532 - acc: 0.70 - ETA: 13s - loss: 0.5546 - acc: 0.70 - ETA: 13s - loss: 0.5505 - acc: 0.71 - ETA: 13s - loss: 0.5491 - acc: 0.71 - ETA: 13s - loss: 0.5423 - acc: 0.71 - ETA: 13s - loss: 0.5465 - acc: 0.71 - ETA: 13s - loss: 0.5430 - acc: 0.72 - ETA: 13s - loss: 0.5457 - acc: 0.72 - ETA: 13s - loss: 0.5450 - acc: 0.72 - ETA: 13s - loss: 0.5488 - acc: 0.71 - ETA: 12s - loss: 0.5515 - acc: 0.71 - ETA: 12s - loss: 0.5526 - acc: 0.71 - ETA: 13s - loss: 0.5525 - acc: 0.71 - ETA: 12s - loss: 0.5523 - acc: 0.71 - ETA: 12s - loss: 0.5513 - acc: 0.71 - ETA: 12s - loss: 0.5510 - acc: 0.71 - ETA: 12s - loss: 0.5520 - acc: 0.71 - ETA: 12s - loss: 0.5549 - acc: 0.71 - ETA: 12s - loss: 0.5550 - acc: 0.71 - ETA: 12s - loss: 0.5563 - acc: 0.71 - ETA: 12s - loss: 0.5558 - acc: 0.71 - ETA: 12s - 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acc: 0.710 - ETA: 1s - loss: 0.5595 - acc: 0.710 - ETA: 1s - loss: 0.5592 - acc: 0.711 - ETA: 1s - loss: 0.5590 - acc: 0.711 - ETA: 1s - loss: 0.5590 - acc: 0.711358517/58517 [==============================] - ETA: 1s - loss: 0.5589 - acc: 0.711 - ETA: 1s - loss: 0.5589 - acc: 0.711 - ETA: 1s - loss: 0.5589 - acc: 0.711 - ETA: 1s - loss: 0.5588 - acc: 0.711 - ETA: 1s - loss: 0.5588 - acc: 0.711 - ETA: 1s - loss: 0.5588 - acc: 0.711 - ETA: 1s - loss: 0.5587 - acc: 0.711 - ETA: 1s - loss: 0.5586 - acc: 0.711 - ETA: 1s - loss: 0.5586 - acc: 0.711 - ETA: 1s - loss: 0.5587 - acc: 0.711 - ETA: 1s - loss: 0.5585 - acc: 0.711 - ETA: 0s - loss: 0.5586 - acc: 0.711 - ETA: 0s - loss: 0.5585 - acc: 0.711 - ETA: 0s - loss: 0.5584 - acc: 0.711 - ETA: 0s - loss: 0.5583 - acc: 0.712 - ETA: 0s - loss: 0.5585 - acc: 0.711 - ETA: 0s - loss: 0.5586 - acc: 0.711 - ETA: 0s - loss: 0.5587 - acc: 0.711 - ETA: 0s - loss: 0.5586 - acc: 0.711 - ETA: 0s - loss: 0.5586 - acc: 0.711 - ETA: 0s - loss: 0.5585 - acc: 0.711 - ETA: 0s - loss: 0.5585 - acc: 0.711 - ETA: 0s - loss: 0.5587 - acc: 0.711 - ETA: 0s - loss: 0.5587 - acc: 0.711 - ETA: 0s - loss: 0.5588 - acc: 0.711 - ETA: 0s - loss: 0.5587 - acc: 0.711 - ETA: 0s - loss: 0.5589 - acc: 0.711 - ETA: 0s - loss: 0.5589 - acc: 0.711 - ETA: 0s - loss: 0.5587 - acc: 0.711 - 15s - loss: 0.5586 - acc: 0.7119 - val_loss: 0.5624 - val_acc: 0.7103
Epoch 5/10
48256/58517 [=======================>......] - ETA: 21s - loss: 0.5749 - acc: 0.67 - ETA: 15s - loss: 0.5600 - acc: 0.70 - ETA: 15s - loss: 0.5795 - acc: 0.69 - ETA: 15s - loss: 0.5666 - acc: 0.70 - ETA: 14s - loss: 0.5615 - acc: 0.70 - ETA: 16s - loss: 0.5584 - acc: 0.70 - ETA: 16s - loss: 0.5624 - acc: 0.70 - ETA: 17s - loss: 0.5604 - acc: 0.70 - ETA: 18s - loss: 0.5588 - acc: 0.70 - ETA: 17s - loss: 0.5553 - acc: 0.71 - ETA: 17s - loss: 0.5532 - acc: 0.71 - ETA: 16s - loss: 0.5514 - acc: 0.71 - ETA: 16s - loss: 0.5515 - acc: 0.71 - ETA: 16s - loss: 0.5550 - acc: 0.71 - ETA: 16s - loss: 0.5534 - acc: 0.71 - ETA: 15s - loss: 0.5523 - acc: 0.71 - ETA: 15s - loss: 0.5520 - acc: 0.71 - ETA: 15s - loss: 0.5550 - acc: 0.71 - ETA: 16s - loss: 0.5555 - acc: 0.71 - ETA: 16s - loss: 0.5546 - acc: 0.71 - ETA: 15s - loss: 0.5551 - acc: 0.71 - ETA: 15s - loss: 0.5562 - acc: 0.71 - ETA: 15s - loss: 0.5564 - acc: 0.71 - ETA: 14s - loss: 0.5578 - acc: 0.71 - ETA: 14s - loss: 0.5593 - acc: 0.70 - ETA: 14s - loss: 0.5581 - acc: 0.70 - ETA: 14s - loss: 0.5580 - acc: 0.70 - ETA: 14s - loss: 0.5580 - acc: 0.70 - ETA: 14s - loss: 0.5601 - acc: 0.70 - ETA: 13s - loss: 0.5600 - acc: 0.70 - ETA: 13s - loss: 0.5600 - acc: 0.70 - ETA: 13s - loss: 0.5600 - acc: 0.70 - ETA: 13s - loss: 0.5619 - acc: 0.70 - ETA: 13s - loss: 0.5623 - acc: 0.70 - ETA: 13s - loss: 0.5624 - acc: 0.70 - ETA: 13s - loss: 0.5636 - acc: 0.70 - ETA: 13s - loss: 0.5634 - acc: 0.70 - ETA: 13s - loss: 0.5636 - acc: 0.70 - ETA: 12s - loss: 0.5625 - acc: 0.70 - ETA: 12s - loss: 0.5627 - acc: 0.70 - ETA: 12s - loss: 0.5615 - acc: 0.70 - ETA: 12s - loss: 0.5616 - acc: 0.70 - ETA: 12s - loss: 0.5637 - acc: 0.70 - ETA: 12s - loss: 0.5629 - acc: 0.70 - ETA: 12s - loss: 0.5634 - acc: 0.70 - ETA: 12s - loss: 0.5632 - acc: 0.70 - ETA: 12s - loss: 0.5625 - acc: 0.70 - ETA: 12s - loss: 0.5622 - acc: 0.70 - ETA: 12s - loss: 0.5628 - acc: 0.70 - ETA: 12s - loss: 0.5635 - acc: 0.70 - ETA: 12s - loss: 0.5623 - acc: 0.70 - ETA: 12s - loss: 0.5617 - acc: 0.70 - ETA: 12s - loss: 0.5622 - acc: 0.70 - ETA: 12s - loss: 0.5630 - acc: 0.70 - ETA: 11s - loss: 0.5625 - acc: 0.70 - ETA: 11s - loss: 0.5623 - acc: 0.70 - ETA: 11s - loss: 0.5618 - acc: 0.70 - ETA: 11s - loss: 0.5614 - acc: 0.70 - ETA: 11s - loss: 0.5612 - acc: 0.70 - ETA: 11s - loss: 0.5602 - acc: 0.70 - ETA: 11s - loss: 0.5604 - acc: 0.70 - ETA: 11s - loss: 0.5613 - acc: 0.70 - ETA: 11s - loss: 0.5617 - acc: 0.70 - ETA: 11s - loss: 0.5617 - acc: 0.70 - ETA: 11s - loss: 0.5613 - acc: 0.70 - ETA: 11s - loss: 0.5623 - acc: 0.70 - ETA: 11s - loss: 0.5622 - acc: 0.70 - ETA: 11s - loss: 0.5617 - acc: 0.70 - ETA: 11s - loss: 0.5609 - acc: 0.70 - ETA: 10s - loss: 0.5603 - acc: 0.70 - ETA: 10s - loss: 0.5595 - acc: 0.70 - ETA: 10s - loss: 0.5593 - acc: 0.70 - ETA: 10s - loss: 0.5595 - acc: 0.70 - ETA: 10s - loss: 0.5588 - acc: 0.70 - ETA: 10s - loss: 0.5589 - acc: 0.70 - ETA: 10s - loss: 0.5585 - acc: 0.70 - ETA: 10s - loss: 0.5587 - acc: 0.70 - ETA: 10s - loss: 0.5580 - acc: 0.70 - 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acc: 0.709 - ETA: 8s - loss: 0.5586 - acc: 0.709 - ETA: 8s - loss: 0.5587 - acc: 0.709 - ETA: 8s - loss: 0.5587 - acc: 0.708 - ETA: 8s - loss: 0.5588 - acc: 0.709 - ETA: 8s - loss: 0.5591 - acc: 0.708 - ETA: 8s - loss: 0.5590 - acc: 0.709 - ETA: 8s - loss: 0.5588 - acc: 0.709 - ETA: 8s - loss: 0.5583 - acc: 0.710 - ETA: 8s - loss: 0.5580 - acc: 0.710 - ETA: 8s - loss: 0.5581 - acc: 0.710 - ETA: 8s - loss: 0.5588 - acc: 0.710 - ETA: 8s - loss: 0.5590 - acc: 0.710 - ETA: 8s - loss: 0.5590 - acc: 0.710 - ETA: 8s - loss: 0.5591 - acc: 0.710 - ETA: 8s - loss: 0.5590 - acc: 0.710 - ETA: 7s - loss: 0.5594 - acc: 0.710 - ETA: 7s - loss: 0.5591 - acc: 0.710 - ETA: 7s - loss: 0.5591 - acc: 0.710 - ETA: 7s - loss: 0.5591 - acc: 0.710 - ETA: 7s - loss: 0.5590 - acc: 0.710 - ETA: 7s - loss: 0.5595 - acc: 0.710 - ETA: 7s - loss: 0.5592 - acc: 0.710 - ETA: 7s - loss: 0.5593 - acc: 0.710 - ETA: 7s - loss: 0.5588 - acc: 0.710 - ETA: 7s - loss: 0.5582 - acc: 0.711 - ETA: 7s - loss: 0.5582 - acc: 0.711 - ETA: 7s - loss: 0.5583 - acc: 0.711 - ETA: 7s - loss: 0.5585 - acc: 0.711 - ETA: 7s - loss: 0.5586 - acc: 0.711 - ETA: 7s - loss: 0.5586 - acc: 0.711 - ETA: 7s - loss: 0.5582 - acc: 0.711 - ETA: 6s - loss: 0.5578 - acc: 0.711 - ETA: 6s - loss: 0.5576 - acc: 0.711 - ETA: 6s - loss: 0.5576 - acc: 0.711 - ETA: 6s - loss: 0.5577 - acc: 0.711 - ETA: 6s - loss: 0.5571 - acc: 0.712 - ETA: 6s - loss: 0.5572 - acc: 0.711 - ETA: 6s - loss: 0.5572 - acc: 0.711 - ETA: 6s - loss: 0.5572 - acc: 0.711 - ETA: 6s - loss: 0.5571 - acc: 0.711 - ETA: 6s - loss: 0.5566 - acc: 0.712 - ETA: 6s - loss: 0.5567 - acc: 0.712 - ETA: 6s - loss: 0.5566 - acc: 0.712 - ETA: 6s - loss: 0.5567 - acc: 0.712 - ETA: 6s - loss: 0.5566 - acc: 0.712 - ETA: 6s - loss: 0.5572 - acc: 0.712 - ETA: 6s - loss: 0.5575 - acc: 0.711 - ETA: 6s - loss: 0.5577 - acc: 0.711 - ETA: 5s - loss: 0.5577 - acc: 0.711 - ETA: 5s - loss: 0.5575 - acc: 0.711 - ETA: 5s - loss: 0.5575 - acc: 0.711 - ETA: 5s - loss: 0.5575 - acc: 0.711 - ETA: 5s - loss: 0.5574 - acc: 0.711 - ETA: 5s - loss: 0.5574 - acc: 0.711 - ETA: 5s - loss: 0.5575 - acc: 0.711 - ETA: 5s - loss: 0.5579 - acc: 0.711 - ETA: 5s - loss: 0.5581 - acc: 0.711 - ETA: 5s - loss: 0.5579 - acc: 0.711 - ETA: 5s - loss: 0.5577 - acc: 0.711 - ETA: 5s - loss: 0.5578 - acc: 0.711 - ETA: 5s - loss: 0.5577 - acc: 0.711 - ETA: 5s - loss: 0.5579 - acc: 0.711 - ETA: 5s - loss: 0.5578 - acc: 0.711 - ETA: 5s - loss: 0.5579 - acc: 0.710 - ETA: 5s - loss: 0.5582 - acc: 0.710 - ETA: 5s - loss: 0.5582 - acc: 0.710 - ETA: 5s - loss: 0.5585 - acc: 0.710 - ETA: 4s - loss: 0.5584 - acc: 0.710 - ETA: 4s - loss: 0.5583 - acc: 0.711 - ETA: 4s - loss: 0.5584 - acc: 0.710 - ETA: 4s - loss: 0.5587 - acc: 0.710 - ETA: 4s - loss: 0.5585 - acc: 0.710 - ETA: 4s - loss: 0.5585 - acc: 0.710 - ETA: 4s - loss: 0.5584 - acc: 0.710 - ETA: 4s - loss: 0.5581 - acc: 0.710 - ETA: 4s - loss: 0.5579 - acc: 0.711 - ETA: 4s - loss: 0.5580 - acc: 0.711 - ETA: 4s - loss: 0.5582 - acc: 0.710 - ETA: 4s - loss: 0.5583 - acc: 0.710 - ETA: 4s - loss: 0.5583 - acc: 0.710 - ETA: 4s - loss: 0.5583 - acc: 0.710 - ETA: 4s - loss: 0.5584 - acc: 0.710 - ETA: 4s - loss: 0.5582 - acc: 0.710 - ETA: 4s - loss: 0.5584 - acc: 0.710 - ETA: 4s - loss: 0.5584 - acc: 0.710 - ETA: 3s - loss: 0.5588 - acc: 0.710 - ETA: 3s - loss: 0.5587 - acc: 0.710 - ETA: 3s - loss: 0.5585 - acc: 0.710 - ETA: 3s - loss: 0.5583 - acc: 0.710 - ETA: 3s - loss: 0.5583 - acc: 0.710 - ETA: 3s - loss: 0.5583 - acc: 0.710 - ETA: 3s - loss: 0.5588 - acc: 0.709 - ETA: 3s - loss: 0.5589 - acc: 0.709 - ETA: 3s - loss: 0.5589 - acc: 0.709 - ETA: 3s - loss: 0.5591 - acc: 0.709 - ETA: 3s - loss: 0.5590 - acc: 0.709 - ETA: 3s - loss: 0.5589 - acc: 0.709 - ETA: 3s - loss: 0.5588 - acc: 0.709 - ETA: 3s - loss: 0.5590 - acc: 0.709 - ETA: 3s - loss: 0.5592 - acc: 0.709 - ETA: 3s - loss: 0.5589 - acc: 0.709 - ETA: 3s - loss: 0.5591 - acc: 0.709 - ETA: 3s - loss: 0.5590 - acc: 0.709 - ETA: 2s - loss: 0.5589 - acc: 0.709 - ETA: 2s - loss: 0.5590 - acc: 0.709 - ETA: 2s - loss: 0.5591 - acc: 0.709 - ETA: 2s - loss: 0.5592 - acc: 0.709 - ETA: 2s - loss: 0.5594 - acc: 0.709 - ETA: 2s - loss: 0.5594 - acc: 0.709 - ETA: 2s - loss: 0.5594 - acc: 0.709858517/58517 [==============================] - ETA: 2s - loss: 0.5594 - acc: 0.709 - ETA: 2s - loss: 0.5594 - acc: 0.709 - ETA: 2s - loss: 0.5593 - acc: 0.709 - ETA: 2s - loss: 0.5592 - acc: 0.710 - ETA: 2s - loss: 0.5594 - acc: 0.709 - ETA: 2s - loss: 0.5593 - acc: 0.709 - ETA: 2s - loss: 0.5593 - acc: 0.709 - ETA: 2s - loss: 0.5593 - acc: 0.709 - ETA: 2s - loss: 0.5592 - acc: 0.709 - ETA: 2s - loss: 0.5592 - acc: 0.709 - ETA: 1s - loss: 0.5591 - acc: 0.710 - ETA: 1s - loss: 0.5589 - acc: 0.710 - ETA: 1s - loss: 0.5590 - acc: 0.709 - ETA: 1s - loss: 0.5590 - acc: 0.710 - ETA: 1s - loss: 0.5588 - acc: 0.710 - ETA: 1s - loss: 0.5588 - acc: 0.710 - ETA: 1s - loss: 0.5587 - acc: 0.710 - ETA: 1s - loss: 0.5587 - acc: 0.710 - ETA: 1s - loss: 0.5587 - acc: 0.710 - ETA: 1s - loss: 0.5586 - acc: 0.710 - ETA: 1s - loss: 0.5590 - acc: 0.710 - 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Epoch 6/10
50112/58517 [========================>.....] - ETA: 14s - loss: 0.5883 - acc: 0.68 - ETA: 14s - loss: 0.5561 - acc: 0.69 - ETA: 14s - loss: 0.5408 - acc: 0.71 - ETA: 14s - loss: 0.5458 - acc: 0.72 - ETA: 13s - loss: 0.5536 - acc: 0.71 - ETA: 13s - loss: 0.5640 - acc: 0.71 - ETA: 13s - loss: 0.5641 - acc: 0.71 - ETA: 13s - loss: 0.5669 - acc: 0.70 - ETA: 13s - loss: 0.5644 - acc: 0.71 - ETA: 13s - loss: 0.5641 - acc: 0.71 - ETA: 13s - loss: 0.5643 - acc: 0.71 - ETA: 13s - loss: 0.5641 - acc: 0.70 - ETA: 13s - loss: 0.5640 - acc: 0.70 - ETA: 13s - loss: 0.5624 - acc: 0.71 - ETA: 12s - loss: 0.5595 - acc: 0.71 - ETA: 12s - loss: 0.5611 - acc: 0.71 - ETA: 12s - loss: 0.5610 - acc: 0.71 - ETA: 12s - loss: 0.5607 - acc: 0.70 - ETA: 12s - loss: 0.5598 - acc: 0.70 - ETA: 12s - loss: 0.5576 - acc: 0.71 - ETA: 12s - loss: 0.5569 - acc: 0.71 - ETA: 12s - loss: 0.5571 - acc: 0.71 - ETA: 12s - loss: 0.5575 - acc: 0.71 - ETA: 12s - loss: 0.5562 - acc: 0.71 - ETA: 12s - loss: 0.5566 - acc: 0.71 - ETA: 12s - 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acc: 0.71 - ETA: 11s - loss: 0.5521 - acc: 0.71 - ETA: 11s - loss: 0.5524 - acc: 0.71 - ETA: 11s - loss: 0.5525 - acc: 0.71 - ETA: 11s - loss: 0.5527 - acc: 0.71 - ETA: 10s - loss: 0.5536 - acc: 0.71 - ETA: 10s - loss: 0.5537 - acc: 0.71 - ETA: 10s - loss: 0.5540 - acc: 0.71 - ETA: 10s - loss: 0.5543 - acc: 0.71 - ETA: 10s - loss: 0.5544 - acc: 0.71 - ETA: 10s - loss: 0.5542 - acc: 0.71 - ETA: 10s - loss: 0.5546 - acc: 0.71 - ETA: 10s - loss: 0.5545 - acc: 0.71 - ETA: 10s - loss: 0.5546 - acc: 0.71 - ETA: 10s - loss: 0.5557 - acc: 0.71 - ETA: 10s - loss: 0.5553 - acc: 0.71 - ETA: 10s - loss: 0.5551 - acc: 0.71 - ETA: 10s - loss: 0.5559 - acc: 0.71 - ETA: 10s - loss: 0.5564 - acc: 0.71 - ETA: 10s - loss: 0.5562 - acc: 0.71 - ETA: 10s - loss: 0.5564 - acc: 0.71 - ETA: 10s - loss: 0.5560 - acc: 0.71 - ETA: 9s - loss: 0.5554 - acc: 0.7155 - ETA: 9s - loss: 0.5553 - acc: 0.714 - ETA: 9s - loss: 0.5558 - acc: 0.714 - ETA: 9s - loss: 0.5564 - acc: 0.713 - ETA: 9s - loss: 0.5554 - acc: 0.714 - 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Epoch 7/10
52160/58517 [=========================>....] - ETA: 21s - loss: 0.7074 - acc: 0.59 - ETA: 16s - loss: 0.5556 - acc: 0.70 - ETA: 15s - loss: 0.5433 - acc: 0.70 - ETA: 15s - loss: 0.5491 - acc: 0.70 - ETA: 14s - loss: 0.5644 - acc: 0.70 - ETA: 14s - loss: 0.5634 - acc: 0.69 - ETA: 14s - loss: 0.5617 - acc: 0.70 - ETA: 14s - loss: 0.5545 - acc: 0.70 - ETA: 14s - loss: 0.5547 - acc: 0.70 - ETA: 15s - loss: 0.5502 - acc: 0.71 - ETA: 15s - loss: 0.5477 - acc: 0.71 - ETA: 15s - loss: 0.5470 - acc: 0.71 - ETA: 14s - loss: 0.5459 - acc: 0.72 - ETA: 14s - loss: 0.5513 - acc: 0.71 - ETA: 14s - loss: 0.5552 - acc: 0.71 - ETA: 14s - loss: 0.5533 - acc: 0.71 - ETA: 14s - loss: 0.5545 - acc: 0.71 - ETA: 14s - loss: 0.5522 - acc: 0.71 - ETA: 14s - loss: 0.5512 - acc: 0.71 - ETA: 13s - loss: 0.5534 - acc: 0.71 - ETA: 13s - loss: 0.5528 - acc: 0.71 - ETA: 13s - loss: 0.5522 - acc: 0.71 - ETA: 13s - loss: 0.5539 - acc: 0.71 - ETA: 13s - loss: 0.5524 - acc: 0.71 - ETA: 13s - loss: 0.5539 - acc: 0.71 - ETA: 13s - loss: 0.5522 - acc: 0.71 - ETA: 13s - loss: 0.5526 - acc: 0.71 - ETA: 13s - loss: 0.5540 - acc: 0.71 - ETA: 12s - loss: 0.5532 - acc: 0.71 - ETA: 12s - loss: 0.5527 - acc: 0.71 - ETA: 12s - loss: 0.5534 - acc: 0.71 - ETA: 12s - loss: 0.5550 - acc: 0.71 - ETA: 12s - loss: 0.5550 - acc: 0.71 - ETA: 12s - loss: 0.5560 - acc: 0.71 - ETA: 12s - loss: 0.5557 - acc: 0.71 - ETA: 12s - loss: 0.5554 - acc: 0.71 - ETA: 12s - loss: 0.5551 - acc: 0.71 - ETA: 12s - loss: 0.5559 - acc: 0.71 - ETA: 12s - loss: 0.5557 - acc: 0.71 - ETA: 11s - loss: 0.5550 - acc: 0.71 - ETA: 11s - loss: 0.5551 - acc: 0.71 - ETA: 11s - loss: 0.5546 - acc: 0.71 - ETA: 11s - loss: 0.5547 - acc: 0.71 - ETA: 11s - loss: 0.5548 - acc: 0.71 - ETA: 11s - loss: 0.5549 - acc: 0.71 - ETA: 11s - loss: 0.5551 - acc: 0.71 - ETA: 11s - loss: 0.5553 - acc: 0.71 - ETA: 11s - loss: 0.5552 - acc: 0.71 - ETA: 11s - loss: 0.5557 - acc: 0.71 - ETA: 11s - loss: 0.5551 - acc: 0.71 - ETA: 11s - loss: 0.5548 - acc: 0.71 - ETA: 11s - loss: 0.5544 - acc: 0.71 - ETA: 10s - loss: 0.5538 - acc: 0.71 - ETA: 10s - loss: 0.5533 - acc: 0.71 - ETA: 10s - loss: 0.5526 - acc: 0.71 - ETA: 10s - loss: 0.5543 - acc: 0.71 - ETA: 10s - loss: 0.5549 - acc: 0.71 - ETA: 10s - loss: 0.5540 - acc: 0.71 - ETA: 10s - loss: 0.5544 - acc: 0.71 - ETA: 10s - loss: 0.5554 - acc: 0.71 - ETA: 10s - loss: 0.5553 - acc: 0.71 - ETA: 10s - loss: 0.5553 - acc: 0.71 - ETA: 10s - loss: 0.5552 - acc: 0.71 - ETA: 10s - loss: 0.5542 - acc: 0.71 - ETA: 10s - loss: 0.5542 - acc: 0.71 - ETA: 10s - loss: 0.5535 - acc: 0.71 - ETA: 10s - loss: 0.5533 - acc: 0.71 - ETA: 10s - loss: 0.5538 - acc: 0.71 - ETA: 10s - loss: 0.5532 - acc: 0.71 - ETA: 10s - loss: 0.5534 - acc: 0.71 - ETA: 9s - loss: 0.5534 - acc: 0.7166 - ETA: 9s - loss: 0.5529 - acc: 0.716 - ETA: 9s - loss: 0.5534 - acc: 0.716 - ETA: 9s - loss: 0.5531 - acc: 0.716 - ETA: 9s - loss: 0.5526 - acc: 0.716 - ETA: 9s - loss: 0.5525 - acc: 0.716 - ETA: 9s - loss: 0.5529 - acc: 0.716 - ETA: 9s - loss: 0.5527 - acc: 0.716 - 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acc: 0.712 - ETA: 1s - loss: 0.5575 - acc: 0.712 - ETA: 1s - loss: 0.5574 - acc: 0.712 - ETA: 1s - loss: 0.5573 - acc: 0.712 - ETA: 1s - loss: 0.5572 - acc: 0.712358517/58517 [==============================] - ETA: 1s - loss: 0.5570 - acc: 0.712 - ETA: 1s - loss: 0.5566 - acc: 0.712 - ETA: 1s - loss: 0.5566 - acc: 0.712 - ETA: 1s - loss: 0.5567 - acc: 0.712 - ETA: 1s - loss: 0.5566 - acc: 0.712 - ETA: 1s - loss: 0.5564 - acc: 0.713 - ETA: 1s - loss: 0.5565 - acc: 0.712 - ETA: 1s - loss: 0.5562 - acc: 0.713 - ETA: 1s - loss: 0.5563 - acc: 0.713 - ETA: 0s - loss: 0.5563 - acc: 0.713 - ETA: 0s - loss: 0.5561 - acc: 0.713 - ETA: 0s - loss: 0.5560 - acc: 0.713 - ETA: 0s - loss: 0.5560 - acc: 0.713 - ETA: 0s - loss: 0.5560 - acc: 0.713 - ETA: 0s - loss: 0.5559 - acc: 0.713 - ETA: 0s - loss: 0.5561 - acc: 0.713 - ETA: 0s - loss: 0.5560 - acc: 0.713 - ETA: 0s - loss: 0.5560 - acc: 0.713 - ETA: 0s - loss: 0.5559 - acc: 0.713 - ETA: 0s - loss: 0.5560 - acc: 0.713 - ETA: 0s - loss: 0.5561 - acc: 0.713 - ETA: 0s - loss: 0.5561 - acc: 0.713 - ETA: 0s - loss: 0.5561 - acc: 0.713 - ETA: 0s - loss: 0.5560 - acc: 0.713 - ETA: 0s - loss: 0.5562 - acc: 0.713 - 14s - loss: 0.5562 - acc: 0.7134 - val_loss: 0.5624 - val_acc: 0.7107
Epoch 8/10
51712/58517 [=========================>....] - ETA: 14s - loss: 0.5322 - acc: 0.70 - ETA: 15s - loss: 0.5429 - acc: 0.71 - ETA: 13s - loss: 0.5621 - acc: 0.70 - ETA: 14s - loss: 0.5578 - acc: 0.70 - ETA: 14s - loss: 0.5553 - acc: 0.71 - ETA: 13s - loss: 0.5540 - acc: 0.71 - ETA: 14s - loss: 0.5486 - acc: 0.71 - ETA: 13s - loss: 0.5503 - acc: 0.71 - ETA: 13s - loss: 0.5540 - acc: 0.71 - ETA: 13s - loss: 0.5574 - acc: 0.70 - ETA: 13s - loss: 0.5542 - acc: 0.71 - ETA: 13s - loss: 0.5523 - acc: 0.71 - ETA: 13s - loss: 0.5542 - acc: 0.71 - ETA: 13s - loss: 0.5539 - acc: 0.71 - ETA: 13s - loss: 0.5576 - acc: 0.70 - ETA: 13s - loss: 0.5586 - acc: 0.70 - ETA: 13s - loss: 0.5581 - acc: 0.71 - ETA: 12s - loss: 0.5568 - acc: 0.71 - ETA: 13s - loss: 0.5571 - acc: 0.71 - ETA: 13s - loss: 0.5558 - acc: 0.71 - ETA: 12s - loss: 0.5557 - acc: 0.71 - ETA: 12s - loss: 0.5564 - acc: 0.70 - ETA: 12s - loss: 0.5550 - acc: 0.71 - ETA: 12s - loss: 0.5558 - acc: 0.71 - ETA: 12s - loss: 0.5543 - acc: 0.71 - ETA: 12s - loss: 0.5536 - acc: 0.71 - ETA: 12s - loss: 0.5537 - acc: 0.71 - ETA: 12s - loss: 0.5531 - acc: 0.71 - ETA: 12s - loss: 0.5528 - acc: 0.71 - ETA: 12s - loss: 0.5536 - acc: 0.71 - ETA: 12s - loss: 0.5528 - acc: 0.71 - ETA: 11s - loss: 0.5540 - acc: 0.71 - ETA: 11s - loss: 0.5536 - acc: 0.71 - ETA: 11s - loss: 0.5533 - acc: 0.71 - ETA: 11s - loss: 0.5527 - acc: 0.71 - ETA: 11s - loss: 0.5535 - acc: 0.71 - ETA: 11s - loss: 0.5545 - acc: 0.71 - ETA: 11s - loss: 0.5538 - acc: 0.71 - ETA: 11s - loss: 0.5543 - acc: 0.71 - ETA: 11s - loss: 0.5537 - acc: 0.71 - ETA: 11s - loss: 0.5535 - acc: 0.71 - ETA: 11s - loss: 0.5540 - acc: 0.71 - ETA: 11s - loss: 0.5542 - acc: 0.71 - ETA: 11s - loss: 0.5544 - acc: 0.71 - ETA: 11s - loss: 0.5544 - acc: 0.71 - ETA: 11s - loss: 0.5541 - acc: 0.71 - ETA: 11s - loss: 0.5546 - acc: 0.71 - ETA: 11s - loss: 0.5532 - acc: 0.71 - ETA: 11s - loss: 0.5538 - acc: 0.71 - ETA: 11s - loss: 0.5530 - acc: 0.71 - ETA: 10s - loss: 0.5520 - acc: 0.71 - ETA: 10s - loss: 0.5512 - acc: 0.72 - ETA: 10s - loss: 0.5512 - acc: 0.72 - ETA: 10s - loss: 0.5509 - acc: 0.72 - ETA: 10s - loss: 0.5512 - acc: 0.72 - ETA: 10s - loss: 0.5513 - acc: 0.71 - ETA: 10s - loss: 0.5511 - acc: 0.71 - ETA: 10s - loss: 0.5506 - acc: 0.71 - ETA: 10s - loss: 0.5509 - acc: 0.71 - ETA: 10s - loss: 0.5507 - acc: 0.71 - ETA: 10s - loss: 0.5498 - acc: 0.72 - ETA: 10s - loss: 0.5507 - acc: 0.72 - ETA: 10s - loss: 0.5502 - acc: 0.72 - ETA: 10s - loss: 0.5497 - acc: 0.72 - ETA: 10s - loss: 0.5504 - acc: 0.72 - ETA: 10s - loss: 0.5509 - acc: 0.71 - ETA: 10s - loss: 0.5507 - acc: 0.72 - ETA: 9s - loss: 0.5506 - acc: 0.7203 - ETA: 9s - loss: 0.5510 - acc: 0.720 - ETA: 9s - loss: 0.5509 - acc: 0.720 - ETA: 9s - loss: 0.5508 - acc: 0.720 - ETA: 9s - loss: 0.5512 - acc: 0.719 - ETA: 9s - loss: 0.5514 - acc: 0.719 - ETA: 9s - loss: 0.5510 - acc: 0.719 - ETA: 9s - loss: 0.5519 - acc: 0.719 - ETA: 9s - loss: 0.5522 - acc: 0.718 - ETA: 9s - loss: 0.5520 - acc: 0.718 - ETA: 9s - loss: 0.5523 - acc: 0.718 - 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acc: 0.715 - ETA: 1s - loss: 0.5549 - acc: 0.715 - ETA: 1s - loss: 0.5548 - acc: 0.715 - ETA: 1s - loss: 0.5547 - acc: 0.715 - ETA: 1s - loss: 0.5548 - acc: 0.715258517/58517 [==============================] - ETA: 1s - loss: 0.5545 - acc: 0.715 - ETA: 1s - loss: 0.5549 - acc: 0.715 - ETA: 1s - loss: 0.5549 - acc: 0.714 - ETA: 1s - loss: 0.5551 - acc: 0.714 - ETA: 1s - loss: 0.5549 - acc: 0.714 - ETA: 1s - loss: 0.5550 - acc: 0.714 - ETA: 1s - loss: 0.5549 - acc: 0.715 - ETA: 1s - loss: 0.5550 - acc: 0.714 - ETA: 1s - loss: 0.5551 - acc: 0.714 - ETA: 1s - loss: 0.5553 - acc: 0.714 - ETA: 0s - loss: 0.5552 - acc: 0.714 - ETA: 0s - loss: 0.5554 - acc: 0.714 - ETA: 0s - loss: 0.5553 - acc: 0.714 - ETA: 0s - loss: 0.5552 - acc: 0.714 - ETA: 0s - loss: 0.5553 - acc: 0.714 - ETA: 0s - loss: 0.5553 - acc: 0.714 - ETA: 0s - loss: 0.5553 - acc: 0.714 - ETA: 0s - loss: 0.5552 - acc: 0.714 - ETA: 0s - loss: 0.5553 - acc: 0.714 - ETA: 0s - loss: 0.5553 - acc: 0.714 - ETA: 0s - loss: 0.5555 - acc: 0.714 - ETA: 0s - loss: 0.5554 - acc: 0.714 - ETA: 0s - loss: 0.5553 - acc: 0.714 - ETA: 0s - loss: 0.5553 - acc: 0.714 - ETA: 0s - loss: 0.5553 - acc: 0.714 - ETA: 0s - loss: 0.5554 - acc: 0.714 - ETA: 0s - loss: 0.5554 - acc: 0.714 - ETA: 0s - loss: 0.5554 - acc: 0.714 - ETA: 0s - loss: 0.5554 - acc: 0.714 - 15s - loss: 0.5553 - acc: 0.7147 - val_loss: 0.5620 - val_acc: 0.7107
Epoch 9/10
47872/58517 [=======================>......] - ETA: 14s - loss: 0.5436 - acc: 0.71 - ETA: 14s - loss: 0.5489 - acc: 0.72 - ETA: 14s - loss: 0.5692 - acc: 0.70 - ETA: 15s - loss: 0.5731 - acc: 0.71 - ETA: 15s - loss: 0.5608 - acc: 0.71 - ETA: 14s - loss: 0.5527 - acc: 0.72 - ETA: 14s - loss: 0.5497 - acc: 0.72 - ETA: 14s - loss: 0.5457 - acc: 0.72 - ETA: 14s - loss: 0.5498 - acc: 0.72 - ETA: 13s - loss: 0.5524 - acc: 0.72 - ETA: 13s - loss: 0.5499 - acc: 0.71 - ETA: 13s - loss: 0.5540 - acc: 0.71 - ETA: 13s - loss: 0.5555 - acc: 0.71 - ETA: 13s - loss: 0.5549 - acc: 0.71 - ETA: 13s - loss: 0.5552 - acc: 0.71 - ETA: 13s - loss: 0.5549 - acc: 0.71 - ETA: 13s - loss: 0.5546 - acc: 0.71 - ETA: 13s - loss: 0.5547 - acc: 0.71 - ETA: 13s - loss: 0.5551 - acc: 0.71 - ETA: 13s - loss: 0.5548 - acc: 0.71 - ETA: 13s - loss: 0.5537 - acc: 0.71 - ETA: 13s - loss: 0.5551 - acc: 0.71 - ETA: 13s - loss: 0.5534 - acc: 0.71 - ETA: 12s - loss: 0.5523 - acc: 0.71 - ETA: 12s - loss: 0.5528 - acc: 0.71 - ETA: 12s - 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acc: 0.715 - ETA: 2s - loss: 0.5539 - acc: 0.714 - ETA: 2s - loss: 0.5540 - acc: 0.714 - ETA: 2s - loss: 0.5540 - acc: 0.714 - ETA: 2s - loss: 0.5538 - acc: 0.715058517/58517 [==============================] - ETA: 2s - loss: 0.5534 - acc: 0.715 - ETA: 2s - loss: 0.5533 - acc: 0.715 - ETA: 2s - loss: 0.5531 - acc: 0.715 - ETA: 2s - loss: 0.5533 - acc: 0.715 - ETA: 2s - loss: 0.5534 - acc: 0.715 - ETA: 2s - loss: 0.5535 - acc: 0.715 - ETA: 2s - loss: 0.5537 - acc: 0.715 - ETA: 2s - loss: 0.5537 - acc: 0.714 - ETA: 2s - loss: 0.5538 - acc: 0.714 - ETA: 2s - loss: 0.5539 - acc: 0.714 - ETA: 2s - loss: 0.5538 - acc: 0.714 - ETA: 2s - loss: 0.5540 - acc: 0.714 - ETA: 2s - loss: 0.5540 - acc: 0.714 - ETA: 1s - loss: 0.5542 - acc: 0.714 - ETA: 1s - loss: 0.5542 - acc: 0.714 - ETA: 1s - loss: 0.5540 - acc: 0.714 - ETA: 1s - loss: 0.5539 - acc: 0.714 - ETA: 1s - loss: 0.5538 - acc: 0.714 - ETA: 1s - loss: 0.5539 - acc: 0.714 - ETA: 1s - loss: 0.5541 - acc: 0.714 - ETA: 1s - loss: 0.5538 - acc: 0.714 - 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val_acc: 0.7089
Epoch 10/10
49280/58517 [========================>.....] - ETA: 18s - loss: 0.5748 - acc: 0.68 - ETA: 14s - loss: 0.5465 - acc: 0.70 - ETA: 13s - loss: 0.5194 - acc: 0.73 - ETA: 14s - loss: 0.5256 - acc: 0.73 - ETA: 13s - loss: 0.5501 - acc: 0.71 - ETA: 13s - loss: 0.5517 - acc: 0.71 - ETA: 13s - loss: 0.5554 - acc: 0.71 - ETA: 13s - loss: 0.5573 - acc: 0.71 - ETA: 13s - loss: 0.5520 - acc: 0.71 - ETA: 13s - loss: 0.5486 - acc: 0.71 - ETA: 13s - loss: 0.5464 - acc: 0.71 - ETA: 13s - loss: 0.5463 - acc: 0.71 - ETA: 13s - loss: 0.5475 - acc: 0.71 - ETA: 13s - loss: 0.5542 - acc: 0.71 - ETA: 13s - loss: 0.5519 - acc: 0.71 - ETA: 13s - loss: 0.5496 - acc: 0.71 - ETA: 13s - loss: 0.5520 - acc: 0.71 - ETA: 13s - loss: 0.5528 - acc: 0.71 - ETA: 12s - loss: 0.5524 - acc: 0.71 - ETA: 12s - loss: 0.5522 - acc: 0.71 - ETA: 12s - loss: 0.5526 - acc: 0.71 - ETA: 12s - loss: 0.5512 - acc: 0.71 - ETA: 12s - loss: 0.5523 - acc: 0.71 - ETA: 12s - loss: 0.5526 - acc: 0.71 - ETA: 12s - loss: 0.5514 - acc: 0.71 - ETA: 12s - 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In [18]:
n = 0
for i in range(0,28,2):
    n+=(weights[i][0].shape)[1]

In [19]:
x_dnn_train = np.random.random((len(X_train_dnn),n))
start_ind=0
for j in range(X_train_dnn.shape[1]):
    mat = weights[j*2][0]
    dim = mat.shape[1]
    for i in range(X_train_dnn.shape[0]):
        x_dnn_train[i,start_ind:start_ind+dim]=mat[X_train_dnn[i,j]]
    start_ind += dim

In [20]:
x_dnn_test = np.random.random((len(X_test_dnn),n))
start_ind=0
for j in range(X_test_dnn.shape[1]):
    mat = weights[j*2][0]
    dim = mat.shape[1]
    for i in range(x_dnn_test.shape[0]):
        x_dnn_test[i,start_ind:start_ind+dim]=mat[X_test_dnn[i,j]]
    start_ind += dim

In [21]:
from sklearn.linear_model import LogisticRegression
l = LogisticRegression(n_jobs = -1)
r = RandomForestClassifier(n_estimators=100, max_depth= 10, n_jobs=-1)

In [22]:
l.fit(x_dnn_train,target)
y_pred_lr = l.predict_proba(x_dnn_test)[:,1]

In [23]:
y_pred_lr.shape


Out[23]:
(31349,)

In [24]:
r.fit(x_dnn_train,target)
y_pred_rf = r.predict_proba(x_dnn_test)[:,1]

In [25]:
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 [26]:
submit = make_submission(y_pred_rf)

In [27]:
submit.to_csv(f'{PATH}\\AV_Stud\\rf_cat_embedding.csv', index=False)

In [28]:
params = {}
params['booster'] = 'gbtree'
#params['updater'] = 'coord_descent'
params["objective"] = "binary:logistic"
params['eval_metric'] = 'auc'
params["eta"] = 0.05 #0.03
params["subsample"] = .90 #.85 was tried before
params["silent"] = 0
params['verbose'] = 1
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['verbose_eval'] = 50

In [29]:
x_train_final_dnn = np.hstack((x_dnn_train, df_raw[numeric_features].values))
x_test_final_dnn = np.hstack((x_dnn_test, df_test[numeric_features].values))

In [33]:
model_xgb, p_train_xgb, p_test_xgb= mlcrate.xgb.train_kfold(params, x_train_final_dnn, target,x_test_final_dnn\
                                                       , folds = 7, stratify=target, print_imp='final')


[mlcrate] Training 7 stratified XGBoost models on training set (73147, 210) with test set (31349, 210)
[mlcrate] Running fold 0, 62697 train samples, 10450 validation samples
[0]	train-auc:0.801348	valid-auc:0.704596
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:0.82629	valid-auc:0.718576
[2]	train-auc:0.83605	valid-auc:0.722714
[3]	train-auc:0.842477	valid-auc:0.726288
[4]	train-auc:0.847346	valid-auc:0.728381
[5]	train-auc:0.850894	valid-auc:0.730341
[6]	train-auc:0.854576	valid-auc:0.731289
[7]	train-auc:0.856952	valid-auc:0.731565
[8]	train-auc:0.858826	valid-auc:0.73275
[9]	train-auc:0.861137	valid-auc:0.733226
[10]	train-auc:0.863073	valid-auc:0.734136
[11]	train-auc:0.86531	valid-auc:0.734426
[12]	train-auc:0.867175	valid-auc:0.734889
[13]	train-auc:0.869013	valid-auc:0.735064
[14]	train-auc:0.87097	valid-auc:0.735869
[15]	train-auc:0.873033	valid-auc:0.73573
[16]	train-auc:0.874624	valid-auc:0.736456
[17]	train-auc:0.875735	valid-auc:0.737051
[18]	train-auc:0.87711	valid-auc:0.737208
[19]	train-auc:0.878569	valid-auc:0.737624
[20]	train-auc:0.879752	valid-auc:0.737951
[21]	train-auc:0.881277	valid-auc:0.738228
[22]	train-auc:0.882626	valid-auc:0.738205
[23]	train-auc:0.884004	valid-auc:0.738703
[24]	train-auc:0.885521	valid-auc:0.739074
[25]	train-auc:0.887204	valid-auc:0.73933
[26]	train-auc:0.888555	valid-auc:0.739185
[27]	train-auc:0.889506	valid-auc:0.739323
[28]	train-auc:0.891087	valid-auc:0.739385
[29]	train-auc:0.892228	valid-auc:0.739992
[30]	train-auc:0.893585	valid-auc:0.740324
[31]	train-auc:0.895311	valid-auc:0.740582
[32]	train-auc:0.896518	valid-auc:0.740569
[33]	train-auc:0.897951	valid-auc:0.740641
[34]	train-auc:0.899491	valid-auc:0.740822
[35]	train-auc:0.900943	valid-auc:0.740994
[36]	train-auc:0.902416	valid-auc:0.741097
[37]	train-auc:0.903207	valid-auc:0.741304
[38]	train-auc:0.904183	valid-auc:0.741243
[39]	train-auc:0.904918	valid-auc:0.741558
[40]	train-auc:0.905979	valid-auc:0.741673
[41]	train-auc:0.907028	valid-auc:0.741637
[42]	train-auc:0.908089	valid-auc:0.741743
[43]	train-auc:0.909268	valid-auc:0.742071
[44]	train-auc:0.910332	valid-auc:0.742162
[45]	train-auc:0.911784	valid-auc:0.742392
[46]	train-auc:0.912663	valid-auc:0.742537
[47]	train-auc:0.913728	valid-auc:0.742644
[48]	train-auc:0.914792	valid-auc:0.742865
[49]	train-auc:0.915533	valid-auc:0.7429
[50]	train-auc:0.916127	valid-auc:0.743067
[51]	train-auc:0.917094	valid-auc:0.74324
[52]	train-auc:0.917901	valid-auc:0.743235
[53]	train-auc:0.918734	valid-auc:0.743622
[54]	train-auc:0.919923	valid-auc:0.743951
[55]	train-auc:0.920916	valid-auc:0.74428
[56]	train-auc:0.92178	valid-auc:0.744484
[57]	train-auc:0.922709	valid-auc:0.744536
[58]	train-auc:0.92367	valid-auc:0.744895
[59]	train-auc:0.924294	valid-auc:0.745049
[60]	train-auc:0.925407	valid-auc:0.745256
[61]	train-auc:0.926091	valid-auc:0.745328
[62]	train-auc:0.926733	valid-auc:0.745281
[63]	train-auc:0.927436	valid-auc:0.745491
[64]	train-auc:0.928044	valid-auc:0.745752
[65]	train-auc:0.929201	valid-auc:0.745968
[66]	train-auc:0.930066	valid-auc:0.746086
[67]	train-auc:0.930974	valid-auc:0.746115
[68]	train-auc:0.931231	valid-auc:0.746281
[69]	train-auc:0.932224	valid-auc:0.746365
[70]	train-auc:0.933131	valid-auc:0.746472
[71]	train-auc:0.933519	valid-auc:0.746488
[72]	train-auc:0.934923	valid-auc:0.746698
[73]	train-auc:0.935579	valid-auc:0.746794
[74]	train-auc:0.936435	valid-auc:0.747113
[75]	train-auc:0.937432	valid-auc:0.747352
[76]	train-auc:0.938088	valid-auc:0.747416
[77]	train-auc:0.939276	valid-auc:0.747347
[78]	train-auc:0.939812	valid-auc:0.747522
[79]	train-auc:0.940331	valid-auc:0.74763
[80]	train-auc:0.940768	valid-auc:0.747751
[81]	train-auc:0.942052	valid-auc:0.747768
[82]	train-auc:0.943108	valid-auc:0.74826
[83]	train-auc:0.943895	valid-auc:0.748325
[84]	train-auc:0.944392	valid-auc:0.748417
[85]	train-auc:0.94502	valid-auc:0.748625
[86]	train-auc:0.945478	valid-auc:0.748616
[87]	train-auc:0.946224	valid-auc:0.748538
[88]	train-auc:0.946893	valid-auc:0.748545
[89]	train-auc:0.947482	valid-auc:0.748676
[90]	train-auc:0.947666	valid-auc:0.748757
[91]	train-auc:0.9482	valid-auc:0.748832
[92]	train-auc:0.948791	valid-auc:0.749111
[93]	train-auc:0.949275	valid-auc:0.749379
[94]	train-auc:0.949527	valid-auc:0.749513
[95]	train-auc:0.949978	valid-auc:0.749568
[96]	train-auc:0.95057	valid-auc:0.749815
[97]	train-auc:0.951142	valid-auc:0.749965
[98]	train-auc:0.952352	valid-auc:0.750127
[99]	train-auc:0.952711	valid-auc:0.75027
[100]	train-auc:0.953099	valid-auc:0.750413
[101]	train-auc:0.95318	valid-auc:0.750341
[102]	train-auc:0.953499	valid-auc:0.750535
[103]	train-auc:0.95393	valid-auc:0.750502
[104]	train-auc:0.954826	valid-auc:0.750887
[105]	train-auc:0.954918	valid-auc:0.750938
[106]	train-auc:0.955225	valid-auc:0.750933
[107]	train-auc:0.95567	valid-auc:0.750856
[108]	train-auc:0.955752	valid-auc:0.75097
[109]	train-auc:0.956695	valid-auc:0.751116
[110]	train-auc:0.957328	valid-auc:0.751422
[111]	train-auc:0.957602	valid-auc:0.751546
[112]	train-auc:0.958138	valid-auc:0.751719
[113]	train-auc:0.958797	valid-auc:0.751782
[114]	train-auc:0.959194	valid-auc:0.75184
[115]	train-auc:0.959709	valid-auc:0.752112
[116]	train-auc:0.959891	valid-auc:0.752298
[117]	train-auc:0.960161	valid-auc:0.752207
[118]	train-auc:0.96059	valid-auc:0.752103
[119]	train-auc:0.960811	valid-auc:0.752023
[120]	train-auc:0.961562	valid-auc:0.752161
[121]	train-auc:0.962087	valid-auc:0.752315
[122]	train-auc:0.962736	valid-auc:0.752531
[123]	train-auc:0.9628	valid-auc:0.752502
[124]	train-auc:0.963235	valid-auc:0.752563
[125]	train-auc:0.963461	valid-auc:0.752562
[126]	train-auc:0.963658	valid-auc:0.752541
[127]	train-auc:0.964071	valid-auc:0.752692
[128]	train-auc:0.964621	valid-auc:0.752621
[129]	train-auc:0.964966	valid-auc:0.752712
[130]	train-auc:0.965278	valid-auc:0.752775
[131]	train-auc:0.965856	valid-auc:0.75268
[132]	train-auc:0.966335	valid-auc:0.752938
[133]	train-auc:0.966686	valid-auc:0.752951
[134]	train-auc:0.967116	valid-auc:0.753001
[135]	train-auc:0.967727	valid-auc:0.753196
[136]	train-auc:0.967845	valid-auc:0.753177
[137]	train-auc:0.968112	valid-auc:0.753218
[138]	train-auc:0.968246	valid-auc:0.753214
[139]	train-auc:0.968517	valid-auc:0.753318
[140]	train-auc:0.968531	valid-auc:0.753327
[141]	train-auc:0.96885	valid-auc:0.753479
[142]	train-auc:0.968854	valid-auc:0.753476
[143]	train-auc:0.969285	valid-auc:0.753348
[144]	train-auc:0.969488	valid-auc:0.753356
[145]	train-auc:0.970232	valid-auc:0.753767
[146]	train-auc:0.970562	valid-auc:0.753982
[147]	train-auc:0.971073	valid-auc:0.753834
[148]	train-auc:0.971576	valid-auc:0.753889
[149]	train-auc:0.971788	valid-auc:0.753839
[150]	train-auc:0.972277	valid-auc:0.754
[151]	train-auc:0.972455	valid-auc:0.754034
[152]	train-auc:0.972918	valid-auc:0.753967
[153]	train-auc:0.973026	valid-auc:0.753944
[154]	train-auc:0.973358	valid-auc:0.753949
[155]	train-auc:0.973712	valid-auc:0.754165
[156]	train-auc:0.973954	valid-auc:0.75415
[157]	train-auc:0.974316	valid-auc:0.754274
[158]	train-auc:0.974662	valid-auc:0.754469
[159]	train-auc:0.974891	valid-auc:0.75463
[160]	train-auc:0.974923	valid-auc:0.754635
[161]	train-auc:0.975029	valid-auc:0.754639
[162]	train-auc:0.975116	valid-auc:0.754679
[163]	train-auc:0.975191	valid-auc:0.754731
[164]	train-auc:0.975607	valid-auc:0.754734
[165]	train-auc:0.975893	valid-auc:0.754827
[166]	train-auc:0.976068	valid-auc:0.754847
[167]	train-auc:0.976124	valid-auc:0.754879
[168]	train-auc:0.976415	valid-auc:0.754922
[169]	train-auc:0.976599	valid-auc:0.755009
[170]	train-auc:0.976645	valid-auc:0.755047
[171]	train-auc:0.977113	valid-auc:0.75511
[172]	train-auc:0.977262	valid-auc:0.75513
[173]	train-auc:0.9775	valid-auc:0.755088
[174]	train-auc:0.977577	valid-auc:0.755086
[175]	train-auc:0.977764	valid-auc:0.755114
[176]	train-auc:0.977887	valid-auc:0.755192
[177]	train-auc:0.978033	valid-auc:0.755271
[178]	train-auc:0.978241	valid-auc:0.755364
[179]	train-auc:0.978622	valid-auc:0.75537
[180]	train-auc:0.978752	valid-auc:0.755477
[181]	train-auc:0.978859	valid-auc:0.755496
[182]	train-auc:0.97921	valid-auc:0.755685
[183]	train-auc:0.979561	valid-auc:0.755552
[184]	train-auc:0.97967	valid-auc:0.755608
[185]	train-auc:0.979803	valid-auc:0.755584
[186]	train-auc:0.979979	valid-auc:0.755716
[187]	train-auc:0.980592	valid-auc:0.755853
[188]	train-auc:0.980821	valid-auc:0.755879
[189]	train-auc:0.980901	valid-auc:0.755924
[190]	train-auc:0.981108	valid-auc:0.756051
[191]	train-auc:0.981554	valid-auc:0.756219
[192]	train-auc:0.981636	valid-auc:0.756225
[193]	train-auc:0.981744	valid-auc:0.756233
[194]	train-auc:0.982068	valid-auc:0.756224
[195]	train-auc:0.982333	valid-auc:0.756105
[196]	train-auc:0.982531	valid-auc:0.756179
[197]	train-auc:0.98266	valid-auc:0.756167
[198]	train-auc:0.982772	valid-auc:0.756097
[199]	train-auc:0.982984	valid-auc:0.75614
[200]	train-auc:0.983102	valid-auc:0.756094
[201]	train-auc:0.983334	valid-auc:0.756103
[202]	train-auc:0.983606	valid-auc:0.756142
[203]	train-auc:0.983742	valid-auc:0.756044
[204]	train-auc:0.98404	valid-auc:0.755904
[205]	train-auc:0.984332	valid-auc:0.755872
[206]	train-auc:0.984824	valid-auc:0.755964
[207]	train-auc:0.985032	valid-auc:0.756022
[208]	train-auc:0.985247	valid-auc:0.756035
[209]	train-auc:0.985431	valid-auc:0.755991
[210]	train-auc:0.985533	valid-auc:0.756016
[211]	train-auc:0.985725	valid-auc:0.756031
[212]	train-auc:0.985831	valid-auc:0.756058
[213]	train-auc:0.98615	valid-auc:0.755926
[214]	train-auc:0.986249	valid-auc:0.75585
[215]	train-auc:0.986322	valid-auc:0.755907
[216]	train-auc:0.986408	valid-auc:0.755879
[217]	train-auc:0.98648	valid-auc:0.755957
[218]	train-auc:0.986646	valid-auc:0.756084
[219]	train-auc:0.98686	valid-auc:0.756153
[220]	train-auc:0.986972	valid-auc:0.756198
[221]	train-auc:0.987056	valid-auc:0.756339
[222]	train-auc:0.987136	valid-auc:0.756408
[223]	train-auc:0.987374	valid-auc:0.756522
[224]	train-auc:0.987682	valid-auc:0.756366
[225]	train-auc:0.9878	valid-auc:0.756357
[226]	train-auc:0.987995	valid-auc:0.756434
[227]	train-auc:0.988138	valid-auc:0.756584
[228]	train-auc:0.988253	valid-auc:0.756605
[229]	train-auc:0.988347	valid-auc:0.756612
[230]	train-auc:0.988434	valid-auc:0.756608
[231]	train-auc:0.9886	valid-auc:0.756588
[232]	train-auc:0.98873	valid-auc:0.756543
[233]	train-auc:0.988953	valid-auc:0.756446
[234]	train-auc:0.989092	valid-auc:0.756429
[235]	train-auc:0.98913	valid-auc:0.756414
[236]	train-auc:0.989374	valid-auc:0.756547
[237]	train-auc:0.989459	valid-auc:0.756512
[238]	train-auc:0.989521	valid-auc:0.756494
[239]	train-auc:0.989731	valid-auc:0.756616
[240]	train-auc:0.989773	valid-auc:0.756598
[241]	train-auc:0.989963	valid-auc:0.756603
[242]	train-auc:0.990014	valid-auc:0.756622
[243]	train-auc:0.990076	valid-auc:0.756571
[244]	train-auc:0.990294	valid-auc:0.756536
[245]	train-auc:0.990353	valid-auc:0.756506
[246]	train-auc:0.990472	valid-auc:0.756426
[247]	train-auc:0.990665	valid-auc:0.756351
[248]	train-auc:0.990828	valid-auc:0.756512
[249]	train-auc:0.991017	valid-auc:0.756424
[250]	train-auc:0.99124	valid-auc:0.756432
[251]	train-auc:0.991369	valid-auc:0.756519
[252]	train-auc:0.991457	valid-auc:0.756492
[253]	train-auc:0.991658	valid-auc:0.756414
[254]	train-auc:0.991877	valid-auc:0.756383
[255]	train-auc:0.991972	valid-auc:0.756439
[256]	train-auc:0.991989	valid-auc:0.756438
[257]	train-auc:0.992111	valid-auc:0.756447
[258]	train-auc:0.992274	valid-auc:0.756356
[259]	train-auc:0.992418	valid-auc:0.756359
[260]	train-auc:0.992504	valid-auc:0.756442
[261]	train-auc:0.992588	valid-auc:0.756498
[262]	train-auc:0.992657	valid-auc:0.756543
[263]	train-auc:0.992742	valid-auc:0.75655
[264]	train-auc:0.992873	valid-auc:0.756606
[265]	train-auc:0.993004	valid-auc:0.756569
[266]	train-auc:0.99309	valid-auc:0.756601
[267]	train-auc:0.993137	valid-auc:0.756648
[268]	train-auc:0.993329	valid-auc:0.756667
[269]	train-auc:0.993468	valid-auc:0.756743
[270]	train-auc:0.993536	valid-auc:0.756683
[271]	train-auc:0.993608	valid-auc:0.75668
[272]	train-auc:0.993707	valid-auc:0.75684
[273]	train-auc:0.993774	valid-auc:0.756829
[274]	train-auc:0.993847	valid-auc:0.756815
[275]	train-auc:0.993894	valid-auc:0.756832
[276]	train-auc:0.993954	valid-auc:0.756797
[277]	train-auc:0.993959	valid-auc:0.756851
[278]	train-auc:0.994024	valid-auc:0.756944
[279]	train-auc:0.994105	valid-auc:0.756779
[280]	train-auc:0.994268	valid-auc:0.756835
[281]	train-auc:0.994325	valid-auc:0.756811
[282]	train-auc:0.994433	valid-auc:0.756767
[283]	train-auc:0.994492	valid-auc:0.756697
[284]	train-auc:0.994534	valid-auc:0.756743
[285]	train-auc:0.994583	valid-auc:0.756657
[286]	train-auc:0.994717	valid-auc:0.75679
[287]	train-auc:0.994802	valid-auc:0.756868
[288]	train-auc:0.994917	valid-auc:0.756925
[289]	train-auc:0.994983	valid-auc:0.75681
[290]	train-auc:0.995084	valid-auc:0.756851
[291]	train-auc:0.995203	valid-auc:0.756719
[292]	train-auc:0.995255	valid-auc:0.75679
[293]	train-auc:0.995299	valid-auc:0.756831
[294]	train-auc:0.995363	valid-auc:0.756695
[295]	train-auc:0.995484	valid-auc:0.75662
[296]	train-auc:0.995572	valid-auc:0.756616
[297]	train-auc:0.995582	valid-auc:0.756569
[298]	train-auc:0.995662	valid-auc:0.75649
[299]	train-auc:0.995718	valid-auc:0.756685
[300]	train-auc:0.995795	valid-auc:0.756775
[301]	train-auc:0.995888	valid-auc:0.756688
[302]	train-auc:0.995955	valid-auc:0.756728
[303]	train-auc:0.995994	valid-auc:0.75667
[304]	train-auc:0.996041	valid-auc:0.756629
[305]	train-auc:0.996081	valid-auc:0.756621
[306]	train-auc:0.996154	valid-auc:0.756607
[307]	train-auc:0.996209	valid-auc:0.756649
[308]	train-auc:0.996257	valid-auc:0.756713
[309]	train-auc:0.996313	valid-auc:0.756841
[310]	train-auc:0.996356	valid-auc:0.756785
[311]	train-auc:0.996376	valid-auc:0.756807
[312]	train-auc:0.996386	valid-auc:0.75679
[313]	train-auc:0.996399	valid-auc:0.75683
[314]	train-auc:0.996449	valid-auc:0.756775
[315]	train-auc:0.996493	valid-auc:0.756672
[316]	train-auc:0.996534	valid-auc:0.756653
[317]	train-auc:0.996593	valid-auc:0.756684
[318]	train-auc:0.996651	valid-auc:0.75678
[319]	train-auc:0.996724	valid-auc:0.756767
[320]	train-auc:0.996779	valid-auc:0.756783
[321]	train-auc:0.996794	valid-auc:0.756694
[322]	train-auc:0.996829	valid-auc:0.75678
[323]	train-auc:0.996861	valid-auc:0.756766
[324]	train-auc:0.996895	valid-auc:0.756766
[325]	train-auc:0.996921	valid-auc:0.756814
[326]	train-auc:0.996962	valid-auc:0.756726
[327]	train-auc:0.996973	valid-auc:0.756706
[328]	train-auc:0.997038	valid-auc:0.756666
Stopping. Best iteration:
[278]	train-auc:0.994024	valid-auc:0.756944

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 3m18s - running score 0.756944
[mlcrate] Running fold 1, 62697 train samples, 10450 validation samples
[0]	train-auc:0.802126	valid-auc:0.717387
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:0.825796	valid-auc:0.729627
[2]	train-auc:0.834618	valid-auc:0.735753
[3]	train-auc:0.839743	valid-auc:0.73949
[4]	train-auc:0.844474	valid-auc:0.742672
[5]	train-auc:0.84877	valid-auc:0.744647
[6]	train-auc:0.85174	valid-auc:0.74648
[7]	train-auc:0.85457	valid-auc:0.74712
[8]	train-auc:0.856687	valid-auc:0.747697
[9]	train-auc:0.859483	valid-auc:0.747975
[10]	train-auc:0.861243	valid-auc:0.748512
[11]	train-auc:0.862815	valid-auc:0.748757
[12]	train-auc:0.863906	valid-auc:0.7492
[13]	train-auc:0.865851	valid-auc:0.749713
[14]	train-auc:0.868062	valid-auc:0.749515
[15]	train-auc:0.869637	valid-auc:0.749912
[16]	train-auc:0.871771	valid-auc:0.75038
[17]	train-auc:0.872906	valid-auc:0.750563
[18]	train-auc:0.874561	valid-auc:0.750894
[19]	train-auc:0.876673	valid-auc:0.750557
[20]	train-auc:0.877815	valid-auc:0.750926
[21]	train-auc:0.879297	valid-auc:0.75136
[22]	train-auc:0.881041	valid-auc:0.751165
[23]	train-auc:0.882978	valid-auc:0.751392
[24]	train-auc:0.884958	valid-auc:0.751619
[25]	train-auc:0.886282	valid-auc:0.751905
[26]	train-auc:0.887515	valid-auc:0.752024
[27]	train-auc:0.888817	valid-auc:0.752544
[28]	train-auc:0.889943	valid-auc:0.752537
[29]	train-auc:0.891207	valid-auc:0.752987
[30]	train-auc:0.892191	valid-auc:0.753114
[31]	train-auc:0.893148	valid-auc:0.753205
[32]	train-auc:0.894338	valid-auc:0.753349
[33]	train-auc:0.895395	valid-auc:0.7537
[34]	train-auc:0.8971	valid-auc:0.75378
[35]	train-auc:0.89862	valid-auc:0.753985
[36]	train-auc:0.899452	valid-auc:0.754047
[37]	train-auc:0.900648	valid-auc:0.754106
[38]	train-auc:0.901578	valid-auc:0.754035
[39]	train-auc:0.902944	valid-auc:0.754364
[40]	train-auc:0.904249	valid-auc:0.75463
[41]	train-auc:0.905592	valid-auc:0.755029
[42]	train-auc:0.907143	valid-auc:0.755299
[43]	train-auc:0.908329	valid-auc:0.755231
[44]	train-auc:0.909589	valid-auc:0.755296
[45]	train-auc:0.910657	valid-auc:0.755577
[46]	train-auc:0.911749	valid-auc:0.755851
[47]	train-auc:0.912503	valid-auc:0.755894
[48]	train-auc:0.913322	valid-auc:0.756305
[49]	train-auc:0.914224	valid-auc:0.756394
[50]	train-auc:0.915501	valid-auc:0.756657
[51]	train-auc:0.916388	valid-auc:0.756708
[52]	train-auc:0.917304	valid-auc:0.756889
[53]	train-auc:0.918127	valid-auc:0.756903
[54]	train-auc:0.919423	valid-auc:0.757234
[55]	train-auc:0.92027	valid-auc:0.757225
[56]	train-auc:0.921265	valid-auc:0.757388
[57]	train-auc:0.921819	valid-auc:0.757553
[58]	train-auc:0.922643	valid-auc:0.757771
[59]	train-auc:0.923513	valid-auc:0.757831
[60]	train-auc:0.924249	valid-auc:0.758001
[61]	train-auc:0.925495	valid-auc:0.757901
[62]	train-auc:0.926612	valid-auc:0.758148
[63]	train-auc:0.927309	valid-auc:0.758475
[64]	train-auc:0.927895	valid-auc:0.758609
[65]	train-auc:0.928725	valid-auc:0.758964
[66]	train-auc:0.930009	valid-auc:0.759136
[67]	train-auc:0.930839	valid-auc:0.759309
[68]	train-auc:0.931956	valid-auc:0.759366
[69]	train-auc:0.932331	valid-auc:0.75939
[70]	train-auc:0.932898	valid-auc:0.759451
[71]	train-auc:0.933708	valid-auc:0.759653
[72]	train-auc:0.934486	valid-auc:0.759973
[73]	train-auc:0.935577	valid-auc:0.759981
[74]	train-auc:0.936111	valid-auc:0.760024
[75]	train-auc:0.93677	valid-auc:0.760314
[76]	train-auc:0.937532	valid-auc:0.760395
[77]	train-auc:0.938259	valid-auc:0.760472
[78]	train-auc:0.938989	valid-auc:0.760594
[79]	train-auc:0.939513	valid-auc:0.760697
[80]	train-auc:0.939762	valid-auc:0.760841
[81]	train-auc:0.940413	valid-auc:0.760786
[82]	train-auc:0.941108	valid-auc:0.761006
[83]	train-auc:0.941943	valid-auc:0.761015
[84]	train-auc:0.942867	valid-auc:0.761122
[85]	train-auc:0.943335	valid-auc:0.761076
[86]	train-auc:0.943883	valid-auc:0.761055
[87]	train-auc:0.944695	valid-auc:0.761095
[88]	train-auc:0.945709	valid-auc:0.76124
[89]	train-auc:0.94644	valid-auc:0.761806
[90]	train-auc:0.94655	valid-auc:0.761777
[91]	train-auc:0.946995	valid-auc:0.761962
[92]	train-auc:0.947569	valid-auc:0.762082
[93]	train-auc:0.94833	valid-auc:0.761809
[94]	train-auc:0.948888	valid-auc:0.7618
[95]	train-auc:0.949168	valid-auc:0.761805
[96]	train-auc:0.949781	valid-auc:0.761776
[97]	train-auc:0.950418	valid-auc:0.761811
[98]	train-auc:0.950616	valid-auc:0.761855
[99]	train-auc:0.951285	valid-auc:0.761925
[100]	train-auc:0.951886	valid-auc:0.7619
[101]	train-auc:0.952736	valid-auc:0.761974
[102]	train-auc:0.953068	valid-auc:0.761972
[103]	train-auc:0.954005	valid-auc:0.762303
[104]	train-auc:0.954538	valid-auc:0.762679
[105]	train-auc:0.954753	valid-auc:0.762764
[106]	train-auc:0.955246	valid-auc:0.762954
[107]	train-auc:0.955903	valid-auc:0.763177
[108]	train-auc:0.956093	valid-auc:0.76316
[109]	train-auc:0.956447	valid-auc:0.763281
[110]	train-auc:0.956755	valid-auc:0.763402
[111]	train-auc:0.957128	valid-auc:0.763616
[112]	train-auc:0.957742	valid-auc:0.763828
[113]	train-auc:0.957991	valid-auc:0.763882
[114]	train-auc:0.95871	valid-auc:0.7638
[115]	train-auc:0.958963	valid-auc:0.76381
[116]	train-auc:0.959527	valid-auc:0.764028
[117]	train-auc:0.959975	valid-auc:0.763992
[118]	train-auc:0.960582	valid-auc:0.763859
[119]	train-auc:0.960878	valid-auc:0.764006
[120]	train-auc:0.961124	valid-auc:0.76402
[121]	train-auc:0.961375	valid-auc:0.764056
[122]	train-auc:0.961717	valid-auc:0.764004
[123]	train-auc:0.961802	valid-auc:0.764051
[124]	train-auc:0.962044	valid-auc:0.764062
[125]	train-auc:0.963028	valid-auc:0.764462
[126]	train-auc:0.963275	valid-auc:0.764489
[127]	train-auc:0.963625	valid-auc:0.764472
[128]	train-auc:0.96413	valid-auc:0.764632
[129]	train-auc:0.964404	valid-auc:0.764515
[130]	train-auc:0.964833	valid-auc:0.764608
[131]	train-auc:0.965168	valid-auc:0.764618
[132]	train-auc:0.965577	valid-auc:0.764652
[133]	train-auc:0.966165	valid-auc:0.764599
[134]	train-auc:0.966793	valid-auc:0.764843
[135]	train-auc:0.967167	valid-auc:0.764972
[136]	train-auc:0.967657	valid-auc:0.765103
[137]	train-auc:0.96818	valid-auc:0.76518
[138]	train-auc:0.968359	valid-auc:0.765079
[139]	train-auc:0.968817	valid-auc:0.76505
[140]	train-auc:0.969085	valid-auc:0.765029
[141]	train-auc:0.969685	valid-auc:0.765038
[142]	train-auc:0.969834	valid-auc:0.765076
[143]	train-auc:0.970273	valid-auc:0.765116
[144]	train-auc:0.970644	valid-auc:0.765272
[145]	train-auc:0.970988	valid-auc:0.765417
[146]	train-auc:0.971319	valid-auc:0.765567
[147]	train-auc:0.971637	valid-auc:0.765628
[148]	train-auc:0.971748	valid-auc:0.765685
[149]	train-auc:0.972051	valid-auc:0.765764
[150]	train-auc:0.972554	valid-auc:0.765786
[151]	train-auc:0.972766	valid-auc:0.765984
[152]	train-auc:0.973048	valid-auc:0.765966
[153]	train-auc:0.973432	valid-auc:0.765963
[154]	train-auc:0.973709	valid-auc:0.766038
[155]	train-auc:0.973908	valid-auc:0.766008
[156]	train-auc:0.974277	valid-auc:0.765939
[157]	train-auc:0.974555	valid-auc:0.765892
[158]	train-auc:0.974869	valid-auc:0.765969
[159]	train-auc:0.975069	valid-auc:0.766111
[160]	train-auc:0.975217	valid-auc:0.766053
[161]	train-auc:0.975821	valid-auc:0.766044
[162]	train-auc:0.976201	valid-auc:0.766229
[163]	train-auc:0.976448	valid-auc:0.766209
[164]	train-auc:0.976804	valid-auc:0.766375
[165]	train-auc:0.977122	valid-auc:0.766427
[166]	train-auc:0.977446	valid-auc:0.766453
[167]	train-auc:0.977906	valid-auc:0.766326
[168]	train-auc:0.978196	valid-auc:0.766449
[169]	train-auc:0.978498	valid-auc:0.766457
[170]	train-auc:0.978648	valid-auc:0.766492
[171]	train-auc:0.978922	valid-auc:0.766544
[172]	train-auc:0.978961	valid-auc:0.766627
[173]	train-auc:0.979071	valid-auc:0.76662
[174]	train-auc:0.979227	valid-auc:0.766518
[175]	train-auc:0.979469	valid-auc:0.766527
[176]	train-auc:0.979553	valid-auc:0.766598
[177]	train-auc:0.979767	valid-auc:0.766507
[178]	train-auc:0.979802	valid-auc:0.766557
[179]	train-auc:0.979992	valid-auc:0.766578
[180]	train-auc:0.980338	valid-auc:0.766656
[181]	train-auc:0.980496	valid-auc:0.766629
[182]	train-auc:0.980784	valid-auc:0.766636
[183]	train-auc:0.981072	valid-auc:0.766686
[184]	train-auc:0.981349	valid-auc:0.766726
[185]	train-auc:0.981707	valid-auc:0.766507
[186]	train-auc:0.981752	valid-auc:0.766537
[187]	train-auc:0.982009	valid-auc:0.766507
[188]	train-auc:0.982252	valid-auc:0.76647
[189]	train-auc:0.982304	valid-auc:0.766486
[190]	train-auc:0.982685	valid-auc:0.766557
[191]	train-auc:0.982843	valid-auc:0.766549
[192]	train-auc:0.983282	valid-auc:0.766817
[193]	train-auc:0.983736	valid-auc:0.766885
[194]	train-auc:0.984124	valid-auc:0.767017
[195]	train-auc:0.984312	valid-auc:0.767066
[196]	train-auc:0.984448	valid-auc:0.767108
[197]	train-auc:0.984755	valid-auc:0.767043
[198]	train-auc:0.985026	valid-auc:0.766946
[199]	train-auc:0.985181	valid-auc:0.766927
[200]	train-auc:0.98555	valid-auc:0.767132
[201]	train-auc:0.985899	valid-auc:0.766853
[202]	train-auc:0.986258	valid-auc:0.766918
[203]	train-auc:0.986358	valid-auc:0.767029
[204]	train-auc:0.986477	valid-auc:0.767147
[205]	train-auc:0.986769	valid-auc:0.767118
[206]	train-auc:0.98686	valid-auc:0.767152
[207]	train-auc:0.986995	valid-auc:0.766989
[208]	train-auc:0.987048	valid-auc:0.766981
[209]	train-auc:0.987173	valid-auc:0.76688
[210]	train-auc:0.987382	valid-auc:0.766859
[211]	train-auc:0.98753	valid-auc:0.766891
[212]	train-auc:0.987602	valid-auc:0.766952
[213]	train-auc:0.987944	valid-auc:0.76688
[214]	train-auc:0.98813	valid-auc:0.766782
[215]	train-auc:0.988278	valid-auc:0.766763
[216]	train-auc:0.988411	valid-auc:0.766726
[217]	train-auc:0.98843	valid-auc:0.766714
[218]	train-auc:0.98845	valid-auc:0.766731
[219]	train-auc:0.988642	valid-auc:0.766908
[220]	train-auc:0.988866	valid-auc:0.766862
[221]	train-auc:0.989101	valid-auc:0.76699
[222]	train-auc:0.989215	valid-auc:0.767004
[223]	train-auc:0.98926	valid-auc:0.767014
[224]	train-auc:0.989326	valid-auc:0.767037
[225]	train-auc:0.989606	valid-auc:0.767264
[226]	train-auc:0.989724	valid-auc:0.767272
[227]	train-auc:0.989888	valid-auc:0.767443
[228]	train-auc:0.990006	valid-auc:0.767487
[229]	train-auc:0.990185	valid-auc:0.767443
[230]	train-auc:0.990212	valid-auc:0.767412
[231]	train-auc:0.990337	valid-auc:0.767309
[232]	train-auc:0.990469	valid-auc:0.76719
[233]	train-auc:0.990641	valid-auc:0.76726
[234]	train-auc:0.990708	valid-auc:0.767353
[235]	train-auc:0.990875	valid-auc:0.767247
[236]	train-auc:0.991011	valid-auc:0.76711
[237]	train-auc:0.991143	valid-auc:0.767249
[238]	train-auc:0.991235	valid-auc:0.767155
[239]	train-auc:0.991344	valid-auc:0.767161
[240]	train-auc:0.991575	valid-auc:0.767185
[241]	train-auc:0.991693	valid-auc:0.767094
[242]	train-auc:0.991751	valid-auc:0.767085
[243]	train-auc:0.991771	valid-auc:0.767096
[244]	train-auc:0.991914	valid-auc:0.767201
[245]	train-auc:0.992042	valid-auc:0.767298
[246]	train-auc:0.992082	valid-auc:0.767346
[247]	train-auc:0.992194	valid-auc:0.767603
[248]	train-auc:0.992209	valid-auc:0.767645
[249]	train-auc:0.99236	valid-auc:0.76778
[250]	train-auc:0.992387	valid-auc:0.767807
[251]	train-auc:0.992558	valid-auc:0.767667
[252]	train-auc:0.992622	valid-auc:0.767582
[253]	train-auc:0.99281	valid-auc:0.767537
[254]	train-auc:0.992981	valid-auc:0.767551
[255]	train-auc:0.993172	valid-auc:0.767448
[256]	train-auc:0.993242	valid-auc:0.767454
[257]	train-auc:0.993321	valid-auc:0.767411
[258]	train-auc:0.993365	valid-auc:0.767424
[259]	train-auc:0.993464	valid-auc:0.767378
[260]	train-auc:0.993495	valid-auc:0.767368
[261]	train-auc:0.993533	valid-auc:0.767334
[262]	train-auc:0.99361	valid-auc:0.767365
[263]	train-auc:0.993668	valid-auc:0.76742
[264]	train-auc:0.993743	valid-auc:0.76752
[265]	train-auc:0.993869	valid-auc:0.767457
[266]	train-auc:0.993935	valid-auc:0.767424
[267]	train-auc:0.99399	valid-auc:0.767272
[268]	train-auc:0.99413	valid-auc:0.767356
[269]	train-auc:0.994227	valid-auc:0.767326
[270]	train-auc:0.994279	valid-auc:0.767421
[271]	train-auc:0.994341	valid-auc:0.767397
[272]	train-auc:0.99437	valid-auc:0.767403
[273]	train-auc:0.994405	valid-auc:0.76742
[274]	train-auc:0.994516	valid-auc:0.76748
[275]	train-auc:0.994557	valid-auc:0.767406
[276]	train-auc:0.994692	valid-auc:0.767447
[277]	train-auc:0.994823	valid-auc:0.767387
[278]	train-auc:0.994889	valid-auc:0.767456
[279]	train-auc:0.994975	valid-auc:0.767356
[280]	train-auc:0.995106	valid-auc:0.767349
[281]	train-auc:0.995139	valid-auc:0.767347
[282]	train-auc:0.995207	valid-auc:0.767347
[283]	train-auc:0.995252	valid-auc:0.767385
[284]	train-auc:0.995314	valid-auc:0.767272
[285]	train-auc:0.995352	valid-auc:0.76726
[286]	train-auc:0.995413	valid-auc:0.767216
[287]	train-auc:0.995475	valid-auc:0.767368
[288]	train-auc:0.995529	valid-auc:0.767328
[289]	train-auc:0.995572	valid-auc:0.767374
[290]	train-auc:0.995621	valid-auc:0.767409
[291]	train-auc:0.995688	valid-auc:0.767513
[292]	train-auc:0.995741	valid-auc:0.767481
[293]	train-auc:0.99584	valid-auc:0.767647
[294]	train-auc:0.995879	valid-auc:0.76769
[295]	train-auc:0.995925	valid-auc:0.767688
[296]	train-auc:0.995951	valid-auc:0.767778
[297]	train-auc:0.996028	valid-auc:0.767773
[298]	train-auc:0.996048	valid-auc:0.767711
[299]	train-auc:0.996085	valid-auc:0.76766
[300]	train-auc:0.996215	valid-auc:0.767715
Stopping. Best iteration:
[250]	train-auc:0.992387	valid-auc:0.767807

[mlcrate] Finished training fold 1 - took 2m55s - running score 0.7623755
[mlcrate] Running fold 2, 62697 train samples, 10450 validation samples
[0]	train-auc:0.805073	valid-auc:0.69177
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:0.827105	valid-auc:0.709779
[2]	train-auc:0.836321	valid-auc:0.714968
[3]	train-auc:0.840961	valid-auc:0.719907
[4]	train-auc:0.845937	valid-auc:0.723375
[5]	train-auc:0.848936	valid-auc:0.725736
[6]	train-auc:0.85165	valid-auc:0.727566
[7]	train-auc:0.854224	valid-auc:0.728727
[8]	train-auc:0.856986	valid-auc:0.730018
[9]	train-auc:0.859612	valid-auc:0.730488
[10]	train-auc:0.861386	valid-auc:0.730889
[11]	train-auc:0.863258	valid-auc:0.731624
[12]	train-auc:0.865097	valid-auc:0.732323
[13]	train-auc:0.866073	valid-auc:0.732908
[14]	train-auc:0.867408	valid-auc:0.732912
[15]	train-auc:0.869214	valid-auc:0.733153
[16]	train-auc:0.870649	valid-auc:0.733664
[17]	train-auc:0.872007	valid-auc:0.734101
[18]	train-auc:0.872987	valid-auc:0.734099
[19]	train-auc:0.874393	valid-auc:0.734775
[20]	train-auc:0.876324	valid-auc:0.735064
[21]	train-auc:0.877546	valid-auc:0.735354
[22]	train-auc:0.878722	valid-auc:0.736022
[23]	train-auc:0.880649	valid-auc:0.736224
[24]	train-auc:0.882841	valid-auc:0.736784
[25]	train-auc:0.884496	valid-auc:0.736888
[26]	train-auc:0.886061	valid-auc:0.73698
[27]	train-auc:0.887134	valid-auc:0.73703
[28]	train-auc:0.888428	valid-auc:0.737646
[29]	train-auc:0.88957	valid-auc:0.737904
[30]	train-auc:0.890585	valid-auc:0.738422
[31]	train-auc:0.891787	valid-auc:0.738531
[32]	train-auc:0.892926	valid-auc:0.7385
[33]	train-auc:0.894225	valid-auc:0.738747
[34]	train-auc:0.895544	valid-auc:0.738817
[35]	train-auc:0.897363	valid-auc:0.739064
[36]	train-auc:0.899324	valid-auc:0.739442
[37]	train-auc:0.900503	valid-auc:0.739483
[38]	train-auc:0.901434	valid-auc:0.73957
[39]	train-auc:0.902738	valid-auc:0.739754
[40]	train-auc:0.903734	valid-auc:0.739989
[41]	train-auc:0.904943	valid-auc:0.74018
[42]	train-auc:0.906154	valid-auc:0.740384
[43]	train-auc:0.90724	valid-auc:0.740519
[44]	train-auc:0.908183	valid-auc:0.740562
[45]	train-auc:0.909289	valid-auc:0.740817
[46]	train-auc:0.910461	valid-auc:0.741035
[47]	train-auc:0.911485	valid-auc:0.740905
[48]	train-auc:0.912527	valid-auc:0.740979
[49]	train-auc:0.913632	valid-auc:0.741157
[50]	train-auc:0.914774	valid-auc:0.741297
[51]	train-auc:0.915728	valid-auc:0.741268
[52]	train-auc:0.916533	valid-auc:0.741725
[53]	train-auc:0.917436	valid-auc:0.741966
[54]	train-auc:0.918648	valid-auc:0.742314
[55]	train-auc:0.91941	valid-auc:0.742531
[56]	train-auc:0.920387	valid-auc:0.742501
[57]	train-auc:0.921454	valid-auc:0.742601
[58]	train-auc:0.9229	valid-auc:0.742601
[59]	train-auc:0.923551	valid-auc:0.742585
[60]	train-auc:0.924115	valid-auc:0.742681
[61]	train-auc:0.924996	valid-auc:0.742947
[62]	train-auc:0.926428	valid-auc:0.743148
[63]	train-auc:0.927596	valid-auc:0.743457
[64]	train-auc:0.928686	valid-auc:0.743665
[65]	train-auc:0.929443	valid-auc:0.743976
[66]	train-auc:0.929798	valid-auc:0.743991
[67]	train-auc:0.930357	valid-auc:0.744149
[68]	train-auc:0.931347	valid-auc:0.744253
[69]	train-auc:0.932157	valid-auc:0.744402
[70]	train-auc:0.932961	valid-auc:0.744414
[71]	train-auc:0.93376	valid-auc:0.744718
[72]	train-auc:0.934281	valid-auc:0.744756
[73]	train-auc:0.934854	valid-auc:0.744694
[74]	train-auc:0.936012	valid-auc:0.745236
[75]	train-auc:0.936989	valid-auc:0.745413
[76]	train-auc:0.937532	valid-auc:0.745524
[77]	train-auc:0.93814	valid-auc:0.745622
[78]	train-auc:0.938396	valid-auc:0.745625
[79]	train-auc:0.939328	valid-auc:0.746147
[80]	train-auc:0.939979	valid-auc:0.746452
[81]	train-auc:0.940659	valid-auc:0.746495
[82]	train-auc:0.941189	valid-auc:0.746606
[83]	train-auc:0.941736	valid-auc:0.746655
[84]	train-auc:0.94231	valid-auc:0.746984
[85]	train-auc:0.942686	valid-auc:0.746987
[86]	train-auc:0.943716	valid-auc:0.747607
[87]	train-auc:0.94433	valid-auc:0.747657
[88]	train-auc:0.945463	valid-auc:0.748091
[89]	train-auc:0.946115	valid-auc:0.748226
[90]	train-auc:0.94649	valid-auc:0.748454
[91]	train-auc:0.946774	valid-auc:0.748488
[92]	train-auc:0.947213	valid-auc:0.748485
[93]	train-auc:0.947651	valid-auc:0.74868
[94]	train-auc:0.948366	valid-auc:0.748866
[95]	train-auc:0.948552	valid-auc:0.748807
[96]	train-auc:0.949173	valid-auc:0.749039
[97]	train-auc:0.949958	valid-auc:0.749078
[98]	train-auc:0.950473	valid-auc:0.749286
[99]	train-auc:0.950881	valid-auc:0.749264
[100]	train-auc:0.951524	valid-auc:0.74937
[101]	train-auc:0.951879	valid-auc:0.749376
[102]	train-auc:0.95208	valid-auc:0.74955
[103]	train-auc:0.952435	valid-auc:0.749632
[104]	train-auc:0.952649	valid-auc:0.749634
[105]	train-auc:0.953026	valid-auc:0.74968
[106]	train-auc:0.953288	valid-auc:0.749781
[107]	train-auc:0.953659	valid-auc:0.750066
[108]	train-auc:0.95417	valid-auc:0.750174
[109]	train-auc:0.954637	valid-auc:0.750288
[110]	train-auc:0.955561	valid-auc:0.750196
[111]	train-auc:0.956204	valid-auc:0.75037
[112]	train-auc:0.956799	valid-auc:0.750504
[113]	train-auc:0.956873	valid-auc:0.750617
[114]	train-auc:0.957732	valid-auc:0.750898
[115]	train-auc:0.958111	valid-auc:0.751006
[116]	train-auc:0.958523	valid-auc:0.751051
[117]	train-auc:0.958983	valid-auc:0.750989
[118]	train-auc:0.95922	valid-auc:0.750935
[119]	train-auc:0.960076	valid-auc:0.750964
[120]	train-auc:0.961071	valid-auc:0.750991
[121]	train-auc:0.961639	valid-auc:0.751032
[122]	train-auc:0.962299	valid-auc:0.750952
[123]	train-auc:0.962646	valid-auc:0.751093
[124]	train-auc:0.963476	valid-auc:0.751104
[125]	train-auc:0.96384	valid-auc:0.751141
[126]	train-auc:0.96441	valid-auc:0.751119
[127]	train-auc:0.964442	valid-auc:0.751161
[128]	train-auc:0.96447	valid-auc:0.751138
[129]	train-auc:0.96453	valid-auc:0.751104
[130]	train-auc:0.96536	valid-auc:0.751153
[131]	train-auc:0.966048	valid-auc:0.751095
[132]	train-auc:0.966825	valid-auc:0.751148
[133]	train-auc:0.967335	valid-auc:0.751189
[134]	train-auc:0.967695	valid-auc:0.751388
[135]	train-auc:0.968183	valid-auc:0.751496
[136]	train-auc:0.968392	valid-auc:0.751515
[137]	train-auc:0.968903	valid-auc:0.75148
[138]	train-auc:0.969018	valid-auc:0.751416
[139]	train-auc:0.969565	valid-auc:0.751575
[140]	train-auc:0.970047	valid-auc:0.751923
[141]	train-auc:0.970467	valid-auc:0.752022
[142]	train-auc:0.971042	valid-auc:0.752275
[143]	train-auc:0.971214	valid-auc:0.752153
[144]	train-auc:0.971252	valid-auc:0.75218
[145]	train-auc:0.971289	valid-auc:0.752198
[146]	train-auc:0.971448	valid-auc:0.752319
[147]	train-auc:0.971579	valid-auc:0.752267
[148]	train-auc:0.971899	valid-auc:0.752386
[149]	train-auc:0.97248	valid-auc:0.752368
[150]	train-auc:0.972903	valid-auc:0.752478
[151]	train-auc:0.973194	valid-auc:0.752485
[152]	train-auc:0.97356	valid-auc:0.752439
[153]	train-auc:0.973693	valid-auc:0.752587
[154]	train-auc:0.974104	valid-auc:0.752591
[155]	train-auc:0.974307	valid-auc:0.75276
[156]	train-auc:0.974686	valid-auc:0.752729
[157]	train-auc:0.974978	valid-auc:0.752792
[158]	train-auc:0.975309	valid-auc:0.752805
[159]	train-auc:0.975848	valid-auc:0.752706
[160]	train-auc:0.976201	valid-auc:0.752995
[161]	train-auc:0.976465	valid-auc:0.752995
[162]	train-auc:0.976574	valid-auc:0.753062
[163]	train-auc:0.97668	valid-auc:0.753145
[164]	train-auc:0.976934	valid-auc:0.753
[165]	train-auc:0.977194	valid-auc:0.752958
[166]	train-auc:0.977541	valid-auc:0.752933
[167]	train-auc:0.977819	valid-auc:0.752738
[168]	train-auc:0.978026	valid-auc:0.752821
[169]	train-auc:0.978166	valid-auc:0.752738
[170]	train-auc:0.978313	valid-auc:0.752723
[171]	train-auc:0.978591	valid-auc:0.752612
[172]	train-auc:0.978855	valid-auc:0.752769
[173]	train-auc:0.9791	valid-auc:0.752879
[174]	train-auc:0.979362	valid-auc:0.752969
[175]	train-auc:0.979697	valid-auc:0.753022
[176]	train-auc:0.97999	valid-auc:0.753155
[177]	train-auc:0.980189	valid-auc:0.753049
[178]	train-auc:0.980457	valid-auc:0.753003
[179]	train-auc:0.980565	valid-auc:0.752886
[180]	train-auc:0.980723	valid-auc:0.752852
[181]	train-auc:0.980765	valid-auc:0.752846
[182]	train-auc:0.981138	valid-auc:0.753092
[183]	train-auc:0.981209	valid-auc:0.753086
[184]	train-auc:0.981588	valid-auc:0.753044
[185]	train-auc:0.981795	valid-auc:0.752882
[186]	train-auc:0.981964	valid-auc:0.752869
[187]	train-auc:0.982533	valid-auc:0.75276
[188]	train-auc:0.982849	valid-auc:0.752713
[189]	train-auc:0.983041	valid-auc:0.752865
[190]	train-auc:0.983211	valid-auc:0.752927
[191]	train-auc:0.983331	valid-auc:0.752985
[192]	train-auc:0.983586	valid-auc:0.752934
[193]	train-auc:0.983672	valid-auc:0.752999
[194]	train-auc:0.98373	valid-auc:0.753124
[195]	train-auc:0.983884	valid-auc:0.753227
[196]	train-auc:0.984014	valid-auc:0.753309
[197]	train-auc:0.984401	valid-auc:0.753318
[198]	train-auc:0.984747	valid-auc:0.753179
[199]	train-auc:0.98493	valid-auc:0.753181
[200]	train-auc:0.985107	valid-auc:0.753187
[201]	train-auc:0.985238	valid-auc:0.753181
[202]	train-auc:0.985369	valid-auc:0.753139
[203]	train-auc:0.985465	valid-auc:0.753151
[204]	train-auc:0.985658	valid-auc:0.753209
[205]	train-auc:0.985923	valid-auc:0.753241
[206]	train-auc:0.986177	valid-auc:0.753368
[207]	train-auc:0.986582	valid-auc:0.753189
[208]	train-auc:0.986665	valid-auc:0.753125
[209]	train-auc:0.986801	valid-auc:0.753113
[210]	train-auc:0.98705	valid-auc:0.753071
[211]	train-auc:0.987293	valid-auc:0.753111
[212]	train-auc:0.987355	valid-auc:0.753044
[213]	train-auc:0.98759	valid-auc:0.753033
[214]	train-auc:0.987702	valid-auc:0.75298
[215]	train-auc:0.987712	valid-auc:0.753009
[216]	train-auc:0.987848	valid-auc:0.753013
[217]	train-auc:0.987883	valid-auc:0.753051
[218]	train-auc:0.987996	valid-auc:0.753246
[219]	train-auc:0.988108	valid-auc:0.753233
[220]	train-auc:0.988255	valid-auc:0.753426
[221]	train-auc:0.988387	valid-auc:0.753513
[222]	train-auc:0.988453	valid-auc:0.753553
[223]	train-auc:0.988592	valid-auc:0.753672
[224]	train-auc:0.988789	valid-auc:0.753674
[225]	train-auc:0.988883	valid-auc:0.75376
[226]	train-auc:0.988967	valid-auc:0.753763
[227]	train-auc:0.989124	valid-auc:0.753904
[228]	train-auc:0.989309	valid-auc:0.753884
[229]	train-auc:0.989339	valid-auc:0.753878
[230]	train-auc:0.989345	valid-auc:0.753884
[231]	train-auc:0.989408	valid-auc:0.753939
[232]	train-auc:0.989551	valid-auc:0.75378
[233]	train-auc:0.989719	valid-auc:0.753975
[234]	train-auc:0.989764	valid-auc:0.75392
[235]	train-auc:0.989825	valid-auc:0.75395
[236]	train-auc:0.989929	valid-auc:0.75392
[237]	train-auc:0.990067	valid-auc:0.754008
[238]	train-auc:0.990238	valid-auc:0.753837
[239]	train-auc:0.990371	valid-auc:0.753837
[240]	train-auc:0.990506	valid-auc:0.753713
[241]	train-auc:0.990708	valid-auc:0.753617
[242]	train-auc:0.990825	valid-auc:0.753866
[243]	train-auc:0.990972	valid-auc:0.753781
[244]	train-auc:0.991101	valid-auc:0.753831
[245]	train-auc:0.991148	valid-auc:0.753832
[246]	train-auc:0.991241	valid-auc:0.753825
[247]	train-auc:0.991308	valid-auc:0.753838
[248]	train-auc:0.991498	valid-auc:0.754005
[249]	train-auc:0.991798	valid-auc:0.753899
[250]	train-auc:0.991827	valid-auc:0.753854
[251]	train-auc:0.991945	valid-auc:0.753909
[252]	train-auc:0.991976	valid-auc:0.753904
[253]	train-auc:0.99201	valid-auc:0.753939
[254]	train-auc:0.992175	valid-auc:0.754056
[255]	train-auc:0.992264	valid-auc:0.754103
[256]	train-auc:0.992379	valid-auc:0.754023
[257]	train-auc:0.992532	valid-auc:0.753986
[258]	train-auc:0.992689	valid-auc:0.753868
[259]	train-auc:0.992732	valid-auc:0.753846
[260]	train-auc:0.992899	valid-auc:0.753892
[261]	train-auc:0.993002	valid-auc:0.753955
[262]	train-auc:0.99302	valid-auc:0.753995
[263]	train-auc:0.993025	valid-auc:0.753997
[264]	train-auc:0.993205	valid-auc:0.753985
[265]	train-auc:0.993252	valid-auc:0.754022
[266]	train-auc:0.993371	valid-auc:0.754087
[267]	train-auc:0.993506	valid-auc:0.753946
[268]	train-auc:0.993538	valid-auc:0.753912
[269]	train-auc:0.993612	valid-auc:0.753964
[270]	train-auc:0.99379	valid-auc:0.753867
[271]	train-auc:0.993832	valid-auc:0.753959
[272]	train-auc:0.993958	valid-auc:0.753894
[273]	train-auc:0.993981	valid-auc:0.753911
[274]	train-auc:0.994061	valid-auc:0.753779
[275]	train-auc:0.994146	valid-auc:0.753811
[276]	train-auc:0.994178	valid-auc:0.75385
[277]	train-auc:0.994227	valid-auc:0.7539
[278]	train-auc:0.994368	valid-auc:0.753932
[279]	train-auc:0.994416	valid-auc:0.753852
[280]	train-auc:0.994437	valid-auc:0.753839
[281]	train-auc:0.994492	valid-auc:0.753847
[282]	train-auc:0.994565	valid-auc:0.753693
[283]	train-auc:0.994585	valid-auc:0.753768
[284]	train-auc:0.994642	valid-auc:0.753756
[285]	train-auc:0.994729	valid-auc:0.753758
[286]	train-auc:0.994826	valid-auc:0.753778
[287]	train-auc:0.99485	valid-auc:0.753739
[288]	train-auc:0.994988	valid-auc:0.753734
[289]	train-auc:0.995071	valid-auc:0.753704
[290]	train-auc:0.99509	valid-auc:0.753742
[291]	train-auc:0.995149	valid-auc:0.753862
[292]	train-auc:0.995191	valid-auc:0.753814
[293]	train-auc:0.995303	valid-auc:0.753949
[294]	train-auc:0.995411	valid-auc:0.754107
[295]	train-auc:0.995512	valid-auc:0.754184
[296]	train-auc:0.995537	valid-auc:0.754196
[297]	train-auc:0.995557	valid-auc:0.754193
[298]	train-auc:0.99557	valid-auc:0.754211
[299]	train-auc:0.995605	valid-auc:0.754328
[300]	train-auc:0.995636	valid-auc:0.754326
[301]	train-auc:0.995731	valid-auc:0.754246
[302]	train-auc:0.995795	valid-auc:0.754282
[303]	train-auc:0.995895	valid-auc:0.754273
[304]	train-auc:0.995923	valid-auc:0.754239
[305]	train-auc:0.996009	valid-auc:0.754083
[306]	train-auc:0.996062	valid-auc:0.754067
[307]	train-auc:0.996098	valid-auc:0.754025
[308]	train-auc:0.996131	valid-auc:0.754015
[309]	train-auc:0.996165	valid-auc:0.75408
[310]	train-auc:0.996251	valid-auc:0.754168
[311]	train-auc:0.996295	valid-auc:0.754125
[312]	train-auc:0.996346	valid-auc:0.754118
[313]	train-auc:0.996424	valid-auc:0.754055
[314]	train-auc:0.996473	valid-auc:0.754103
[315]	train-auc:0.996483	valid-auc:0.75416
[316]	train-auc:0.996528	valid-auc:0.75411
[317]	train-auc:0.996579	valid-auc:0.754089
[318]	train-auc:0.996624	valid-auc:0.75409
[319]	train-auc:0.996648	valid-auc:0.754153
[320]	train-auc:0.996707	valid-auc:0.754172
[321]	train-auc:0.996777	valid-auc:0.754165
[322]	train-auc:0.996899	valid-auc:0.754201
[323]	train-auc:0.996975	valid-auc:0.754246
[324]	train-auc:0.99702	valid-auc:0.754266
[325]	train-auc:0.997044	valid-auc:0.754247
[326]	train-auc:0.997073	valid-auc:0.754271
[327]	train-auc:0.99708	valid-auc:0.754275
[328]	train-auc:0.997123	valid-auc:0.754413
[329]	train-auc:0.997163	valid-auc:0.754356
[330]	train-auc:0.997176	valid-auc:0.754354
[331]	train-auc:0.99721	valid-auc:0.754326
[332]	train-auc:0.997258	valid-auc:0.75446
[333]	train-auc:0.99728	valid-auc:0.754429
[334]	train-auc:0.997319	valid-auc:0.754434
[335]	train-auc:0.997348	valid-auc:0.754546
[336]	train-auc:0.997402	valid-auc:0.754436
[337]	train-auc:0.997455	valid-auc:0.754324
[338]	train-auc:0.997468	valid-auc:0.754258
[339]	train-auc:0.997473	valid-auc:0.754284
[340]	train-auc:0.997511	valid-auc:0.754232
[341]	train-auc:0.997566	valid-auc:0.754231
[342]	train-auc:0.997662	valid-auc:0.754377
[343]	train-auc:0.997715	valid-auc:0.754318
[344]	train-auc:0.99777	valid-auc:0.754319
[345]	train-auc:0.997779	valid-auc:0.754329
[346]	train-auc:0.997809	valid-auc:0.754361
[347]	train-auc:0.99783	valid-auc:0.754415
[348]	train-auc:0.99786	valid-auc:0.75458
[349]	train-auc:0.99792	valid-auc:0.754635
[350]	train-auc:0.997952	valid-auc:0.754663
[351]	train-auc:0.997976	valid-auc:0.754673
[352]	train-auc:0.998001	valid-auc:0.754614
[353]	train-auc:0.998043	valid-auc:0.754624
[354]	train-auc:0.998049	valid-auc:0.754587
[355]	train-auc:0.998059	valid-auc:0.754581
[356]	train-auc:0.998078	valid-auc:0.754547
[357]	train-auc:0.998104	valid-auc:0.754581
[358]	train-auc:0.998128	valid-auc:0.754487
[359]	train-auc:0.998169	valid-auc:0.754471
[360]	train-auc:0.998194	valid-auc:0.754458
[361]	train-auc:0.998234	valid-auc:0.754442
[362]	train-auc:0.998265	valid-auc:0.754424
[363]	train-auc:0.99829	valid-auc:0.754433
[364]	train-auc:0.998313	valid-auc:0.754435
[365]	train-auc:0.998351	valid-auc:0.754537
[366]	train-auc:0.998369	valid-auc:0.754566
[367]	train-auc:0.998386	valid-auc:0.754662
[368]	train-auc:0.998413	valid-auc:0.754679
[369]	train-auc:0.998442	valid-auc:0.754651
[370]	train-auc:0.998464	valid-auc:0.754581
[371]	train-auc:0.998481	valid-auc:0.75461
[372]	train-auc:0.99852	valid-auc:0.75453
[373]	train-auc:0.998562	valid-auc:0.754531
[374]	train-auc:0.998587	valid-auc:0.754424
[375]	train-auc:0.998622	valid-auc:0.754419
[376]	train-auc:0.99864	valid-auc:0.754404
[377]	train-auc:0.998648	valid-auc:0.754424
[378]	train-auc:0.998659	valid-auc:0.754517
[379]	train-auc:0.998691	valid-auc:0.75453
[380]	train-auc:0.998716	valid-auc:0.754553
[381]	train-auc:0.998727	valid-auc:0.754597
[382]	train-auc:0.998742	valid-auc:0.754614
[383]	train-auc:0.998757	valid-auc:0.754554
[384]	train-auc:0.998766	valid-auc:0.754533
[385]	train-auc:0.998811	valid-auc:0.754552
[386]	train-auc:0.998817	valid-auc:0.754529
[387]	train-auc:0.998848	valid-auc:0.754592
[388]	train-auc:0.998866	valid-auc:0.754476
[389]	train-auc:0.998887	valid-auc:0.754526
[390]	train-auc:0.9989	valid-auc:0.754571
[391]	train-auc:0.998922	valid-auc:0.754628
[392]	train-auc:0.998935	valid-auc:0.754747
[393]	train-auc:0.998946	valid-auc:0.754766
[394]	train-auc:0.998969	valid-auc:0.754837
[395]	train-auc:0.998989	valid-auc:0.754818
[396]	train-auc:0.999008	valid-auc:0.754821
[397]	train-auc:0.999015	valid-auc:0.754873
[398]	train-auc:0.999038	valid-auc:0.754799
[399]	train-auc:0.999042	valid-auc:0.754831
[400]	train-auc:0.99905	valid-auc:0.754847
[401]	train-auc:0.999059	valid-auc:0.754817
[402]	train-auc:0.999062	valid-auc:0.754816
[403]	train-auc:0.999081	valid-auc:0.754844
[404]	train-auc:0.999085	valid-auc:0.754888
[405]	train-auc:0.999097	valid-auc:0.754872
[406]	train-auc:0.999116	valid-auc:0.754864
[407]	train-auc:0.999118	valid-auc:0.754919
[408]	train-auc:0.999127	valid-auc:0.754809
[409]	train-auc:0.999139	valid-auc:0.754834
[410]	train-auc:0.999144	valid-auc:0.754894
[411]	train-auc:0.999156	valid-auc:0.754972
[412]	train-auc:0.999157	valid-auc:0.754969
[413]	train-auc:0.999171	valid-auc:0.755002
[414]	train-auc:0.999184	valid-auc:0.754926
[415]	train-auc:0.999184	valid-auc:0.754936
[416]	train-auc:0.99919	valid-auc:0.754961
[417]	train-auc:0.99921	valid-auc:0.754897
[418]	train-auc:0.999231	valid-auc:0.754934
[419]	train-auc:0.999238	valid-auc:0.754873
[420]	train-auc:0.999251	valid-auc:0.754885
[421]	train-auc:0.999261	valid-auc:0.754835
[422]	train-auc:0.999269	valid-auc:0.754841
[423]	train-auc:0.999278	valid-auc:0.754851
[424]	train-auc:0.999283	valid-auc:0.754963
[425]	train-auc:0.99929	valid-auc:0.754916
[426]	train-auc:0.9993	valid-auc:0.754927
[427]	train-auc:0.999305	valid-auc:0.75485
[428]	train-auc:0.999324	valid-auc:0.75497
[429]	train-auc:0.999338	valid-auc:0.754974
[430]	train-auc:0.999345	valid-auc:0.75494
[431]	train-auc:0.999352	valid-auc:0.754988
[432]	train-auc:0.999353	valid-auc:0.755017
[433]	train-auc:0.999355	valid-auc:0.755054
[434]	train-auc:0.999363	valid-auc:0.755035
[435]	train-auc:0.999378	valid-auc:0.755124
[436]	train-auc:0.999392	valid-auc:0.755166
[437]	train-auc:0.999395	valid-auc:0.755157
[438]	train-auc:0.999403	valid-auc:0.755196
[439]	train-auc:0.999409	valid-auc:0.755165
[440]	train-auc:0.999422	valid-auc:0.755135
[441]	train-auc:0.999431	valid-auc:0.755224
[442]	train-auc:0.999445	valid-auc:0.755197
[443]	train-auc:0.999456	valid-auc:0.755105
[444]	train-auc:0.999467	valid-auc:0.755125
[445]	train-auc:0.999473	valid-auc:0.755114
[446]	train-auc:0.999479	valid-auc:0.755108
[447]	train-auc:0.999485	valid-auc:0.755226
[448]	train-auc:0.999496	valid-auc:0.755145
[449]	train-auc:0.999508	valid-auc:0.755105
[450]	train-auc:0.99952	valid-auc:0.755045
[451]	train-auc:0.999532	valid-auc:0.755121
[452]	train-auc:0.999534	valid-auc:0.755133
[453]	train-auc:0.999545	valid-auc:0.755161
[454]	train-auc:0.999559	valid-auc:0.755199
[455]	train-auc:0.999572	valid-auc:0.755222
[456]	train-auc:0.999575	valid-auc:0.75518
[457]	train-auc:0.999582	valid-auc:0.755143
[458]	train-auc:0.99959	valid-auc:0.755257
[459]	train-auc:0.999598	valid-auc:0.755148
[460]	train-auc:0.999608	valid-auc:0.755074
[461]	train-auc:0.999619	valid-auc:0.754962
[462]	train-auc:0.999621	valid-auc:0.754943
[463]	train-auc:0.999626	valid-auc:0.754896
[464]	train-auc:0.999631	valid-auc:0.754824
[465]	train-auc:0.999633	valid-auc:0.754851
[466]	train-auc:0.999637	valid-auc:0.754811
[467]	train-auc:0.999645	valid-auc:0.754758
[468]	train-auc:0.999653	valid-auc:0.754813
[469]	train-auc:0.999657	valid-auc:0.754755
[470]	train-auc:0.99966	valid-auc:0.754735
[471]	train-auc:0.99967	valid-auc:0.754627
[472]	train-auc:0.999675	valid-auc:0.754654
[473]	train-auc:0.999677	valid-auc:0.754689
[474]	train-auc:0.999685	valid-auc:0.754712
[475]	train-auc:0.999689	valid-auc:0.754684
[476]	train-auc:0.99969	valid-auc:0.754677
[477]	train-auc:0.999696	valid-auc:0.754652
[478]	train-auc:0.999698	valid-auc:0.754678
[479]	train-auc:0.999701	valid-auc:0.754714
[480]	train-auc:0.999704	valid-auc:0.754699
[481]	train-auc:0.999711	valid-auc:0.754714
[482]	train-auc:0.999714	valid-auc:0.754807
[483]	train-auc:0.999715	valid-auc:0.754828
[484]	train-auc:0.99972	valid-auc:0.754866
[485]	train-auc:0.999723	valid-auc:0.754929
[486]	train-auc:0.999724	valid-auc:0.754955
[487]	train-auc:0.99973	valid-auc:0.75487
[488]	train-auc:0.999735	valid-auc:0.754896
[489]	train-auc:0.999745	valid-auc:0.754817
[490]	train-auc:0.999749	valid-auc:0.754814
[491]	train-auc:0.999752	valid-auc:0.754848
[492]	train-auc:0.999759	valid-auc:0.754802
[493]	train-auc:0.999763	valid-auc:0.754705
[494]	train-auc:0.999769	valid-auc:0.754671
[495]	train-auc:0.999773	valid-auc:0.754667
[496]	train-auc:0.999781	valid-auc:0.754752
[497]	train-auc:0.999786	valid-auc:0.754819
[498]	train-auc:0.999789	valid-auc:0.754806
[499]	train-auc:0.999794	valid-auc:0.754826
[500]	train-auc:0.999795	valid-auc:0.754812
[501]	train-auc:0.999797	valid-auc:0.754783
[502]	train-auc:0.999799	valid-auc:0.754808
[503]	train-auc:0.999806	valid-auc:0.754789
[504]	train-auc:0.99981	valid-auc:0.754736
[505]	train-auc:0.999812	valid-auc:0.754693
[506]	train-auc:0.999815	valid-auc:0.754724
[507]	train-auc:0.999818	valid-auc:0.754674
[508]	train-auc:0.999823	valid-auc:0.754606
Stopping. Best iteration:
[458]	train-auc:0.99959	valid-auc:0.755257

[mlcrate] Finished training fold 2 - took 5m00s - running score 0.7600026666666667
[mlcrate] Running fold 3, 62697 train samples, 10450 validation samples
[0]	train-auc:0.799425	valid-auc:0.702943
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:0.823667	valid-auc:0.720345
[2]	train-auc:0.833432	valid-auc:0.723061
[3]	train-auc:0.839319	valid-auc:0.72769
[4]	train-auc:0.845212	valid-auc:0.730796
[5]	train-auc:0.848298	valid-auc:0.731349
[6]	train-auc:0.852045	valid-auc:0.733027
[7]	train-auc:0.854173	valid-auc:0.733315
[8]	train-auc:0.856063	valid-auc:0.734307
[9]	train-auc:0.857972	valid-auc:0.736021
[10]	train-auc:0.859968	valid-auc:0.736243
[11]	train-auc:0.862047	valid-auc:0.736706
[12]	train-auc:0.864484	valid-auc:0.737487
[13]	train-auc:0.866275	valid-auc:0.737803
[14]	train-auc:0.868335	valid-auc:0.73875
[15]	train-auc:0.870539	valid-auc:0.739518
[16]	train-auc:0.871878	valid-auc:0.73948
[17]	train-auc:0.873669	valid-auc:0.739789
[18]	train-auc:0.875559	valid-auc:0.739806
[19]	train-auc:0.87725	valid-auc:0.740111
[20]	train-auc:0.878703	valid-auc:0.740262
[21]	train-auc:0.880288	valid-auc:0.740614
[22]	train-auc:0.881557	valid-auc:0.741293
[23]	train-auc:0.882703	valid-auc:0.741851
[24]	train-auc:0.884406	valid-auc:0.742157
[25]	train-auc:0.88567	valid-auc:0.742722
[26]	train-auc:0.88684	valid-auc:0.742427
[27]	train-auc:0.887999	valid-auc:0.742587
[28]	train-auc:0.889401	valid-auc:0.742854
[29]	train-auc:0.890573	valid-auc:0.742934
[30]	train-auc:0.891741	valid-auc:0.743223
[31]	train-auc:0.892947	valid-auc:0.743073
[32]	train-auc:0.894624	valid-auc:0.743071
[33]	train-auc:0.895608	valid-auc:0.743213
[34]	train-auc:0.897356	valid-auc:0.74325
[35]	train-auc:0.898389	valid-auc:0.74315
[36]	train-auc:0.899224	valid-auc:0.743175
[37]	train-auc:0.900224	valid-auc:0.743328
[38]	train-auc:0.901386	valid-auc:0.743493
[39]	train-auc:0.902451	valid-auc:0.743995
[40]	train-auc:0.903387	valid-auc:0.744156
[41]	train-auc:0.904421	valid-auc:0.744185
[42]	train-auc:0.905786	valid-auc:0.744465
[43]	train-auc:0.906794	valid-auc:0.744528
[44]	train-auc:0.908105	valid-auc:0.744572
[45]	train-auc:0.909393	valid-auc:0.745134
[46]	train-auc:0.91075	valid-auc:0.745454
[47]	train-auc:0.911612	valid-auc:0.745401
[48]	train-auc:0.911966	valid-auc:0.745518
[49]	train-auc:0.91265	valid-auc:0.74563
[50]	train-auc:0.914173	valid-auc:0.745828
[51]	train-auc:0.915008	valid-auc:0.745902
[52]	train-auc:0.915728	valid-auc:0.746104
[53]	train-auc:0.916596	valid-auc:0.746565
[54]	train-auc:0.917669	valid-auc:0.746684
[55]	train-auc:0.918509	valid-auc:0.746862
[56]	train-auc:0.919565	valid-auc:0.747035
[57]	train-auc:0.920189	valid-auc:0.747288
[58]	train-auc:0.921148	valid-auc:0.747334
[59]	train-auc:0.922056	valid-auc:0.747527
[60]	train-auc:0.922399	valid-auc:0.747668
[61]	train-auc:0.923612	valid-auc:0.747864
[62]	train-auc:0.924958	valid-auc:0.747943
[63]	train-auc:0.925737	valid-auc:0.747887
[64]	train-auc:0.926408	valid-auc:0.74808
[65]	train-auc:0.927265	valid-auc:0.748317
[66]	train-auc:0.927981	valid-auc:0.748559
[67]	train-auc:0.928714	valid-auc:0.748743
[68]	train-auc:0.929758	valid-auc:0.748918
[69]	train-auc:0.930373	valid-auc:0.749094
[70]	train-auc:0.931215	valid-auc:0.749443
[71]	train-auc:0.931535	valid-auc:0.749484
[72]	train-auc:0.932188	valid-auc:0.74959
[73]	train-auc:0.932755	valid-auc:0.749764
[74]	train-auc:0.933398	valid-auc:0.749872
[75]	train-auc:0.933771	valid-auc:0.750087
[76]	train-auc:0.934155	valid-auc:0.750055
[77]	train-auc:0.934668	valid-auc:0.749975
[78]	train-auc:0.935413	valid-auc:0.74989
[79]	train-auc:0.936596	valid-auc:0.750248
[80]	train-auc:0.937487	valid-auc:0.750372
[81]	train-auc:0.938288	valid-auc:0.750377
[82]	train-auc:0.938502	valid-auc:0.750389
[83]	train-auc:0.938996	valid-auc:0.750457
[84]	train-auc:0.939466	valid-auc:0.750608
[85]	train-auc:0.940174	valid-auc:0.750633
[86]	train-auc:0.941018	valid-auc:0.750919
[87]	train-auc:0.942108	valid-auc:0.751076
[88]	train-auc:0.942349	valid-auc:0.751176
[89]	train-auc:0.94283	valid-auc:0.751221
[90]	train-auc:0.943385	valid-auc:0.751268
[91]	train-auc:0.943608	valid-auc:0.75141
[92]	train-auc:0.943782	valid-auc:0.751639
[93]	train-auc:0.944429	valid-auc:0.751684
[94]	train-auc:0.945251	valid-auc:0.752028
[95]	train-auc:0.94622	valid-auc:0.751976
[96]	train-auc:0.946459	valid-auc:0.751889
[97]	train-auc:0.946926	valid-auc:0.752009
[98]	train-auc:0.947295	valid-auc:0.752029
[99]	train-auc:0.947339	valid-auc:0.751994
[100]	train-auc:0.947785	valid-auc:0.752094
[101]	train-auc:0.948309	valid-auc:0.752106
[102]	train-auc:0.948471	valid-auc:0.752028
[103]	train-auc:0.949149	valid-auc:0.752127
[104]	train-auc:0.949307	valid-auc:0.752197
[105]	train-auc:0.949581	valid-auc:0.752261
[106]	train-auc:0.950022	valid-auc:0.752398
[107]	train-auc:0.950595	valid-auc:0.752431
[108]	train-auc:0.951322	valid-auc:0.752679
[109]	train-auc:0.951868	valid-auc:0.752892
[110]	train-auc:0.952309	valid-auc:0.753076
[111]	train-auc:0.952851	valid-auc:0.753117
[112]	train-auc:0.953136	valid-auc:0.753038
[113]	train-auc:0.953302	valid-auc:0.753019
[114]	train-auc:0.953903	valid-auc:0.753095
[115]	train-auc:0.954353	valid-auc:0.753136
[116]	train-auc:0.954712	valid-auc:0.753407
[117]	train-auc:0.955187	valid-auc:0.753417
[118]	train-auc:0.955803	valid-auc:0.753635
[119]	train-auc:0.956583	valid-auc:0.753844
[120]	train-auc:0.956673	valid-auc:0.753757
[121]	train-auc:0.9569	valid-auc:0.753832
[122]	train-auc:0.957131	valid-auc:0.753883
[123]	train-auc:0.957553	valid-auc:0.753792
[124]	train-auc:0.958312	valid-auc:0.753852
[125]	train-auc:0.958542	valid-auc:0.753879
[126]	train-auc:0.958898	valid-auc:0.753717
[127]	train-auc:0.959151	valid-auc:0.75373
[128]	train-auc:0.959481	valid-auc:0.753726
[129]	train-auc:0.959514	valid-auc:0.753734
[130]	train-auc:0.959698	valid-auc:0.753714
[131]	train-auc:0.959844	valid-auc:0.753809
[132]	train-auc:0.960628	valid-auc:0.753869
[133]	train-auc:0.961062	valid-auc:0.753843
[134]	train-auc:0.961517	valid-auc:0.753954
[135]	train-auc:0.962003	valid-auc:0.753983
[136]	train-auc:0.962307	valid-auc:0.754047
[137]	train-auc:0.962682	valid-auc:0.75397
[138]	train-auc:0.963412	valid-auc:0.754135
[139]	train-auc:0.963764	valid-auc:0.754194
[140]	train-auc:0.964356	valid-auc:0.754393
[141]	train-auc:0.964591	valid-auc:0.754336
[142]	train-auc:0.964817	valid-auc:0.754242
[143]	train-auc:0.965009	valid-auc:0.754238
[144]	train-auc:0.965228	valid-auc:0.75428
[145]	train-auc:0.96555	valid-auc:0.754373
[146]	train-auc:0.965956	valid-auc:0.754508
[147]	train-auc:0.966329	valid-auc:0.754539
[148]	train-auc:0.966929	valid-auc:0.754582
[149]	train-auc:0.96709	valid-auc:0.754667
[150]	train-auc:0.967456	valid-auc:0.754627
[151]	train-auc:0.968132	valid-auc:0.754851
[152]	train-auc:0.968609	valid-auc:0.754707
[153]	train-auc:0.969011	valid-auc:0.754783
[154]	train-auc:0.969271	valid-auc:0.754853
[155]	train-auc:0.969736	valid-auc:0.754821
[156]	train-auc:0.97028	valid-auc:0.754711
[157]	train-auc:0.970507	valid-auc:0.754757
[158]	train-auc:0.970751	valid-auc:0.754825
[159]	train-auc:0.971199	valid-auc:0.75502
[160]	train-auc:0.971433	valid-auc:0.755116
[161]	train-auc:0.971778	valid-auc:0.755093
[162]	train-auc:0.971944	valid-auc:0.755118
[163]	train-auc:0.97242	valid-auc:0.755189
[164]	train-auc:0.972542	valid-auc:0.755183
[165]	train-auc:0.972578	valid-auc:0.755188
[166]	train-auc:0.973046	valid-auc:0.755376
[167]	train-auc:0.973915	valid-auc:0.755469
[168]	train-auc:0.974017	valid-auc:0.75548
[169]	train-auc:0.974432	valid-auc:0.755573
[170]	train-auc:0.974639	valid-auc:0.755657
[171]	train-auc:0.974903	valid-auc:0.75573
[172]	train-auc:0.97506	valid-auc:0.75569
[173]	train-auc:0.97529	valid-auc:0.755654
[174]	train-auc:0.975694	valid-auc:0.75576
[175]	train-auc:0.97585	valid-auc:0.7558
[176]	train-auc:0.976246	valid-auc:0.755879
[177]	train-auc:0.976327	valid-auc:0.755936
[178]	train-auc:0.976365	valid-auc:0.755944
[179]	train-auc:0.976723	valid-auc:0.755887
[180]	train-auc:0.976822	valid-auc:0.755982
[181]	train-auc:0.976902	valid-auc:0.755933
[182]	train-auc:0.977211	valid-auc:0.756176
[183]	train-auc:0.977425	valid-auc:0.756153
[184]	train-auc:0.977508	valid-auc:0.756165
[185]	train-auc:0.977646	valid-auc:0.756143
[186]	train-auc:0.977994	valid-auc:0.756237
[187]	train-auc:0.978195	valid-auc:0.756231
[188]	train-auc:0.978405	valid-auc:0.756282
[189]	train-auc:0.978577	valid-auc:0.756462
[190]	train-auc:0.97869	valid-auc:0.756446
[191]	train-auc:0.979012	valid-auc:0.756495
[192]	train-auc:0.979163	valid-auc:0.756513
[193]	train-auc:0.979323	valid-auc:0.756506
[194]	train-auc:0.979386	valid-auc:0.756513
[195]	train-auc:0.97965	valid-auc:0.75655
[196]	train-auc:0.97989	valid-auc:0.756547
[197]	train-auc:0.980314	valid-auc:0.756576
[198]	train-auc:0.980462	valid-auc:0.756493
[199]	train-auc:0.980512	valid-auc:0.756561
[200]	train-auc:0.980701	valid-auc:0.75662
[201]	train-auc:0.980964	valid-auc:0.756657
[202]	train-auc:0.981264	valid-auc:0.756727
[203]	train-auc:0.981544	valid-auc:0.756762
[204]	train-auc:0.981649	valid-auc:0.756792
[205]	train-auc:0.982008	valid-auc:0.7568
[206]	train-auc:0.982127	valid-auc:0.756743
[207]	train-auc:0.982514	valid-auc:0.756915
[208]	train-auc:0.982566	valid-auc:0.756932
[209]	train-auc:0.982785	valid-auc:0.757062
[210]	train-auc:0.982887	valid-auc:0.75715
[211]	train-auc:0.983325	valid-auc:0.757165
[212]	train-auc:0.983413	valid-auc:0.75718
[213]	train-auc:0.983608	valid-auc:0.757231
[214]	train-auc:0.983859	valid-auc:0.757138
[215]	train-auc:0.983965	valid-auc:0.757128
[216]	train-auc:0.984265	valid-auc:0.757031
[217]	train-auc:0.984404	valid-auc:0.756999
[218]	train-auc:0.984842	valid-auc:0.757096
[219]	train-auc:0.985137	valid-auc:0.757136
[220]	train-auc:0.985252	valid-auc:0.757208
[221]	train-auc:0.985312	valid-auc:0.757214
[222]	train-auc:0.985447	valid-auc:0.757329
[223]	train-auc:0.985632	valid-auc:0.757366
[224]	train-auc:0.986	valid-auc:0.757346
[225]	train-auc:0.986167	valid-auc:0.757481
[226]	train-auc:0.986345	valid-auc:0.757616
[227]	train-auc:0.986375	valid-auc:0.757596
[228]	train-auc:0.986662	valid-auc:0.757621
[229]	train-auc:0.986707	valid-auc:0.757682
[230]	train-auc:0.986842	valid-auc:0.757748
[231]	train-auc:0.987054	valid-auc:0.757782
[232]	train-auc:0.987252	valid-auc:0.757812
[233]	train-auc:0.987385	valid-auc:0.757724
[234]	train-auc:0.987469	valid-auc:0.757855
[235]	train-auc:0.987746	valid-auc:0.757877
[236]	train-auc:0.988018	valid-auc:0.757724
[237]	train-auc:0.988224	valid-auc:0.757765
[238]	train-auc:0.988391	valid-auc:0.757734
[239]	train-auc:0.988485	valid-auc:0.757685
[240]	train-auc:0.988645	valid-auc:0.757699
[241]	train-auc:0.988676	valid-auc:0.757716
[242]	train-auc:0.988816	valid-auc:0.757729
[243]	train-auc:0.98895	valid-auc:0.757734
[244]	train-auc:0.989226	valid-auc:0.757618
[245]	train-auc:0.989315	valid-auc:0.757597
[246]	train-auc:0.989495	valid-auc:0.757619
[247]	train-auc:0.989748	valid-auc:0.75759
[248]	train-auc:0.990019	valid-auc:0.757677
[249]	train-auc:0.990098	valid-auc:0.757616
[250]	train-auc:0.990118	valid-auc:0.757639
[251]	train-auc:0.990337	valid-auc:0.757662
[252]	train-auc:0.990447	valid-auc:0.757703
[253]	train-auc:0.990514	valid-auc:0.75775
[254]	train-auc:0.990673	valid-auc:0.757708
[255]	train-auc:0.990795	valid-auc:0.757818
[256]	train-auc:0.990947	valid-auc:0.757816
[257]	train-auc:0.99109	valid-auc:0.757773
[258]	train-auc:0.991256	valid-auc:0.757762
[259]	train-auc:0.991349	valid-auc:0.757729
[260]	train-auc:0.991493	valid-auc:0.757722
[261]	train-auc:0.991621	valid-auc:0.75771
[262]	train-auc:0.991718	valid-auc:0.757681
[263]	train-auc:0.991844	valid-auc:0.757718
[264]	train-auc:0.991925	valid-auc:0.757762
[265]	train-auc:0.992079	valid-auc:0.757902
[266]	train-auc:0.99216	valid-auc:0.757863
[267]	train-auc:0.992353	valid-auc:0.758079
[268]	train-auc:0.992471	valid-auc:0.757995
[269]	train-auc:0.992656	valid-auc:0.757972
[270]	train-auc:0.992787	valid-auc:0.757922
[271]	train-auc:0.992825	valid-auc:0.757891
[272]	train-auc:0.992955	valid-auc:0.758115
[273]	train-auc:0.993123	valid-auc:0.758166
[274]	train-auc:0.993207	valid-auc:0.758267
[275]	train-auc:0.993277	valid-auc:0.758348
[276]	train-auc:0.993359	valid-auc:0.758396
[277]	train-auc:0.993387	valid-auc:0.758382
[278]	train-auc:0.993412	valid-auc:0.758354
[279]	train-auc:0.993532	valid-auc:0.758316
[280]	train-auc:0.993645	valid-auc:0.758322
[281]	train-auc:0.99367	valid-auc:0.75833
[282]	train-auc:0.993796	valid-auc:0.758382
[283]	train-auc:0.993934	valid-auc:0.758579
[284]	train-auc:0.993986	valid-auc:0.758517
[285]	train-auc:0.994041	valid-auc:0.758531
[286]	train-auc:0.994213	valid-auc:0.758583
[287]	train-auc:0.994279	valid-auc:0.758601
[288]	train-auc:0.994395	valid-auc:0.758513
[289]	train-auc:0.994498	valid-auc:0.75851
[290]	train-auc:0.994553	valid-auc:0.758583
[291]	train-auc:0.99465	valid-auc:0.758521
[292]	train-auc:0.994709	valid-auc:0.758595
[293]	train-auc:0.994755	valid-auc:0.758601
[294]	train-auc:0.994794	valid-auc:0.758677
[295]	train-auc:0.994888	valid-auc:0.758655
[296]	train-auc:0.994982	valid-auc:0.758707
[297]	train-auc:0.995051	valid-auc:0.758848
[298]	train-auc:0.995062	valid-auc:0.75883
[299]	train-auc:0.99514	valid-auc:0.75891
[300]	train-auc:0.995204	valid-auc:0.758968
[301]	train-auc:0.995287	valid-auc:0.758954
[302]	train-auc:0.995336	valid-auc:0.759062
[303]	train-auc:0.995447	valid-auc:0.758976
[304]	train-auc:0.995489	valid-auc:0.758969
[305]	train-auc:0.995527	valid-auc:0.758971
[306]	train-auc:0.995562	valid-auc:0.75894
[307]	train-auc:0.995655	valid-auc:0.759045
[308]	train-auc:0.995714	valid-auc:0.759047
[309]	train-auc:0.995772	valid-auc:0.759031
[310]	train-auc:0.995853	valid-auc:0.758958
[311]	train-auc:0.995925	valid-auc:0.759013
[312]	train-auc:0.995929	valid-auc:0.75904
[313]	train-auc:0.995975	valid-auc:0.759081
[314]	train-auc:0.996011	valid-auc:0.759078
[315]	train-auc:0.996066	valid-auc:0.759144
[316]	train-auc:0.996079	valid-auc:0.759091
[317]	train-auc:0.9961	valid-auc:0.759157
[318]	train-auc:0.996148	valid-auc:0.759112
[319]	train-auc:0.996212	valid-auc:0.759115
[320]	train-auc:0.996286	valid-auc:0.759053
[321]	train-auc:0.996341	valid-auc:0.759032
[322]	train-auc:0.996439	valid-auc:0.759061
[323]	train-auc:0.996487	valid-auc:0.759122
[324]	train-auc:0.996526	valid-auc:0.759225
[325]	train-auc:0.996588	valid-auc:0.759103
[326]	train-auc:0.996667	valid-auc:0.75903
[327]	train-auc:0.996705	valid-auc:0.759076
[328]	train-auc:0.996745	valid-auc:0.759064
[329]	train-auc:0.996825	valid-auc:0.758987
[330]	train-auc:0.996889	valid-auc:0.758986
[331]	train-auc:0.9969	valid-auc:0.758953
[332]	train-auc:0.996941	valid-auc:0.759002
[333]	train-auc:0.99699	valid-auc:0.758962
[334]	train-auc:0.997039	valid-auc:0.759054
[335]	train-auc:0.997078	valid-auc:0.759107
[336]	train-auc:0.997113	valid-auc:0.759086
[337]	train-auc:0.997117	valid-auc:0.759082
[338]	train-auc:0.997213	valid-auc:0.758984
[339]	train-auc:0.997238	valid-auc:0.759016
[340]	train-auc:0.997303	valid-auc:0.759105
[341]	train-auc:0.997367	valid-auc:0.759118
[342]	train-auc:0.997387	valid-auc:0.759136
[343]	train-auc:0.997444	valid-auc:0.759158
[344]	train-auc:0.997458	valid-auc:0.759175
[345]	train-auc:0.997502	valid-auc:0.759185
[346]	train-auc:0.997543	valid-auc:0.759232
[347]	train-auc:0.997567	valid-auc:0.759263
[348]	train-auc:0.997598	valid-auc:0.759274
[349]	train-auc:0.99764	valid-auc:0.759247
[350]	train-auc:0.997668	valid-auc:0.759161
[351]	train-auc:0.997687	valid-auc:0.759221
[352]	train-auc:0.997721	valid-auc:0.759225
[353]	train-auc:0.997747	valid-auc:0.759193
[354]	train-auc:0.997795	valid-auc:0.759436
[355]	train-auc:0.997813	valid-auc:0.759527
[356]	train-auc:0.997849	valid-auc:0.759645
[357]	train-auc:0.997866	valid-auc:0.759737
[358]	train-auc:0.997895	valid-auc:0.759646
[359]	train-auc:0.997921	valid-auc:0.759577
[360]	train-auc:0.997939	valid-auc:0.759516
[361]	train-auc:0.997947	valid-auc:0.75957
[362]	train-auc:0.998028	valid-auc:0.759468
[363]	train-auc:0.998041	valid-auc:0.759526
[364]	train-auc:0.998066	valid-auc:0.759639
[365]	train-auc:0.998117	valid-auc:0.75955
[366]	train-auc:0.998155	valid-auc:0.759576
[367]	train-auc:0.998163	valid-auc:0.759604
[368]	train-auc:0.998211	valid-auc:0.759588
[369]	train-auc:0.998231	valid-auc:0.759589
[370]	train-auc:0.998253	valid-auc:0.75959
[371]	train-auc:0.998269	valid-auc:0.75961
[372]	train-auc:0.998297	valid-auc:0.759664
[373]	train-auc:0.998323	valid-auc:0.759737
[374]	train-auc:0.998349	valid-auc:0.759747
[375]	train-auc:0.998367	valid-auc:0.759794
[376]	train-auc:0.998402	valid-auc:0.759741
[377]	train-auc:0.998436	valid-auc:0.759731
[378]	train-auc:0.998443	valid-auc:0.759737
[379]	train-auc:0.99848	valid-auc:0.759636
[380]	train-auc:0.998495	valid-auc:0.759622
[381]	train-auc:0.998511	valid-auc:0.759727
[382]	train-auc:0.998548	valid-auc:0.759759
[383]	train-auc:0.998567	valid-auc:0.759712
[384]	train-auc:0.998596	valid-auc:0.759678
[385]	train-auc:0.998626	valid-auc:0.759787
[386]	train-auc:0.99865	valid-auc:0.759706
[387]	train-auc:0.998662	valid-auc:0.759657
[388]	train-auc:0.998684	valid-auc:0.759658
[389]	train-auc:0.998694	valid-auc:0.759554
[390]	train-auc:0.998719	valid-auc:0.759548
[391]	train-auc:0.998727	valid-auc:0.759574
[392]	train-auc:0.998746	valid-auc:0.75964
[393]	train-auc:0.998756	valid-auc:0.75965
[394]	train-auc:0.998777	valid-auc:0.759803
[395]	train-auc:0.998796	valid-auc:0.759763
[396]	train-auc:0.998816	valid-auc:0.759878
[397]	train-auc:0.998825	valid-auc:0.759928
[398]	train-auc:0.998842	valid-auc:0.759933
[399]	train-auc:0.998882	valid-auc:0.759931
[400]	train-auc:0.998891	valid-auc:0.759969
[401]	train-auc:0.998905	valid-auc:0.759899
[402]	train-auc:0.998919	valid-auc:0.759882
[403]	train-auc:0.998937	valid-auc:0.759806
[404]	train-auc:0.998939	valid-auc:0.759788
[405]	train-auc:0.998956	valid-auc:0.759887
[406]	train-auc:0.998963	valid-auc:0.759877
[407]	train-auc:0.998983	valid-auc:0.759778
[408]	train-auc:0.998994	valid-auc:0.75979
[409]	train-auc:0.999016	valid-auc:0.759919
[410]	train-auc:0.999027	valid-auc:0.760023
[411]	train-auc:0.999049	valid-auc:0.760034
[412]	train-auc:0.999068	valid-auc:0.760075
[413]	train-auc:0.999089	valid-auc:0.760157
[414]	train-auc:0.99912	valid-auc:0.7601
[415]	train-auc:0.999135	valid-auc:0.76003
[416]	train-auc:0.999151	valid-auc:0.759935
[417]	train-auc:0.999162	valid-auc:0.76
[418]	train-auc:0.99917	valid-auc:0.760009
[419]	train-auc:0.999184	valid-auc:0.759899
[420]	train-auc:0.99921	valid-auc:0.759897
[421]	train-auc:0.999227	valid-auc:0.759932
[422]	train-auc:0.999245	valid-auc:0.759901
[423]	train-auc:0.999252	valid-auc:0.759847
[424]	train-auc:0.999273	valid-auc:0.75969
[425]	train-auc:0.999292	valid-auc:0.759689
[426]	train-auc:0.999294	valid-auc:0.759677
[427]	train-auc:0.999298	valid-auc:0.759649
[428]	train-auc:0.999299	valid-auc:0.759675
[429]	train-auc:0.999315	valid-auc:0.759708
[430]	train-auc:0.999342	valid-auc:0.759866
[431]	train-auc:0.999348	valid-auc:0.759887
[432]	train-auc:0.999371	valid-auc:0.759743
[433]	train-auc:0.999375	valid-auc:0.759703
[434]	train-auc:0.999387	valid-auc:0.759593
[435]	train-auc:0.999403	valid-auc:0.759658
[436]	train-auc:0.999408	valid-auc:0.759621
[437]	train-auc:0.999416	valid-auc:0.759646
[438]	train-auc:0.999422	valid-auc:0.75966
[439]	train-auc:0.999435	valid-auc:0.759669
[440]	train-auc:0.999436	valid-auc:0.759686
[441]	train-auc:0.999445	valid-auc:0.759693
[442]	train-auc:0.999453	valid-auc:0.759628
[443]	train-auc:0.999463	valid-auc:0.759647
[444]	train-auc:0.999478	valid-auc:0.759619
[445]	train-auc:0.999485	valid-auc:0.759551
[446]	train-auc:0.999499	valid-auc:0.759564
[447]	train-auc:0.999509	valid-auc:0.759578
[448]	train-auc:0.999517	valid-auc:0.759673
[449]	train-auc:0.999518	valid-auc:0.759652
[450]	train-auc:0.999532	valid-auc:0.759632
[451]	train-auc:0.999535	valid-auc:0.759615
[452]	train-auc:0.999545	valid-auc:0.759682
[453]	train-auc:0.999552	valid-auc:0.759669
[454]	train-auc:0.999557	valid-auc:0.75966
[455]	train-auc:0.99956	valid-auc:0.759645
[456]	train-auc:0.999561	valid-auc:0.759676
[457]	train-auc:0.999571	valid-auc:0.759689
[458]	train-auc:0.999583	valid-auc:0.759682
[459]	train-auc:0.999594	valid-auc:0.759665
[460]	train-auc:0.999599	valid-auc:0.759641
[461]	train-auc:0.999613	valid-auc:0.759808
[462]	train-auc:0.999619	valid-auc:0.759854
[463]	train-auc:0.999632	valid-auc:0.759892
Stopping. Best iteration:
[413]	train-auc:0.999089	valid-auc:0.760157

[mlcrate] Finished training fold 3 - took 4m28s - running score 0.76004125
[mlcrate] Running fold 4, 62697 train samples, 10450 validation samples
[0]	train-auc:0.798471	valid-auc:0.708554
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:0.82339	valid-auc:0.729928
[2]	train-auc:0.83258	valid-auc:0.735677
[3]	train-auc:0.838973	valid-auc:0.740736
[4]	train-auc:0.844507	valid-auc:0.742084
[5]	train-auc:0.847193	valid-auc:0.742758
[6]	train-auc:0.850035	valid-auc:0.743681
[7]	train-auc:0.852035	valid-auc:0.744777
[8]	train-auc:0.854245	valid-auc:0.746201
[9]	train-auc:0.856829	valid-auc:0.746442
[10]	train-auc:0.859531	valid-auc:0.746518
[11]	train-auc:0.861683	valid-auc:0.746645
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[349]	train-auc:0.997826	valid-auc:0.76738
[350]	train-auc:0.997837	valid-auc:0.767345
[351]	train-auc:0.997867	valid-auc:0.767248
[352]	train-auc:0.997934	valid-auc:0.767365
[353]	train-auc:0.997972	valid-auc:0.767427
[354]	train-auc:0.997995	valid-auc:0.767445
[355]	train-auc:0.99802	valid-auc:0.767446
Stopping. Best iteration:
[305]	train-auc:0.996005	valid-auc:0.767716

[mlcrate] Finished training fold 4 - took 3m30s - running score 0.7615762
[mlcrate] Running fold 5, 62698 train samples, 10449 validation samples
[0]	train-auc:0.805118	valid-auc:0.711957
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:0.824769	valid-auc:0.729181
[2]	train-auc:0.83472	valid-auc:0.731418
[3]	train-auc:0.841087	valid-auc:0.732713
[4]	train-auc:0.845819	valid-auc:0.734643
[5]	train-auc:0.851232	valid-auc:0.736121
[6]	train-auc:0.853899	valid-auc:0.737592
[7]	train-auc:0.856583	valid-auc:0.73841
[8]	train-auc:0.858458	valid-auc:0.738622
[9]	train-auc:0.860388	valid-auc:0.739144
[10]	train-auc:0.861764	valid-auc:0.73927
[11]	train-auc:0.86364	valid-auc:0.740237
[12]	train-auc:0.865614	valid-auc:0.741065
[13]	train-auc:0.86742	valid-auc:0.741786
[14]	train-auc:0.869578	valid-auc:0.741558
[15]	train-auc:0.871112	valid-auc:0.742342
[16]	train-auc:0.872192	valid-auc:0.742356
[17]	train-auc:0.873826	valid-auc:0.742787
[18]	train-auc:0.875706	valid-auc:0.743308
[19]	train-auc:0.877304	valid-auc:0.74299
[20]	train-auc:0.878686	valid-auc:0.743564
[21]	train-auc:0.880118	valid-auc:0.743595
[22]	train-auc:0.88143	valid-auc:0.74407
[23]	train-auc:0.882946	valid-auc:0.744373
[24]	train-auc:0.884778	valid-auc:0.744699
[25]	train-auc:0.885898	valid-auc:0.74444
[26]	train-auc:0.887124	valid-auc:0.744375
[27]	train-auc:0.888609	valid-auc:0.744631
[28]	train-auc:0.889836	valid-auc:0.744596
[29]	train-auc:0.891283	valid-auc:0.744873
[30]	train-auc:0.89239	valid-auc:0.745274
[31]	train-auc:0.893811	valid-auc:0.745514
[32]	train-auc:0.895208	valid-auc:0.745662
[33]	train-auc:0.896982	valid-auc:0.745815
[34]	train-auc:0.898421	valid-auc:0.745905
[35]	train-auc:0.899745	valid-auc:0.745802
[36]	train-auc:0.900568	valid-auc:0.746091
[37]	train-auc:0.901592	valid-auc:0.746103
[38]	train-auc:0.902811	valid-auc:0.746
[39]	train-auc:0.903576	valid-auc:0.746118
[40]	train-auc:0.904725	valid-auc:0.746165
[41]	train-auc:0.905897	valid-auc:0.745929
[42]	train-auc:0.906881	valid-auc:0.74601
[43]	train-auc:0.907859	valid-auc:0.74638
[44]	train-auc:0.908952	valid-auc:0.746767
[45]	train-auc:0.910242	valid-auc:0.746757
[46]	train-auc:0.911377	valid-auc:0.746896
[47]	train-auc:0.912453	valid-auc:0.746993
[48]	train-auc:0.913246	valid-auc:0.746998
[49]	train-auc:0.913945	valid-auc:0.747215
[50]	train-auc:0.91478	valid-auc:0.747324
[51]	train-auc:0.91537	valid-auc:0.747414
[52]	train-auc:0.916096	valid-auc:0.747454
[53]	train-auc:0.917476	valid-auc:0.747978
[54]	train-auc:0.91858	valid-auc:0.748535
[55]	train-auc:0.919489	valid-auc:0.748546
[56]	train-auc:0.920878	valid-auc:0.748544
[57]	train-auc:0.92134	valid-auc:0.7484
[58]	train-auc:0.92208	valid-auc:0.748484
[59]	train-auc:0.922961	valid-auc:0.748626
[60]	train-auc:0.923948	valid-auc:0.748647
[61]	train-auc:0.924646	valid-auc:0.748629
[62]	train-auc:0.925679	valid-auc:0.748774
[63]	train-auc:0.926411	valid-auc:0.748778
[64]	train-auc:0.927028	valid-auc:0.748841
[65]	train-auc:0.928048	valid-auc:0.748903
[66]	train-auc:0.929473	valid-auc:0.749175
[67]	train-auc:0.930481	valid-auc:0.74929
[68]	train-auc:0.931152	valid-auc:0.74938
[69]	train-auc:0.931585	valid-auc:0.749386
[70]	train-auc:0.931933	valid-auc:0.749432
[71]	train-auc:0.932857	valid-auc:0.749493
[72]	train-auc:0.933333	valid-auc:0.749578
[73]	train-auc:0.934193	valid-auc:0.749682
[74]	train-auc:0.935022	valid-auc:0.749929
[75]	train-auc:0.935379	valid-auc:0.749955
[76]	train-auc:0.936104	valid-auc:0.750028
[77]	train-auc:0.936567	valid-auc:0.749927
[78]	train-auc:0.93673	valid-auc:0.750003
[79]	train-auc:0.937465	valid-auc:0.750207
[80]	train-auc:0.937951	valid-auc:0.750449
[81]	train-auc:0.938318	valid-auc:0.750347
[82]	train-auc:0.938873	valid-auc:0.750386
[83]	train-auc:0.939624	valid-auc:0.750364
[84]	train-auc:0.940137	valid-auc:0.750471
[85]	train-auc:0.940841	valid-auc:0.750436
[86]	train-auc:0.941785	valid-auc:0.750459
[87]	train-auc:0.942024	valid-auc:0.750433
[88]	train-auc:0.942625	valid-auc:0.750318
[89]	train-auc:0.943552	valid-auc:0.750404
[90]	train-auc:0.94394	valid-auc:0.750382
[91]	train-auc:0.944253	valid-auc:0.750284
[92]	train-auc:0.944431	valid-auc:0.750444
[93]	train-auc:0.944967	valid-auc:0.750426
[94]	train-auc:0.946193	valid-auc:0.750876
[95]	train-auc:0.946742	valid-auc:0.750892
[96]	train-auc:0.947206	valid-auc:0.750935
[97]	train-auc:0.947508	valid-auc:0.75099
[98]	train-auc:0.948669	valid-auc:0.750876
[99]	train-auc:0.949261	valid-auc:0.751125
[100]	train-auc:0.949711	valid-auc:0.751043
[101]	train-auc:0.950053	valid-auc:0.750961
[102]	train-auc:0.950617	valid-auc:0.751117
[103]	train-auc:0.950927	valid-auc:0.75112
[104]	train-auc:0.951603	valid-auc:0.751086
[105]	train-auc:0.952036	valid-auc:0.751219
[106]	train-auc:0.952256	valid-auc:0.751353
[107]	train-auc:0.952386	valid-auc:0.751418
[108]	train-auc:0.952823	valid-auc:0.751465
[109]	train-auc:0.953476	valid-auc:0.751743
[110]	train-auc:0.953805	valid-auc:0.751829
[111]	train-auc:0.95391	valid-auc:0.751796
[112]	train-auc:0.954303	valid-auc:0.751848
[113]	train-auc:0.954724	valid-auc:0.751965
[114]	train-auc:0.954849	valid-auc:0.751958
[115]	train-auc:0.954907	valid-auc:0.751919
[116]	train-auc:0.955597	valid-auc:0.752174
[117]	train-auc:0.956094	valid-auc:0.75226
[118]	train-auc:0.956699	valid-auc:0.752395
[119]	train-auc:0.956728	valid-auc:0.752468
[120]	train-auc:0.956944	valid-auc:0.752385
[121]	train-auc:0.957186	valid-auc:0.752464
[122]	train-auc:0.957804	valid-auc:0.752438
[123]	train-auc:0.95796	valid-auc:0.752629
[124]	train-auc:0.958088	valid-auc:0.75278
[125]	train-auc:0.958167	valid-auc:0.752799
[126]	train-auc:0.95845	valid-auc:0.752865
[127]	train-auc:0.959193	valid-auc:0.753026
[128]	train-auc:0.959602	valid-auc:0.752847
[129]	train-auc:0.959971	valid-auc:0.752898
[130]	train-auc:0.960606	valid-auc:0.752967
[131]	train-auc:0.961314	valid-auc:0.75289
[132]	train-auc:0.96149	valid-auc:0.752911
[133]	train-auc:0.961981	valid-auc:0.752994
[134]	train-auc:0.962368	valid-auc:0.753067
[135]	train-auc:0.962509	valid-auc:0.753067
[136]	train-auc:0.962937	valid-auc:0.753132
[137]	train-auc:0.963186	valid-auc:0.753061
[138]	train-auc:0.963455	valid-auc:0.753131
[139]	train-auc:0.963475	valid-auc:0.753143
[140]	train-auc:0.963741	valid-auc:0.75306
[141]	train-auc:0.9643	valid-auc:0.753147
[142]	train-auc:0.964631	valid-auc:0.753159
[143]	train-auc:0.965145	valid-auc:0.753283
[144]	train-auc:0.965931	valid-auc:0.753194
[145]	train-auc:0.966214	valid-auc:0.753316
[146]	train-auc:0.966471	valid-auc:0.753358
[147]	train-auc:0.966688	valid-auc:0.753342
[148]	train-auc:0.967462	valid-auc:0.753265
[149]	train-auc:0.968076	valid-auc:0.753352
[150]	train-auc:0.968224	valid-auc:0.753345
[151]	train-auc:0.968274	valid-auc:0.753358
[152]	train-auc:0.968606	valid-auc:0.753529
[153]	train-auc:0.969064	valid-auc:0.753623
[154]	train-auc:0.9692	valid-auc:0.753565
[155]	train-auc:0.969541	valid-auc:0.753488
[156]	train-auc:0.970355	valid-auc:0.75352
[157]	train-auc:0.970504	valid-auc:0.753543
[158]	train-auc:0.970708	valid-auc:0.753595
[159]	train-auc:0.97135	valid-auc:0.753711
[160]	train-auc:0.971602	valid-auc:0.753844
[161]	train-auc:0.972171	valid-auc:0.754055
[162]	train-auc:0.972323	valid-auc:0.753966
[163]	train-auc:0.97283	valid-auc:0.754133
[164]	train-auc:0.973018	valid-auc:0.754232
[165]	train-auc:0.97352	valid-auc:0.754267
[166]	train-auc:0.973738	valid-auc:0.754353
[167]	train-auc:0.974074	valid-auc:0.754258
[168]	train-auc:0.974443	valid-auc:0.754496
[169]	train-auc:0.974694	valid-auc:0.754506
[170]	train-auc:0.974826	valid-auc:0.754527
[171]	train-auc:0.975034	valid-auc:0.754658
[172]	train-auc:0.975609	valid-auc:0.754787
[173]	train-auc:0.975967	valid-auc:0.754671
[174]	train-auc:0.976142	valid-auc:0.754688
[175]	train-auc:0.976324	valid-auc:0.754656
[176]	train-auc:0.97669	valid-auc:0.754455
[177]	train-auc:0.977348	valid-auc:0.754534
[178]	train-auc:0.97749	valid-auc:0.754605
[179]	train-auc:0.977978	valid-auc:0.754589
[180]	train-auc:0.978141	valid-auc:0.754475
[181]	train-auc:0.978232	valid-auc:0.754476
[182]	train-auc:0.978504	valid-auc:0.754597
[183]	train-auc:0.978575	valid-auc:0.754642
[184]	train-auc:0.978852	valid-auc:0.754651
[185]	train-auc:0.979004	valid-auc:0.754848
[186]	train-auc:0.979304	valid-auc:0.754872
[187]	train-auc:0.979563	valid-auc:0.755121
[188]	train-auc:0.97997	valid-auc:0.755171
[189]	train-auc:0.980185	valid-auc:0.755286
[190]	train-auc:0.980471	valid-auc:0.755349
[191]	train-auc:0.980792	valid-auc:0.755371
[192]	train-auc:0.981119	valid-auc:0.755351
[193]	train-auc:0.981141	valid-auc:0.755316
[194]	train-auc:0.98128	valid-auc:0.755408
[195]	train-auc:0.981384	valid-auc:0.755383
[196]	train-auc:0.981554	valid-auc:0.75535
[197]	train-auc:0.98199	valid-auc:0.755446
[198]	train-auc:0.982007	valid-auc:0.755464
[199]	train-auc:0.982025	valid-auc:0.755477
[200]	train-auc:0.982137	valid-auc:0.75551
[201]	train-auc:0.982299	valid-auc:0.755594
[202]	train-auc:0.98269	valid-auc:0.755532
[203]	train-auc:0.982914	valid-auc:0.75561
[204]	train-auc:0.98309	valid-auc:0.755604
[205]	train-auc:0.983252	valid-auc:0.755697
[206]	train-auc:0.983355	valid-auc:0.755754
[207]	train-auc:0.983486	valid-auc:0.755694
[208]	train-auc:0.983734	valid-auc:0.755617
[209]	train-auc:0.984065	valid-auc:0.755713
[210]	train-auc:0.984184	valid-auc:0.755767
[211]	train-auc:0.984502	valid-auc:0.755649
[212]	train-auc:0.98484	valid-auc:0.755633
[213]	train-auc:0.984973	valid-auc:0.7556
[214]	train-auc:0.985274	valid-auc:0.755447
[215]	train-auc:0.985411	valid-auc:0.755576
[216]	train-auc:0.985797	valid-auc:0.755533
[217]	train-auc:0.986069	valid-auc:0.755388
[218]	train-auc:0.986206	valid-auc:0.755425
[219]	train-auc:0.986335	valid-auc:0.755488
[220]	train-auc:0.9865	valid-auc:0.755709
[221]	train-auc:0.986795	valid-auc:0.75582
[222]	train-auc:0.986925	valid-auc:0.755927
[223]	train-auc:0.986992	valid-auc:0.755884
[224]	train-auc:0.987186	valid-auc:0.755914
[225]	train-auc:0.987406	valid-auc:0.755838
[226]	train-auc:0.987564	valid-auc:0.755825
[227]	train-auc:0.987645	valid-auc:0.755755
[228]	train-auc:0.987736	valid-auc:0.755729
[229]	train-auc:0.987772	valid-auc:0.755722
[230]	train-auc:0.987998	valid-auc:0.755603
[231]	train-auc:0.98818	valid-auc:0.755643
[232]	train-auc:0.988356	valid-auc:0.755662
[233]	train-auc:0.988616	valid-auc:0.755646
[234]	train-auc:0.988878	valid-auc:0.755615
[235]	train-auc:0.988919	valid-auc:0.755643
[236]	train-auc:0.988959	valid-auc:0.755684
[237]	train-auc:0.989035	valid-auc:0.75565
[238]	train-auc:0.989246	valid-auc:0.755693
[239]	train-auc:0.989454	valid-auc:0.755907
[240]	train-auc:0.989541	valid-auc:0.755971
[241]	train-auc:0.989656	valid-auc:0.755952
[242]	train-auc:0.989849	valid-auc:0.755968
[243]	train-auc:0.989963	valid-auc:0.755989
[244]	train-auc:0.990098	valid-auc:0.755942
[245]	train-auc:0.990222	valid-auc:0.755981
[246]	train-auc:0.99039	valid-auc:0.755935
[247]	train-auc:0.990516	valid-auc:0.755913
[248]	train-auc:0.990685	valid-auc:0.755926
[249]	train-auc:0.990747	valid-auc:0.755931
[250]	train-auc:0.990865	valid-auc:0.755976
[251]	train-auc:0.991047	valid-auc:0.756061
[252]	train-auc:0.991199	valid-auc:0.755952
[253]	train-auc:0.991258	valid-auc:0.75585
[254]	train-auc:0.991403	valid-auc:0.755829
[255]	train-auc:0.991484	valid-auc:0.755808
[256]	train-auc:0.991663	valid-auc:0.755625
[257]	train-auc:0.991823	valid-auc:0.755528
[258]	train-auc:0.991954	valid-auc:0.755619
[259]	train-auc:0.992077	valid-auc:0.755657
[260]	train-auc:0.992142	valid-auc:0.755637
[261]	train-auc:0.992258	valid-auc:0.755588
[262]	train-auc:0.992334	valid-auc:0.755501
[263]	train-auc:0.992444	valid-auc:0.755528
[264]	train-auc:0.992545	valid-auc:0.755466
[265]	train-auc:0.992628	valid-auc:0.7554
[266]	train-auc:0.992818	valid-auc:0.755352
[267]	train-auc:0.992868	valid-auc:0.755404
[268]	train-auc:0.993037	valid-auc:0.755361
[269]	train-auc:0.993132	valid-auc:0.755392
[270]	train-auc:0.993193	valid-auc:0.75535
[271]	train-auc:0.993367	valid-auc:0.755353
[272]	train-auc:0.993481	valid-auc:0.75564
[273]	train-auc:0.993565	valid-auc:0.755597
[274]	train-auc:0.993618	valid-auc:0.755583
[275]	train-auc:0.993698	valid-auc:0.755662
[276]	train-auc:0.993846	valid-auc:0.755603
[277]	train-auc:0.993896	valid-auc:0.755525
[278]	train-auc:0.993959	valid-auc:0.755577
[279]	train-auc:0.994027	valid-auc:0.755672
[280]	train-auc:0.994095	valid-auc:0.755656
[281]	train-auc:0.994114	valid-auc:0.755589
[282]	train-auc:0.994188	valid-auc:0.755471
[283]	train-auc:0.994342	valid-auc:0.755541
[284]	train-auc:0.994435	valid-auc:0.755631
[285]	train-auc:0.99448	valid-auc:0.755674
[286]	train-auc:0.994545	valid-auc:0.75563
[287]	train-auc:0.994575	valid-auc:0.755719
[288]	train-auc:0.994679	valid-auc:0.755879
[289]	train-auc:0.99479	valid-auc:0.755992
[290]	train-auc:0.994827	valid-auc:0.756024
[291]	train-auc:0.994919	valid-auc:0.756101
[292]	train-auc:0.995026	valid-auc:0.75618
[293]	train-auc:0.99507	valid-auc:0.756232
[294]	train-auc:0.99511	valid-auc:0.756287
[295]	train-auc:0.995215	valid-auc:0.756323
[296]	train-auc:0.995251	valid-auc:0.756328
[297]	train-auc:0.995302	valid-auc:0.756311
[298]	train-auc:0.995411	valid-auc:0.756203
[299]	train-auc:0.995441	valid-auc:0.756145
[300]	train-auc:0.995519	valid-auc:0.756148
[301]	train-auc:0.995577	valid-auc:0.756194
[302]	train-auc:0.995643	valid-auc:0.756233
[303]	train-auc:0.995686	valid-auc:0.756291
[304]	train-auc:0.995705	valid-auc:0.756236
[305]	train-auc:0.995765	valid-auc:0.756267
[306]	train-auc:0.995783	valid-auc:0.756296
[307]	train-auc:0.995849	valid-auc:0.756402
[308]	train-auc:0.995906	valid-auc:0.756454
[309]	train-auc:0.995986	valid-auc:0.756413
[310]	train-auc:0.996074	valid-auc:0.756622
[311]	train-auc:0.996099	valid-auc:0.756596
[312]	train-auc:0.996135	valid-auc:0.756656
[313]	train-auc:0.996193	valid-auc:0.756732
[314]	train-auc:0.996259	valid-auc:0.756835
[315]	train-auc:0.996312	valid-auc:0.756884
[316]	train-auc:0.996392	valid-auc:0.756774
[317]	train-auc:0.996434	valid-auc:0.756746
[318]	train-auc:0.99648	valid-auc:0.756716
[319]	train-auc:0.9965	valid-auc:0.756805
[320]	train-auc:0.996556	valid-auc:0.756754
[321]	train-auc:0.99661	valid-auc:0.756792
[322]	train-auc:0.996678	valid-auc:0.756681
[323]	train-auc:0.996729	valid-auc:0.756708
[324]	train-auc:0.996776	valid-auc:0.756817
[325]	train-auc:0.996836	valid-auc:0.756745
[326]	train-auc:0.996851	valid-auc:0.756685
[327]	train-auc:0.996891	valid-auc:0.756712
[328]	train-auc:0.996924	valid-auc:0.756802
[329]	train-auc:0.99695	valid-auc:0.756845
[330]	train-auc:0.996966	valid-auc:0.756812
[331]	train-auc:0.996999	valid-auc:0.756845
[332]	train-auc:0.996999	valid-auc:0.756876
[333]	train-auc:0.997011	valid-auc:0.75691
[334]	train-auc:0.997069	valid-auc:0.756938
[335]	train-auc:0.997078	valid-auc:0.756895
[336]	train-auc:0.997177	valid-auc:0.757006
[337]	train-auc:0.997204	valid-auc:0.757013
[338]	train-auc:0.997261	valid-auc:0.756981
[339]	train-auc:0.997314	valid-auc:0.756968
[340]	train-auc:0.99736	valid-auc:0.756958
[341]	train-auc:0.997377	valid-auc:0.756949
[342]	train-auc:0.997421	valid-auc:0.756888
[343]	train-auc:0.997428	valid-auc:0.756885
[344]	train-auc:0.997477	valid-auc:0.756879
[345]	train-auc:0.99754	valid-auc:0.75688
[346]	train-auc:0.997569	valid-auc:0.756972
[347]	train-auc:0.997618	valid-auc:0.756973
[348]	train-auc:0.997672	valid-auc:0.756895
[349]	train-auc:0.997743	valid-auc:0.756907
[350]	train-auc:0.997792	valid-auc:0.756941
[351]	train-auc:0.997809	valid-auc:0.756852
[352]	train-auc:0.997842	valid-auc:0.756916
[353]	train-auc:0.997884	valid-auc:0.756898
[354]	train-auc:0.997928	valid-auc:0.756961
[355]	train-auc:0.997965	valid-auc:0.756945
[356]	train-auc:0.997986	valid-auc:0.756883
[357]	train-auc:0.998003	valid-auc:0.756916
[358]	train-auc:0.998018	valid-auc:0.756977
[359]	train-auc:0.998076	valid-auc:0.757078
[360]	train-auc:0.998107	valid-auc:0.757217
[361]	train-auc:0.998114	valid-auc:0.757294
[362]	train-auc:0.998139	valid-auc:0.757283
[363]	train-auc:0.99817	valid-auc:0.757251
[364]	train-auc:0.998192	valid-auc:0.757221
[365]	train-auc:0.998206	valid-auc:0.757253
[366]	train-auc:0.998249	valid-auc:0.757324
[367]	train-auc:0.998252	valid-auc:0.75735
[368]	train-auc:0.998259	valid-auc:0.757326
[369]	train-auc:0.998268	valid-auc:0.757282
[370]	train-auc:0.998291	valid-auc:0.757287
[371]	train-auc:0.998329	valid-auc:0.757321
[372]	train-auc:0.998333	valid-auc:0.757269
[373]	train-auc:0.998338	valid-auc:0.757237
[374]	train-auc:0.998369	valid-auc:0.757292
[375]	train-auc:0.998399	valid-auc:0.757282
[376]	train-auc:0.998431	valid-auc:0.757353
[377]	train-auc:0.998441	valid-auc:0.757361
[378]	train-auc:0.99845	valid-auc:0.757316
[379]	train-auc:0.99849	valid-auc:0.757392
[380]	train-auc:0.998523	valid-auc:0.757495
[381]	train-auc:0.99854	valid-auc:0.757508
[382]	train-auc:0.998559	valid-auc:0.757492
[383]	train-auc:0.998599	valid-auc:0.757501
[384]	train-auc:0.998609	valid-auc:0.757504
[385]	train-auc:0.998613	valid-auc:0.75752
[386]	train-auc:0.998638	valid-auc:0.757617
[387]	train-auc:0.998659	valid-auc:0.757595
[388]	train-auc:0.998666	valid-auc:0.75758
[389]	train-auc:0.998674	valid-auc:0.757645
[390]	train-auc:0.998686	valid-auc:0.757643
[391]	train-auc:0.998704	valid-auc:0.757744
[392]	train-auc:0.998724	valid-auc:0.757731
[393]	train-auc:0.99874	valid-auc:0.757716
[394]	train-auc:0.998753	valid-auc:0.757714
[395]	train-auc:0.998773	valid-auc:0.757788
[396]	train-auc:0.998799	valid-auc:0.757733
[397]	train-auc:0.998826	valid-auc:0.75769
[398]	train-auc:0.998843	valid-auc:0.757764
[399]	train-auc:0.99888	valid-auc:0.757693
[400]	train-auc:0.998912	valid-auc:0.757641
[401]	train-auc:0.998922	valid-auc:0.757672
[402]	train-auc:0.998937	valid-auc:0.757677
[403]	train-auc:0.998947	valid-auc:0.757679
[404]	train-auc:0.998963	valid-auc:0.757694
[405]	train-auc:0.998973	valid-auc:0.757674
[406]	train-auc:0.998974	valid-auc:0.757687
[407]	train-auc:0.998986	valid-auc:0.757653
[408]	train-auc:0.999013	valid-auc:0.757718
[409]	train-auc:0.999023	valid-auc:0.757701
[410]	train-auc:0.999038	valid-auc:0.757768
[411]	train-auc:0.999061	valid-auc:0.757799
[412]	train-auc:0.999092	valid-auc:0.757957
[413]	train-auc:0.999107	valid-auc:0.757916
[414]	train-auc:0.999115	valid-auc:0.757928
[415]	train-auc:0.999134	valid-auc:0.75807
[416]	train-auc:0.999136	valid-auc:0.758024
[417]	train-auc:0.99916	valid-auc:0.758144
[418]	train-auc:0.999164	valid-auc:0.758097
[419]	train-auc:0.999177	valid-auc:0.758204
[420]	train-auc:0.999189	valid-auc:0.758182
[421]	train-auc:0.9992	valid-auc:0.758201
[422]	train-auc:0.999212	valid-auc:0.758234
[423]	train-auc:0.999219	valid-auc:0.758199
[424]	train-auc:0.999235	valid-auc:0.758173
[425]	train-auc:0.999249	valid-auc:0.758074
[426]	train-auc:0.999261	valid-auc:0.757994
[427]	train-auc:0.99926	valid-auc:0.757984
[428]	train-auc:0.999271	valid-auc:0.758013
[429]	train-auc:0.999283	valid-auc:0.758031
[430]	train-auc:0.999291	valid-auc:0.758019
[431]	train-auc:0.999298	valid-auc:0.757964
[432]	train-auc:0.999311	valid-auc:0.757994
[433]	train-auc:0.999326	valid-auc:0.757916
[434]	train-auc:0.999344	valid-auc:0.757899
[435]	train-auc:0.999347	valid-auc:0.757927
[436]	train-auc:0.999361	valid-auc:0.757992
[437]	train-auc:0.999369	valid-auc:0.757881
[438]	train-auc:0.999371	valid-auc:0.757902
[439]	train-auc:0.999377	valid-auc:0.757917
[440]	train-auc:0.999398	valid-auc:0.757877
[441]	train-auc:0.999412	valid-auc:0.757902
[442]	train-auc:0.999428	valid-auc:0.757877
[443]	train-auc:0.999438	valid-auc:0.757896
[444]	train-auc:0.999454	valid-auc:0.757932
[445]	train-auc:0.999457	valid-auc:0.757902
[446]	train-auc:0.999466	valid-auc:0.757905
[447]	train-auc:0.999481	valid-auc:0.75793
[448]	train-auc:0.999488	valid-auc:0.757948
[449]	train-auc:0.999496	valid-auc:0.757928
[450]	train-auc:0.999509	valid-auc:0.757947
[451]	train-auc:0.999516	valid-auc:0.757933
[452]	train-auc:0.999529	valid-auc:0.757967
[453]	train-auc:0.999534	valid-auc:0.758014
[454]	train-auc:0.999543	valid-auc:0.757937
[455]	train-auc:0.999551	valid-auc:0.757989
[456]	train-auc:0.99956	valid-auc:0.757933
[457]	train-auc:0.999566	valid-auc:0.757945
[458]	train-auc:0.999568	valid-auc:0.757946
[459]	train-auc:0.999581	valid-auc:0.757983
[460]	train-auc:0.999586	valid-auc:0.758067
[461]	train-auc:0.999593	valid-auc:0.757978
[462]	train-auc:0.999605	valid-auc:0.758
[463]	train-auc:0.99961	valid-auc:0.758043
[464]	train-auc:0.999614	valid-auc:0.758088
[465]	train-auc:0.999618	valid-auc:0.758143
[466]	train-auc:0.999624	valid-auc:0.758089
[467]	train-auc:0.999625	valid-auc:0.758142
[468]	train-auc:0.99963	valid-auc:0.758132
[469]	train-auc:0.999645	valid-auc:0.758105
[470]	train-auc:0.999655	valid-auc:0.758106
[471]	train-auc:0.999663	valid-auc:0.758041
[472]	train-auc:0.99967	valid-auc:0.758067
Stopping. Best iteration:
[422]	train-auc:0.999212	valid-auc:0.758234

[mlcrate] Finished training fold 5 - took 4m41s - running score 0.7610191666666667
[mlcrate] Running fold 6, 62699 train samples, 10448 validation samples
[0]	train-auc:0.800985	valid-auc:0.705988
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:0.823359	valid-auc:0.721245
[2]	train-auc:0.834586	valid-auc:0.72946
[3]	train-auc:0.840976	valid-auc:0.734694
[4]	train-auc:0.845387	valid-auc:0.737729
[5]	train-auc:0.849406	valid-auc:0.739165
[6]	train-auc:0.851967	valid-auc:0.739829
[7]	train-auc:0.854535	valid-auc:0.740421
[8]	train-auc:0.855919	valid-auc:0.741808
[9]	train-auc:0.85764	valid-auc:0.741377
[10]	train-auc:0.859513	valid-auc:0.741741
[11]	train-auc:0.861358	valid-auc:0.742788
[12]	train-auc:0.86275	valid-auc:0.743429
[13]	train-auc:0.865032	valid-auc:0.743654
[14]	train-auc:0.86701	valid-auc:0.744247
[15]	train-auc:0.869174	valid-auc:0.745448
[16]	train-auc:0.871176	valid-auc:0.746112
[17]	train-auc:0.872592	valid-auc:0.74621
[18]	train-auc:0.874326	valid-auc:0.746924
[19]	train-auc:0.875751	valid-auc:0.747145
[20]	train-auc:0.877683	valid-auc:0.747592
[21]	train-auc:0.879073	valid-auc:0.747814
[22]	train-auc:0.880832	valid-auc:0.747983
[23]	train-auc:0.882062	valid-auc:0.748748
[24]	train-auc:0.883555	valid-auc:0.749041
[25]	train-auc:0.884937	valid-auc:0.749237
[26]	train-auc:0.88626	valid-auc:0.749463
[27]	train-auc:0.887537	valid-auc:0.749667
[28]	train-auc:0.888929	valid-auc:0.74986
[29]	train-auc:0.89088	valid-auc:0.74998
[30]	train-auc:0.892619	valid-auc:0.749978
[31]	train-auc:0.893836	valid-auc:0.75012
[32]	train-auc:0.895066	valid-auc:0.750411
[33]	train-auc:0.89619	valid-auc:0.750679
[34]	train-auc:0.897518	valid-auc:0.750957
[35]	train-auc:0.898725	valid-auc:0.751273
[36]	train-auc:0.899586	valid-auc:0.751699
[37]	train-auc:0.900351	valid-auc:0.752029
[38]	train-auc:0.901746	valid-auc:0.752169
[39]	train-auc:0.902814	valid-auc:0.752284
[40]	train-auc:0.903719	valid-auc:0.752614
[41]	train-auc:0.905019	valid-auc:0.752668
[42]	train-auc:0.906086	valid-auc:0.753179
[43]	train-auc:0.907121	valid-auc:0.75337
[44]	train-auc:0.908138	valid-auc:0.753648
[45]	train-auc:0.909243	valid-auc:0.753955
[46]	train-auc:0.910352	valid-auc:0.754095
[47]	train-auc:0.911197	valid-auc:0.754357
[48]	train-auc:0.912398	valid-auc:0.754494
[49]	train-auc:0.913156	valid-auc:0.754868
[50]	train-auc:0.913915	valid-auc:0.754967
[51]	train-auc:0.914877	valid-auc:0.754973
[52]	train-auc:0.915725	valid-auc:0.755138
[53]	train-auc:0.916333	valid-auc:0.755269
[54]	train-auc:0.917414	valid-auc:0.755271
[55]	train-auc:0.918233	valid-auc:0.75533
[56]	train-auc:0.919105	valid-auc:0.755619
[57]	train-auc:0.919915	valid-auc:0.756123
[58]	train-auc:0.92043	valid-auc:0.756115
[59]	train-auc:0.921062	valid-auc:0.756187
[60]	train-auc:0.922399	valid-auc:0.756478
[61]	train-auc:0.923466	valid-auc:0.75679
[62]	train-auc:0.924722	valid-auc:0.756783
[63]	train-auc:0.925515	valid-auc:0.756894
[64]	train-auc:0.926397	valid-auc:0.75725
[65]	train-auc:0.926622	valid-auc:0.757271
[66]	train-auc:0.926978	valid-auc:0.757364
[67]	train-auc:0.927789	valid-auc:0.757356
[68]	train-auc:0.928544	valid-auc:0.757408
[69]	train-auc:0.929043	valid-auc:0.757503
[70]	train-auc:0.930398	valid-auc:0.757576
[71]	train-auc:0.931508	valid-auc:0.757971
[72]	train-auc:0.932615	valid-auc:0.758072
[73]	train-auc:0.933206	valid-auc:0.758042
[74]	train-auc:0.93356	valid-auc:0.758009
[75]	train-auc:0.933858	valid-auc:0.758095
[76]	train-auc:0.934488	valid-auc:0.75833
[77]	train-auc:0.934921	valid-auc:0.758413
[78]	train-auc:0.935609	valid-auc:0.758412
[79]	train-auc:0.936189	valid-auc:0.758645
[80]	train-auc:0.936638	valid-auc:0.758722
[81]	train-auc:0.93736	valid-auc:0.758851
[82]	train-auc:0.937821	valid-auc:0.75871
[83]	train-auc:0.938993	valid-auc:0.759044
[84]	train-auc:0.939612	valid-auc:0.759209
[85]	train-auc:0.939893	valid-auc:0.759204
[86]	train-auc:0.940475	valid-auc:0.759442
[87]	train-auc:0.941321	valid-auc:0.75977
[88]	train-auc:0.9424	valid-auc:0.759915
[89]	train-auc:0.94316	valid-auc:0.760039
[90]	train-auc:0.943347	valid-auc:0.760132
[91]	train-auc:0.943468	valid-auc:0.760216
[92]	train-auc:0.944092	valid-auc:0.760174
[93]	train-auc:0.944413	valid-auc:0.760107
[94]	train-auc:0.945157	valid-auc:0.760449
[95]	train-auc:0.945543	valid-auc:0.760548
[96]	train-auc:0.946349	valid-auc:0.760548
[97]	train-auc:0.946672	valid-auc:0.760652
[98]	train-auc:0.947749	valid-auc:0.760815
[99]	train-auc:0.948165	valid-auc:0.761012
[100]	train-auc:0.948361	valid-auc:0.761134
[101]	train-auc:0.948464	valid-auc:0.76112
[102]	train-auc:0.949151	valid-auc:0.761421
[103]	train-auc:0.949993	valid-auc:0.761731
[104]	train-auc:0.950332	valid-auc:0.761822
[105]	train-auc:0.951235	valid-auc:0.761982
[106]	train-auc:0.95171	valid-auc:0.762106
[107]	train-auc:0.951918	valid-auc:0.762135
[108]	train-auc:0.952683	valid-auc:0.762328
[109]	train-auc:0.953438	valid-auc:0.762488
[110]	train-auc:0.95403	valid-auc:0.762782
[111]	train-auc:0.954513	valid-auc:0.762859
[112]	train-auc:0.955645	valid-auc:0.763123
[113]	train-auc:0.956035	valid-auc:0.763219
[114]	train-auc:0.956161	valid-auc:0.763262
[115]	train-auc:0.956453	valid-auc:0.763373
[116]	train-auc:0.956807	valid-auc:0.763368
[117]	train-auc:0.957734	valid-auc:0.763518
[118]	train-auc:0.95844	valid-auc:0.763776
[119]	train-auc:0.959171	valid-auc:0.763803
[120]	train-auc:0.95945	valid-auc:0.763899
[121]	train-auc:0.959903	valid-auc:0.764124
[122]	train-auc:0.959947	valid-auc:0.764188
[123]	train-auc:0.960494	valid-auc:0.764189
[124]	train-auc:0.960777	valid-auc:0.764349
[125]	train-auc:0.96101	valid-auc:0.76444
[126]	train-auc:0.961297	valid-auc:0.764368
[127]	train-auc:0.961453	valid-auc:0.764383
[128]	train-auc:0.961883	valid-auc:0.764459
[129]	train-auc:0.962152	valid-auc:0.764631
[130]	train-auc:0.962688	valid-auc:0.764987
[131]	train-auc:0.963039	valid-auc:0.765041
[132]	train-auc:0.963705	valid-auc:0.765131
[133]	train-auc:0.964102	valid-auc:0.765145
[134]	train-auc:0.964596	valid-auc:0.765003
[135]	train-auc:0.965013	valid-auc:0.764907
[136]	train-auc:0.965282	valid-auc:0.765055
[137]	train-auc:0.96537	valid-auc:0.76509
[138]	train-auc:0.965596	valid-auc:0.765173
[139]	train-auc:0.96595	valid-auc:0.765157
[140]	train-auc:0.966099	valid-auc:0.765159
[141]	train-auc:0.966429	valid-auc:0.765108
[142]	train-auc:0.966869	valid-auc:0.765173
[143]	train-auc:0.967376	valid-auc:0.765104
[144]	train-auc:0.967548	valid-auc:0.765021
[145]	train-auc:0.967647	valid-auc:0.765022
[146]	train-auc:0.967935	valid-auc:0.765007
[147]	train-auc:0.968387	valid-auc:0.765041
[148]	train-auc:0.968509	valid-auc:0.765016
[149]	train-auc:0.968732	valid-auc:0.765164
[150]	train-auc:0.969157	valid-auc:0.765309
[151]	train-auc:0.969577	valid-auc:0.765388
[152]	train-auc:0.969656	valid-auc:0.765447
[153]	train-auc:0.969841	valid-auc:0.765532
[154]	train-auc:0.970259	valid-auc:0.765786
[155]	train-auc:0.970334	valid-auc:0.765821
[156]	train-auc:0.970945	valid-auc:0.765691
[157]	train-auc:0.971286	valid-auc:0.765761
[158]	train-auc:0.971529	valid-auc:0.765935
[159]	train-auc:0.971972	valid-auc:0.765911
[160]	train-auc:0.972109	valid-auc:0.765931
[161]	train-auc:0.972589	valid-auc:0.765849
[162]	train-auc:0.972688	valid-auc:0.765858
[163]	train-auc:0.973351	valid-auc:0.765969
[164]	train-auc:0.973467	valid-auc:0.766036
[165]	train-auc:0.97363	valid-auc:0.766119
[166]	train-auc:0.973755	valid-auc:0.766243
[167]	train-auc:0.974221	valid-auc:0.766315
[168]	train-auc:0.974458	valid-auc:0.766282
[169]	train-auc:0.974594	valid-auc:0.766318
[170]	train-auc:0.974889	valid-auc:0.766265
[171]	train-auc:0.975267	valid-auc:0.766315
[172]	train-auc:0.975604	valid-auc:0.766355
[173]	train-auc:0.975938	valid-auc:0.766363
[174]	train-auc:0.976316	valid-auc:0.766385
[175]	train-auc:0.976556	valid-auc:0.766391
[176]	train-auc:0.976744	valid-auc:0.766392
[177]	train-auc:0.977157	valid-auc:0.766275
[178]	train-auc:0.977485	valid-auc:0.766237
[179]	train-auc:0.977588	valid-auc:0.766203
[180]	train-auc:0.977904	valid-auc:0.766175
[181]	train-auc:0.978256	valid-auc:0.766157
[182]	train-auc:0.978393	valid-auc:0.766101
[183]	train-auc:0.97874	valid-auc:0.766085
[184]	train-auc:0.979065	valid-auc:0.766206
[185]	train-auc:0.979155	valid-auc:0.766224
[186]	train-auc:0.979276	valid-auc:0.766179
[187]	train-auc:0.979657	valid-auc:0.766414
[188]	train-auc:0.979709	valid-auc:0.766387
[189]	train-auc:0.98007	valid-auc:0.766337
[190]	train-auc:0.980646	valid-auc:0.766776
[191]	train-auc:0.980701	valid-auc:0.766756
[192]	train-auc:0.980894	valid-auc:0.766658
[193]	train-auc:0.981057	valid-auc:0.766692
[194]	train-auc:0.981361	valid-auc:0.767008
[195]	train-auc:0.981901	valid-auc:0.766937
[196]	train-auc:0.982336	valid-auc:0.766816
[197]	train-auc:0.982394	valid-auc:0.766785
[198]	train-auc:0.982866	valid-auc:0.766691
[199]	train-auc:0.983263	valid-auc:0.766685
[200]	train-auc:0.983734	valid-auc:0.766788
[201]	train-auc:0.983801	valid-auc:0.766843
[202]	train-auc:0.983903	valid-auc:0.766822
[203]	train-auc:0.98416	valid-auc:0.766885
[204]	train-auc:0.984368	valid-auc:0.767022
[205]	train-auc:0.984639	valid-auc:0.766973
[206]	train-auc:0.984917	valid-auc:0.766836
[207]	train-auc:0.985018	valid-auc:0.766888
[208]	train-auc:0.985285	valid-auc:0.766861
[209]	train-auc:0.985314	valid-auc:0.766903
[210]	train-auc:0.98551	valid-auc:0.766959
[211]	train-auc:0.98561	valid-auc:0.767044
[212]	train-auc:0.98569	valid-auc:0.767062
[213]	train-auc:0.985798	valid-auc:0.767161
[214]	train-auc:0.986003	valid-auc:0.767012
[215]	train-auc:0.986351	valid-auc:0.767186
[216]	train-auc:0.986666	valid-auc:0.767192
[217]	train-auc:0.986766	valid-auc:0.767172
[218]	train-auc:0.987046	valid-auc:0.76732
[219]	train-auc:0.987431	valid-auc:0.767499
[220]	train-auc:0.98754	valid-auc:0.76745
[221]	train-auc:0.987616	valid-auc:0.76748
[222]	train-auc:0.98774	valid-auc:0.767441
[223]	train-auc:0.987922	valid-auc:0.767546
[224]	train-auc:0.98797	valid-auc:0.767537
[225]	train-auc:0.988178	valid-auc:0.767555
[226]	train-auc:0.988209	valid-auc:0.767541
[227]	train-auc:0.98827	valid-auc:0.767582
[228]	train-auc:0.988291	valid-auc:0.76759
[229]	train-auc:0.988477	valid-auc:0.767604
[230]	train-auc:0.988552	valid-auc:0.767656
[231]	train-auc:0.988652	valid-auc:0.767756
[232]	train-auc:0.988779	valid-auc:0.76783
[233]	train-auc:0.988985	valid-auc:0.767924
[234]	train-auc:0.989214	valid-auc:0.767782
[235]	train-auc:0.98934	valid-auc:0.767813
[236]	train-auc:0.989544	valid-auc:0.767855
[237]	train-auc:0.98968	valid-auc:0.767912
[238]	train-auc:0.989829	valid-auc:0.767977
[239]	train-auc:0.989954	valid-auc:0.76809
[240]	train-auc:0.99013	valid-auc:0.767968
[241]	train-auc:0.990229	valid-auc:0.767971
[242]	train-auc:0.990319	valid-auc:0.768038
[243]	train-auc:0.990446	valid-auc:0.7681
[244]	train-auc:0.99054	valid-auc:0.768063
[245]	train-auc:0.990704	valid-auc:0.767965
[246]	train-auc:0.990898	valid-auc:0.768011
[247]	train-auc:0.991085	valid-auc:0.768151
[248]	train-auc:0.991255	valid-auc:0.768033
[249]	train-auc:0.991507	valid-auc:0.76792
[250]	train-auc:0.99162	valid-auc:0.768046
[251]	train-auc:0.991795	valid-auc:0.768079
[252]	train-auc:0.991903	valid-auc:0.767998
[253]	train-auc:0.992094	valid-auc:0.767924
[254]	train-auc:0.992242	valid-auc:0.767908
[255]	train-auc:0.992275	valid-auc:0.767962
[256]	train-auc:0.992346	valid-auc:0.767898
[257]	train-auc:0.992422	valid-auc:0.767935
[258]	train-auc:0.992575	valid-auc:0.767825
[259]	train-auc:0.992701	valid-auc:0.767803
[260]	train-auc:0.992812	valid-auc:0.767784
[261]	train-auc:0.992839	valid-auc:0.767841
[262]	train-auc:0.992849	valid-auc:0.76791
[263]	train-auc:0.992976	valid-auc:0.767808
[264]	train-auc:0.993069	valid-auc:0.767815
[265]	train-auc:0.993108	valid-auc:0.767802
[266]	train-auc:0.993193	valid-auc:0.767886
[267]	train-auc:0.993207	valid-auc:0.767912
[268]	train-auc:0.993351	valid-auc:0.767986
[269]	train-auc:0.993455	valid-auc:0.767889
[270]	train-auc:0.993671	valid-auc:0.767871
[271]	train-auc:0.99374	valid-auc:0.768016
[272]	train-auc:0.993757	valid-auc:0.767969
[273]	train-auc:0.993851	valid-auc:0.767972
[274]	train-auc:0.993895	valid-auc:0.767987
[275]	train-auc:0.993973	valid-auc:0.768082
[276]	train-auc:0.994036	valid-auc:0.768058
[277]	train-auc:0.994155	valid-auc:0.768105
[278]	train-auc:0.994225	valid-auc:0.768132
[279]	train-auc:0.994358	valid-auc:0.768101
[280]	train-auc:0.994507	valid-auc:0.76807
[281]	train-auc:0.994596	valid-auc:0.768043
[282]	train-auc:0.994662	valid-auc:0.768099
[283]	train-auc:0.994738	valid-auc:0.768137
[284]	train-auc:0.994829	valid-auc:0.76808
[285]	train-auc:0.994937	valid-auc:0.768193
[286]	train-auc:0.995	valid-auc:0.768389
[287]	train-auc:0.995099	valid-auc:0.768188
[288]	train-auc:0.995219	valid-auc:0.768134
[289]	train-auc:0.995271	valid-auc:0.768152
[290]	train-auc:0.99534	valid-auc:0.768391
[291]	train-auc:0.995436	valid-auc:0.768499
[292]	train-auc:0.995513	valid-auc:0.768532
[293]	train-auc:0.995568	valid-auc:0.768601
[294]	train-auc:0.995586	valid-auc:0.768594
[295]	train-auc:0.995649	valid-auc:0.768746
[296]	train-auc:0.995671	valid-auc:0.768741
[297]	train-auc:0.995797	valid-auc:0.768795
[298]	train-auc:0.99583	valid-auc:0.768881
[299]	train-auc:0.995923	valid-auc:0.768872
[300]	train-auc:0.996	valid-auc:0.768886
[301]	train-auc:0.996061	valid-auc:0.768768
[302]	train-auc:0.996094	valid-auc:0.768814
[303]	train-auc:0.99614	valid-auc:0.768946
[304]	train-auc:0.996183	valid-auc:0.768896
[305]	train-auc:0.996277	valid-auc:0.768771
[306]	train-auc:0.996336	valid-auc:0.768762
[307]	train-auc:0.996372	valid-auc:0.768753
[308]	train-auc:0.996448	valid-auc:0.768863
[309]	train-auc:0.996525	valid-auc:0.768836
[310]	train-auc:0.996642	valid-auc:0.768816
[311]	train-auc:0.996662	valid-auc:0.768817
[312]	train-auc:0.996679	valid-auc:0.768813
[313]	train-auc:0.996767	valid-auc:0.768792
[314]	train-auc:0.996808	valid-auc:0.768795
[315]	train-auc:0.996868	valid-auc:0.768886
[316]	train-auc:0.996923	valid-auc:0.768889
[317]	train-auc:0.996991	valid-auc:0.768867
[318]	train-auc:0.997026	valid-auc:0.768861
[319]	train-auc:0.997061	valid-auc:0.768874
[320]	train-auc:0.997117	valid-auc:0.768934
[321]	train-auc:0.997173	valid-auc:0.768899
[322]	train-auc:0.997191	valid-auc:0.768886
[323]	train-auc:0.997211	valid-auc:0.768854
[324]	train-auc:0.997266	valid-auc:0.768774
[325]	train-auc:0.997341	valid-auc:0.768711
[326]	train-auc:0.997403	valid-auc:0.768803
[327]	train-auc:0.997459	valid-auc:0.768752
[328]	train-auc:0.997507	valid-auc:0.768755
[329]	train-auc:0.997536	valid-auc:0.768705
[330]	train-auc:0.997557	valid-auc:0.768604
[331]	train-auc:0.997579	valid-auc:0.768578
[332]	train-auc:0.997596	valid-auc:0.768585
[333]	train-auc:0.997625	valid-auc:0.768565
[334]	train-auc:0.99767	valid-auc:0.76843
[335]	train-auc:0.99771	valid-auc:0.768519
[336]	train-auc:0.997739	valid-auc:0.768523
[337]	train-auc:0.997777	valid-auc:0.768629
[338]	train-auc:0.997828	valid-auc:0.768762
[339]	train-auc:0.997855	valid-auc:0.768695
[340]	train-auc:0.997877	valid-auc:0.768707
[341]	train-auc:0.997896	valid-auc:0.768731
[342]	train-auc:0.997966	valid-auc:0.768717
[343]	train-auc:0.997986	valid-auc:0.768661
[344]	train-auc:0.998005	valid-auc:0.768656
[345]	train-auc:0.998027	valid-auc:0.768536
[346]	train-auc:0.998044	valid-auc:0.768596
[347]	train-auc:0.998058	valid-auc:0.768623
[348]	train-auc:0.998094	valid-auc:0.768757
[349]	train-auc:0.998111	valid-auc:0.7688
[350]	train-auc:0.998151	valid-auc:0.768769
[351]	train-auc:0.998171	valid-auc:0.768703
[352]	train-auc:0.998215	valid-auc:0.768665
[353]	train-auc:0.998239	valid-auc:0.768595
Stopping. Best iteration:
[303]	train-auc:0.99614	valid-auc:0.768946

[mlcrate] Finished training fold 6 - took 3m31s - running score 0.7621515714285714
[mlcrate] Finished training 7 XGBoost models, took 27m35s

In [35]:
submit = make_submission(p_test_xgb)
submit.to_csv(f'{PATH}\\AV_Stud\\xgb_1_06.csv', index=False)
submit.head()


Out[35]:
id is_pass
0 1626_45 0.354804
1 11020_130 0.950365
2 12652_146 0.595852
3 7038_72 0.037997
4 888_71 0.162516

In [36]:
np.mean(p_test_xgb), np.std(p_test_xgb)


Out[36]:
(0.60203171, 0.25843671)

In [37]:
np.save(f'{PATH}\\AV_Stud\\stack_preds\\xgb_embedding', p_train_xgb)

In [38]:
np.save(f'{PATH}\\AV_Stud\\xgb_train_embedding', x_train_final_dnn)
np.save(f'{PATH}\\AV_Stud\\xgb_test_embedding', x_test_final_dnn)

In [ ]: