av_student_datafest_2_preprocessing-Copy1


imports


In [1]:
%load_ext autoreload
%autoreload 2

%matplotlib inline

In [2]:
import time
import xgboost as xgb
import lightgbm as lgb
import category_encoders as cat_ed
import gc, mlcrate, glob

from fastai.imports import *
from fastai.structured import *
from gplearn.genetic import SymbolicTransformer
from pandas_summary import DataFrameSummary
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
from IPython.display import display
from catboost import CatBoostClassifier
from scipy.cluster import hierarchy as hc
from collections import Counter

from sklearn import metrics
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import mean_squared_error, accuracy_score, roc_auc_score, log_loss
from sklearn.model_selection import KFold, StratifiedKFold, GridSearchCV, train_test_split
from sklearn.decomposition import PCA, TruncatedSVD, FastICA, FactorAnalysis
from sklearn.random_projection import GaussianRandomProjection, SparseRandomProjection
from sklearn.cluster import KMeans

from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.neural_network import MLPClassifier
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF

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

pd.option_context("display.max_rows", 20);
pd.option_context("display.max_columns", 20);

In [3]:
PATH = os.getcwd();
PATH


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

In [4]:
# df_raw_1 = pd.read_csv(f'{PATH}\\AV_Stud_2\\train.csv', low_memory=False)
# df_test_1 = pd.read_csv(f'{PATH}\\AV_Stud_2\\test.csv', low_memory=False)

# df_raw['last_new_job'] = df_raw_1['last_new_job']
# df_test['last_new_job'] = df_test_1['last_new_job']

# del df_raw_1, df_test_1

In [5]:
df_raw = pd.read_csv(f'{PATH}\\AV_Stud_2\\train.csv', low_memory=False)
df_test = pd.read_csv(f'{PATH}\\AV_Stud_2\\test.csv', low_memory=False)

stack_train_1 = pd.read_csv(f'{PATH}\\AV_Stud_2\\stack_train.csv')
stack_test_1 = pd.read_csv(f'{PATH}\\AV_Stud_2\\stack_test.csv')

# stack_train_2 = pd.read_csv(f'{PATH}\\AV_Stud_2\\stack_train_2.csv')
# stack_test_2 = pd.read_csv(f'{PATH}\\AV_Stud_2\\stack_test_2.csv')

train_67 = np.load(f'{PATH}\\AV_Stud_2\\train_67.npy')
test_67 = np.load(f'{PATH}\\AV_Stud_2\\test_67.npy')

# drop enrollee id

In [6]:
target = stack_train_1.target
stack_train_1.drop('target', axis=1, inplace=True)
df_raw.drop(['target'],axis=1,inplace=True)
#df_test.drop('enrollee_id', axis=1,inplace=True)

cleaning a bit


In [7]:
#target = df_raw.target.values
drop_col = ['enrollee_id']
df_raw.drop(drop_col, axis=1,inplace=True)
df_test.drop(drop_col, axis=1, inplace=True)

In [9]:
cols = ['city', 'city_development_index', 'gender',
       'relevent_experience', 'enrolled_university', 'enrolled_university_degree',
       'major_discipline', 'experience', 'company_size', 'company_type',
       'last_new_job', 'training_hours']
df_raw.columns = cols
df_test.columns = cols

In [8]:
for c in df_raw.columns:
    n = df_raw[c].nunique()
    print(c)
    if n <= 8:
        print(n, sorted(df_raw[c].value_counts().to_dict().items()))
    else:
        print(n)
    print(120 * '-')


city
123
------------------------------------------------------------------------------------------------------------------------
city_development_index
93
------------------------------------------------------------------------------------------------------------------------
gender
3 [('Female', 1188), ('Male', 12884), ('Other', 189)]
------------------------------------------------------------------------------------------------------------------------
relevent_experience
2 [('Has relevent experience', 13596), ('No relevent experience', 4763)]
------------------------------------------------------------------------------------------------------------------------
enrolled_university
3 [('Full time course', 3187), ('Part time course', 1171), ('no_enrollment', 13659)]
------------------------------------------------------------------------------------------------------------------------
enrolled_university_degree
5 [('Graduate', 10769), ('High School', 2032), ('Masters', 4319), ('Phd', 459), ('Primary School', 323)]
------------------------------------------------------------------------------------------------------------------------
major_discipline
6 [('Arts', 239), ('Business Degree', 307), ('Humanities', 688), ('No Major', 206), ('Other', 343), ('STEM', 13738)]
------------------------------------------------------------------------------------------------------------------------
experience
22
------------------------------------------------------------------------------------------------------------------------
company_size
8 [('10/49', 1466), ('100-500', 2698), ('1000-4999', 1399), ('10000+', 2044), ('50-99', 3120), ('500-999', 902), ('5000-9999', 591), ('<10', 1360)]
------------------------------------------------------------------------------------------------------------------------
company_type
6 [('Early Stage Startup', 582), ('Funded Startup', 1038), ('NGO', 534), ('Other', 119), ('Public Sector', 996), ('Pvt Ltd', 10051)]
------------------------------------------------------------------------------------------------------------------------
last_new_job
6 [('1', 7567), ('2', 2835), ('3', 1027), ('4', 1038), ('>4', 3339), ('never', 2186)]
------------------------------------------------------------------------------------------------------------------------
training_hours
241
------------------------------------------------------------------------------------------------------------------------
target
2 [(0, 15934), (1, 2425)]
------------------------------------------------------------------------------------------------------------------------

city and city_dev col's


In [10]:
#clean city split
df_raw['city'] = df_raw['city'].str.split('_',expand=True)[1]
df_raw['city'] = df_raw['city'].astype('int32')

df_test['city'] = df_test['city'].str.split('_',expand=True)[1]
df_test['city'] = df_test['city'].astype('int32')

In [11]:
df_raw['is_city_in_103_21_116_114_160'] = np.full(df_raw.shape[0], 0)
my_query = df_raw[(df_raw['city'] == 103)|(df_raw['city'] == 21) | (df_raw['city'] == 16) | (df_raw['city'] == 114) | (df_raw['city'] == 160)].index
df_raw.iloc[my_query, -1] = 1

In [12]:
df_test['is_city_in_103_21_116_114_160'] = np.full(df_test.shape[0], 0)

my_query = df_test[(df_test['city'] == 103)|(df_test['city'] == 21) | (df_test['city'] == 16) | (df_test['city'] == 114) | (df_test['city'] == 160)].index
df_test.iloc[my_query, -1] = 1

In [32]:
sns.countplot(df_raw[(df_raw['city'] == 103)|(df_raw['city'] == 21) | (df_raw['city'] == 16) |\
       (df_raw['city'] == 114) | (df_raw['city'] == 160)]['city'], data=df_raw, hue='target');



In [43]:
df_raw['city_development_index'].value_counts(ascending=False).head(20).cumsum()


Out[43]:
0.920     5185
0.624     6857
0.910     8511
0.926     9983
0.698    10638
0.897    11262
0.939    11806
0.855    12261
0.924    12579
0.804    12892
0.884    13173
0.887    13444
0.754    13708
0.913    13925
0.899    14119
0.802    14307
0.925    14485
0.893    14660
0.878    14816
0.743    14968
Name: city_development_index, dtype: int64

In [13]:
df_raw['is_dev_20'] = np.zeros(df_raw.shape[0])
my_query = df_raw.query('city_development_index<=.2').index
df_raw.iloc[my_query, -1] = 1

df_raw['is_dev_21_30'] = np.zeros(df_raw.shape[0])
my_query = df_raw.query('city_development_index>=.21 & city_development_index<=.3').index
df_raw.iloc[my_query, -1] = 1

df_raw['is_dev_31_40'] = np.zeros(df_raw.shape[0])
my_query = df_raw.query('city_development_index>=.31 & city_development_index<=.4').index
df_raw.iloc[my_query, -1] = 1

df_raw['is_dev_41_50'] = np.zeros(df_raw.shape[0])
my_query = df_raw.query('city_development_index>=.41 & city_development_index<=.5').index
df_raw.iloc[my_query, -1] = 1

df_raw['is_dev_51_60'] = np.zeros(df_raw.shape[0])
my_query = df_raw.query('city_development_index>=.51 & city_development_index<=.6').index
df_raw.iloc[my_query, -1] = 1

df_raw['is_dev_61_70'] = np.zeros(df_raw.shape[0])
my_query = df_raw.query('city_development_index>=.61 & city_development_index<=.7').index
df_raw.iloc[my_query, -1] = 1

df_raw['is_dev_71_80'] = np.zeros(df_raw.shape[0])
my_query = df_raw.query('city_development_index>=.71 & city_development_index<=.8').index
df_raw.iloc[my_query, -1] = 1

df_raw['is_dev_81_90'] = np.zeros(df_raw.shape[0])
my_query = df_raw.query('city_development_index>=.81 & city_development_index<=.9').index
df_raw.iloc[my_query, -1] = 1

df_raw['is_dev_91'] = np.zeros(df_raw.shape[0])
my_query = df_raw.query('city_development_index>=.91').index
df_raw.iloc[my_query, -1] = 1

In [14]:
df_test['is_dev_20'] = np.zeros(df_test.shape[0])
my_query = df_test.query('city_development_index<=.2').index
df_test.iloc[my_query, -1] = 1

df_test['is_dev_21_30'] = np.zeros(df_test.shape[0])
my_query = df_test.query('city_development_index>=.21 & city_development_index<=.3').index
df_test.iloc[my_query, -1] = 1

df_test['is_dev_31_40'] = np.zeros(df_test.shape[0])
my_query = df_test.query('city_development_index>=.31 & city_development_index<=.4').index
df_test.iloc[my_query, -1] = 1

df_test['is_dev_41_50'] = np.zeros(df_test.shape[0])
my_query = df_test.query('city_development_index>=.41 & city_development_index<=.5').index
df_test.iloc[my_query, -1] = 1

df_test['is_dev_51_60'] = np.zeros(df_test.shape[0])
my_query = df_test.query('city_development_index>=.51 & city_development_index<=.6').index
df_test.iloc[my_query, -1] = 1

df_test['is_dev_61_70'] = np.zeros(df_test.shape[0])
my_query = df_test.query('city_development_index>=.61 & city_development_index<=.7').index
df_test.iloc[my_query, -1] = 1

df_test['is_dev_71_80'] = np.zeros(df_test.shape[0])
my_query = df_test.query('city_development_index>=.71 & city_development_index<=.8').index
df_test.iloc[my_query, -1] = 1

df_test['is_dev_81_90'] = np.zeros(df_test.shape[0])
my_query = df_test.query('city_development_index>=.81 & city_development_index<=.9').index
df_test.iloc[my_query, -1] = 1

df_test['is_dev_91'] = np.zeros(df_test.shape[0])
my_query = df_test.query('city_development_index>=.91').index
df_test.iloc[my_query, -1] = 1

In [15]:
df_raw.shape, df_test.shape


Out[15]:
((18359, 22), (15021, 22))

company_size col


In [16]:
# merge both df's
df_raw['min_company_size'] = np.full(df_raw.shape[0], np.nan)
df_raw['max_company_size'] = np.full(df_raw.shape[0], np.nan)

my_query = df_raw[df_raw['company_size'] =='<10'].index
df_raw.iloc[my_query, -2] = 0
df_raw.iloc[my_query, -1] = 9

my_query = df_raw[df_raw['company_size'] =='10/49'].index
df_raw.iloc[my_query, -2] = 10
df_raw.iloc[my_query, -1] = 49

my_query = df_raw[df_raw['company_size'] =='50-99'].index
df_raw.iloc[my_query, -2] = 50
df_raw.iloc[my_query, -1] = 99

my_query = df_raw[df_raw['company_size'] =='100-500'].index
df_raw.iloc[my_query, -2] = 100
df_raw.iloc[my_query, -1] = 500

my_query = df_raw[df_raw['company_size'] =='500-999'].index
df_raw.iloc[my_query, -2] = 500
df_raw.iloc[my_query, -1] = 999

my_query = df_raw[df_raw['company_size'] =='1000-4999'].index
df_raw.iloc[my_query, -2] = 1000
df_raw.iloc[my_query, -1] = 4999

my_query = df_raw[df_raw['company_size'] =='5000-9999'].index
df_raw.iloc[my_query, -2] = 5000
df_raw.iloc[my_query, -1] = 9999

my_query = df_raw[df_raw['company_size'] =='10000+'].index
df_raw.iloc[my_query, -2] = 10000
df_raw.iloc[my_query, -1] = 15000

########################################################################

df_test['min_company_size'] = np.full(df_test.shape[0], -1)
df_test['max_company_size'] = np.full(df_test.shape[0], -1)

my_query = df_test[df_test['company_size'] =='<10'].index
df_test.iloc[my_query, -2] = 0
df_test.iloc[my_query, -1] = 9

my_query = df_test[df_test['company_size'] =='10/49'].index
df_test.iloc[my_query, -2] = 10
df_test.iloc[my_query, -1] = 49

my_query = df_test[df_test['company_size'] =='50-99'].index
df_test.iloc[my_query, -2] = 50
df_test.iloc[my_query, -1] = 99

my_query = df_test[df_test['company_size'] =='100-500'].index
df_test.iloc[my_query, -2] = 100
df_test.iloc[my_query, -1] = 500

my_query = df_test[df_test['company_size'] =='500-999'].index
df_test.iloc[my_query, -2] = 500
df_test.iloc[my_query, -1] = 999

my_query = df_test[df_test['company_size'] =='1000-4999'].index
df_test.iloc[my_query, -2] = 1000
df_test.iloc[my_query, -1] = 4999

my_query = df_test[df_test['company_size'] =='5000-9999'].index
df_test.iloc[my_query, -2] = 5000
df_test.iloc[my_query, -1] = 9999

my_query = df_test[df_test['company_size'] =='10000+'].index
df_test.iloc[my_query, -2] = 10000
df_test.iloc[my_query, -1] = 15000

# df_raw.drop('company_size', axis=1, inplace=True)
# df_test.drop('company_size', axis=1, inplace=True)

# fill na's now wrt to exp level (to do) ####################################################

#df_raw['company_size'].fillna(df_raw.groupby('experience')['company_size'].tranform('median'))
#drop_col.append('company_size')

In [ ]:
sns.countplot(data=df_raw,hue='target', x = 'min_company_size')

In [58]:
sns.countplot(data=df_raw,hue='target', x = 'max_company_size')


Out[58]:
<matplotlib.axes._subplots.AxesSubplot at 0x23d9e26b7f0>

In [ ]:
# df_raw['company_size'].str.split('-', expand=True)

In [ ]:
# df_raw[df_raw['company_size'] =='10/49']['company_size'].str.split('/', expand=True)[1]

last_new_job col


In [17]:
df_raw['last_new_job'].replace('>4',5, inplace = True)
df_raw['last_new_job'].replace('never',0, inplace = True)
df_raw['last_new_job'] = df_raw['last_new_job'].astype('float32')

df_test['last_new_job'].replace('>4',5, inplace = True)
df_test['last_new_job'].replace('never',0, inplace = True)
df_test['last_new_job'] = df_test['last_new_job'].astype('float32')

In [63]:
sns.countplot(df_raw['last_new_job'], hue='target', data=df_raw)


Out[63]:
<matplotlib.axes._subplots.AxesSubplot at 0x23d9e2fa470>

In [265]:
df_test['last_new_job'].fillna(method='ffill', inplace=True)

In [18]:
df_raw['last_new_job'].fillna(np.nan, inplace=True)
df_test['last_new_job'].fillna(np.nan, inplace=True)

In [65]:
sns.countplot(df_raw['last_new_job'], hue='target', data=df_raw)


Out[65]:
<matplotlib.axes._subplots.AxesSubplot at 0x23d9e2fd710>

experience col


In [77]:
plt.xticks(rotation=90)
sns.countplot('experience', data=df_raw, hue='target',);



In [19]:
df_raw['experience'].replace('>20',22, inplace = True)
df_test['experience'].replace('>20',22, inplace = True)

df_raw['experience'].replace('<1',.6, inplace = True)
df_test['experience'].replace('<1',.6, inplace = True)

df_raw['experience'] = df_raw['experience'].astype('float32')
df_test['experience'] = df_test['experience'].astype('float32')

In [20]:
df_raw['experience'].fillna(np.nan,inplace=True)
df_test['experience'].fillna(np.nan,inplace=True)

company_type col


In [21]:
df_raw['is_startup'] = np.full(df_raw.shape[0], 0)
my_query = df_raw[(df_raw['company_type'] == 'Funded Startup' )|(df_raw['company_type'] == 'Early Stage Startup')].index
df_raw.iloc[my_query, -1] = 1

df_test['is_startup'] = np.full(df_test.shape[0], 0)
my_query = df_test[(df_test['company_type'] == 'Funded Startup' )|(df_test['company_type'] == 'Early Stage Startup')].index
df_test.iloc[my_query, -1] = 1

In [22]:
df_raw['is_ltd'] = np.full(df_raw.shape[0], 0)
my_query = df_raw[(df_raw['company_type'] == 'Pvt Ltd' )|(df_raw['company_type'] == 'Public Sector')].index
df_raw.iloc[my_query, -1] = 1

df_test['is_ltd'] = np.full(df_test.shape[0], 0)
my_query = df_test[(df_test['company_type'] == 'Pvt Ltd' )|(df_test['company_type'] == 'Public Sector')].index
df_test.iloc[my_query, -1] = 1

In [23]:
df_raw['company_type'].fillna(value=np.nan, axis=0, inplace=True)
df_test['company_type'].fillna(value=np.nan, axis=0, inplace=True)

gender


In [165]:
df_raw.isnull().sum().sort_values(ascending=False).head()/len(df_raw)


Out[165]:
company_size                  0.260308
gender                        0.223215
major_discipline              0.154584
enrolled_university_degree    0.024892
enrolled_university           0.018628
dtype: float64

In [161]:
sns.countplot(df_raw['gender'],data=df_raw, hue='target');



In [178]:
df_raw['gender'].value_counts(normalize=True)


Out[178]:
Male       0.701781
Unknown    0.223215
Female     0.064709
Other      0.010295
Name: gender, dtype: float64

In [24]:
df_raw1 = df_raw.copy()
df_test1 = df_test.copy()

In [25]:
df_raw['gender'].fillna(value=np.nan, axis=0, inplace=True)
df_test['gender'].fillna(value=np.nan, axis=0, inplace=True)

In [182]:
df_raw.head(2)


Out[182]:
city city_development_index gender relevent_experience enrolled_university enrolled_university_degree major_discipline experience company_size company_type ... is_dev_41_50 is_dev_51_60 is_dev_61_70 is_dev_71_80 is_dev_81_90 is_dev_91 min_company_size max_company_size is_startup is_ltd
0 149 0.689 Male Has relevent experience no_enrollment Graduate STEM 3.0 100-500 Pvt Ltd ... 0.0 0.0 1.0 0.0 0.0 0.0 100 500 0 1
1 83 0.923 Male Has relevent experience no_enrollment Graduate STEM 14.0 <10 Funded Startup ... 0.0 0.0 0.0 0.0 0.0 1.0 0 9 1 0

2 rows × 27 columns


In [197]:
survived = 'work'
not_survived = 'not work'
fig, axes = plt.subplots(nrows=2, ncols=2,figsize=(10, 10))

women = df_raw[df_raw['gender']=='Female']
men = df_raw[df_raw['gender']=='Male']
other = df_raw[df_raw['gender']== 'Other']
unknown = df_raw[df_raw['gender']== 'Unknown']

ax = sns.distplot(women[women['target']==1].city_development_index, bins=18, label = survived, ax = axes[0][0], kde =False)
ax = sns.distplot(women[women['target']==0].city_development_index, bins=40, label = not_survived, ax = axes[0][0], kde =False)
ax.legend()
ax.set_title('Female')

ax = sns.distplot(men[men['target']==1].city_development_index, bins=18, label = survived, ax = axes[0][1], kde = False)
ax = sns.distplot(men[men['target']==0].city_development_index, bins=40, label = not_survived, ax = axes[0][1], kde = False)
ax.legend()
ax.set_title('Male')

ax = sns.distplot(other[other['target']==1].city_development_index, bins=18, label = survived, ax = axes[1][0], kde =False)
ax = sns.distplot(other[other['target']==0].city_development_index, bins=40, label = not_survived, ax = axes[1][0], kde =False)
ax.legend()
ax.set_title('Other')

ax = sns.distplot(unknown[unknown['target']==1].city_development_index, bins=18, label = survived, ax = axes[1][1], kde =False)
ax = sns.distplot(unknown[unknown['target']==0].city_development_index, bins=40, label = not_survived, ax = axes[1][1], kde =False)
ax.legend()
_ = ax.set_title('Unknown')


major discipline


In [177]:
plt.xticks(rotation=90)
sns.countplot('gender', data=df_raw, hue='company_type')


Out[177]:
<matplotlib.axes._subplots.AxesSubplot at 0x23da4eac278>

In [180]:
plt.xticks(rotation=90)
sns.countplot('major_discipline', data=df_raw, hue='company_type')


Out[180]:
<matplotlib.axes._subplots.AxesSubplot at 0x23da4e8c518>

In [26]:
df_raw['unaffliated_college'] = np.zeros(df_raw.shape[0])
df_test['unaffliated_college'] = np.zeros(df_test.shape[0])

for i in ['Phd', 'Graduate', 'Masters', 'High School', 'Primary School']:

    my_query = df_raw[(df_raw['enrolled_university'] == 'no_enrollment') & (df_raw['enrolled_university_degree'] == i)].index
    df_raw.iloc[my_query, -1] = 1
    
    my_query = df_test[(df_test['enrolled_university'] == 'no_enrollment') & (df_test['enrolled_university_degree'] == i)].index
    df_test.iloc[my_query, -1] = 1

df_raw['enroll_type__enroll_deg__major_in'] = df_raw.enrolled_university+'_'+df_raw.enrolled_university_degree\
                                                +'_'+df_raw.major_discipline
df_test['enroll_type__enroll_deg__major_in'] = df_test.enrolled_university+'_'+df_test.enrolled_university_degree\
                                                +'_'+df_test.major_discipline
    
df_raw['gender__major_in'] = df_raw.gender+'_'+df_raw.major_discipline
df_test['gender__major_in'] = df_test.gender+'_'+df_test.major_discipline

df_raw['enroll_deg__major_in'] = df_raw.enrolled_university_degree+'_'+df_raw.major_discipline
df_test['enroll_deg__major_in'] = df_test.enrolled_university_degree+'_'+df_test.major_discipline
    
df_raw['city_dev__max_comp_siz'] = df_raw.city.astype(str)+'_'+df_raw.max_company_size.astype(str)
df_test['city_dev__max_comp_siz'] = df_test.city.astype(str)+'_'+df_test.max_company_size.astype(str)

In [224]:
for i in ['Phd', 'Graduate', 'Masters', 'High School', 'Primary School'] : # enrolled_university_degree
    for j in ['Arts', 'Business Degree', 'Other', 'STEM', 'Humanities', 'No Major']: #major discipline
        print(i,'&&',j,'\nTrain-', df_raw[(df_raw['major_discipline'] == j ) & (df_raw['enrolled_university'] == 'no_enrollment') & (df_raw['enrolled_university_degree'] == i)].shape[0], \
             'Test-',df_test[(df_raw['major_discipline'] == j ) & (df_test['enrolled_university'] == 'no_enrollment') & (df_test['enrolled_university_degree'] == i)].shape[0])


Phd && Arts 
Train- 3 Test- 3
Phd && Business Degree 
Train- 5 Test- 9
Phd && Other 
Train- 5 Test- 6
Phd && STEM 
Train- 378 Test- 276
Phd && Humanities 
Train- 23 Test- 18
Phd && No Major 
Train- 0 Test- 2
Graduate && Arts 
Train- 179 Test- 88
Graduate && Business Degree 
Train- 179 Test- 111
Graduate && Other 
Train- 197 Test- 119
Graduate && STEM 
Train- 6994 Test- 4924
Graduate && Humanities 
Train- 391 Test- 252
Graduate && No Major 
Train- 153 Test- 81
Masters && Arts 
Train- 32 Test- 33
Masters && Business Degree 
Train- 75 Test- 45
Masters && Other 
Train- 76 Test- 64
Masters && STEM 
Train- 3172 Test- 2152
Masters && Humanities 
Train- 203 Test- 106
Masters && No Major 
Train- 23 Test- 39
High School && Arts 
Train- 0 Test- 12
High School && Business Degree 
Train- 0 Test- 14
High School && Other 
Train- 0 Test- 14
High School && STEM 
Train- 0 Test- 628
High School && Humanities 
Train- 0 Test- 39
High School && No Major 
Train- 0 Test- 10
Primary School && Arts 
Train- 0 Test- 0
Primary School && Business Degree 
Train- 0 Test- 2
Primary School && Other 
Train- 0 Test- 4
Primary School && STEM 
Train- 0 Test- 189
Primary School && Humanities 
Train- 0 Test- 15
Primary School && No Major 
Train- 0 Test- 4

In [172]:
plt.xticks(rotation=90)
sns.countplot('enrolled_university', data=df_raw, hue='company_type')


Out[172]:
<matplotlib.axes._subplots.AxesSubplot at 0x23da4bc0da0>

In [173]:
plt.xticks(rotation=90)
sns.countplot('enrolled_university_degree', data=df_raw, hue='company_type')


Out[173]:
<matplotlib.axes._subplots.AxesSubplot at 0x23da4cb5b70>

In [27]:
df_raw['major_discipline'].fillna(np.nan, axis=0, inplace=True)
df_test['major_discipline'].fillna(np.nan, axis=0, inplace=True)

df_raw['enrolled_university'].fillna(np.nan, axis=0, inplace=True)
df_test['enrolled_university'].fillna(np.nan, axis=0, inplace=True)

df_raw['enrolled_university_degree'].fillna(np.nan, axis=0, inplace=True)
df_test['enrolled_university_degree'].fillna(np.nan, axis=0, inplace=True)

In [268]:
df_test['last_new_job'].fillna(0,axis=0,inplace=True)

In [32]:
df_test.isnull().sum().sort_values(ascending=False).head(10)/len(df_test)


Out[32]:
gender__major_in                     0.336196
company_type                         0.288263
company_size                         0.269689
gender                               0.225551
enroll_type__enroll_deg__major_in    0.169696
enroll_deg__major_in                 0.159377
major_discipline                     0.159310
enrolled_university_degree           0.026297
last_new_job                         0.020238
enrolled_university                  0.018574
dtype: float64

In [33]:
df_raw.isnull().sum().sort_values(ascending=False).head(10)/len(df_raw)


Out[33]:
gender__major_in                     0.333569
company_type                         0.274470
company_size                         0.260308
max_company_size                     0.260308
min_company_size                     0.260308
gender                               0.223215
enroll_type__enroll_deg__major_in    0.164769
enroll_deg__major_in                 0.154584
major_discipline                     0.154584
enrolled_university_degree           0.024892
dtype: float64

start here


In [34]:
# This way we have randomness and are able to reproduce the behaviour within this cell.
# *****[Helper Script] Comes directly from a kaggle kernel with negligible changes... (not mine)*****

np.random.seed(13)
from sklearn.model_selection import KFold

def impact_coding(data, feature, target='y'):
    '''
    In this implementation we get the values and the dictionary as two different steps.
    This is just because initially we were ignoring the dictionary as a result variable.
    
    In this implementation the KFolds use shuffling. If you want reproducibility the cv 
    could be moved to a parameter.
    '''
    n_folds = 7
    n_inner_folds = 5
    impact_coded = pd.Series()
    
    oof_default_mean = data[target].mean() # Gobal mean to use by default (you could further tune this)
    kf = KFold(n_splits=n_folds, shuffle=True)
    oof_mean_cv = pd.DataFrame()
    split = 0
    for infold, oof in kf.split(data[feature]):
            impact_coded_cv = pd.Series()
            kf_inner = KFold(n_splits=n_inner_folds, shuffle=True)
            inner_split = 0
            inner_oof_mean_cv = pd.DataFrame()
            oof_default_inner_mean = data.iloc[infold][target].mean()
            for infold_inner, oof_inner in kf_inner.split(data.iloc[infold]):
                # The mean to apply to the inner oof split (a 1/n_folds % based on the rest)
                oof_mean = data.iloc[infold_inner].groupby(by=feature)[target].mean()
                impact_coded_cv = impact_coded_cv.append(data.iloc[infold].apply(
                            lambda x: oof_mean[x[feature]]
                                      if x[feature] in oof_mean.index
                                      else oof_default_inner_mean
                            , axis=1))

                # Also populate mapping (this has all group -> mean for all inner CV folds)
                inner_oof_mean_cv = inner_oof_mean_cv.join(pd.DataFrame(oof_mean), rsuffix=inner_split, how='outer')
                inner_oof_mean_cv.fillna(value=oof_default_inner_mean, inplace=True)
                inner_split += 1

            # Also populate mapping
            oof_mean_cv = oof_mean_cv.join(pd.DataFrame(inner_oof_mean_cv), rsuffix=split, how='outer')
            oof_mean_cv.fillna(value=oof_default_mean, inplace=True)
            split += 1
            
            impact_coded = impact_coded.append(data.iloc[oof].apply(
                            lambda x: inner_oof_mean_cv.loc[x[feature]].mean()
                                      if x[feature] in inner_oof_mean_cv.index
                                      else oof_default_mean
                            , axis=1))

    return impact_coded, oof_mean_cv.mean(axis=1), oof_default_mean

In [25]:
df_raw.get_ftype_counts()


Out[25]:
category:dense     7
float32:dense      2
float64:dense     12
int32:dense        4
int64:dense        2
dtype: int64

In [28]:
#drop id if original data is imported
#target = df_raw.target.values
#df_raw.drop(['target'],axis=1, inplace=True)
#df_test.drop('id', axis=1, inplace=True)

features = df_raw.columns
numeric_features = []
categorical_features = []
i = 0
index = []
for dtype, feature in zip(df_raw.dtypes, df_raw.columns):

    if dtype == object:
        #print(column)
        #print(train_data[column].describe())
        categorical_features.append(feature)
        index.append(i)
    else:
        numeric_features.append(feature)
    i +=1
train_cats(df_raw);
apply_cats(df_test, df_raw);
categorical_features


Out[28]:
['gender',
 'relevent_experience',
 'enrolled_university',
 'enrolled_university_degree',
 'major_discipline',
 'company_size',
 'company_type',
 'enroll_type__enroll_deg__major_in',
 'gender__major_in',
 'enroll_deg__major_in',
 'city_dev__max_comp_siz']

In [36]:
%%time

df_raw['target'] = target
# Apply the encoding to training and test data, and preserve the mapping
impact_coding_map = {}
# for f in categorical_features:
for f in categorical_features:
    print("Impact coding for {}".format(f))
    df_raw["impact_encoded_{}".format(f)], impact_coding_mapping, default_coding = impact_coding(df_raw, f,'target')
    impact_coding_map[f] = (impact_coding_mapping, default_coding)
    mapping, default_mean = impact_coding_map[f]
    df_test["impact_encoded_{}".format(f)] = df_test.apply(lambda x: mapping[x[f]]
                                                                         if x[f] in mapping
                                                                         else default_mean
                                                               , axis=1)

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


Impact coding for gender
Impact coding for relevent_experience
Impact coding for enrolled_university
Impact coding for enrolled_university_degree
Impact coding for major_discipline
Impact coding for company_size
Impact coding for company_type
Impact coding for enroll_type__enroll_deg__major_in
Impact coding for gender__major_in
Impact coding for enroll_deg__major_in
Impact coding for city_dev__max_comp_siz
Wall time: 9min 22s

In [29]:
df_raw = pd.get_dummies(df_raw,columns=categorical_features,prefix='dummy')
df_test = pd.get_dummies(df_test,columns=categorical_features,prefix='dummy')

In [30]:
X_train = df_raw.drop(categorical_features,axis=1)  #numeric ones
X_test  = df_test.drop(categorical_features,axis=1) #numeric ones

In [66]:
df_raw[:].fillna(method='ffill', inplace=True)
df_test[:].fillna(method='ffill', inplace=True)

In [ ]:
%%time
N_COMP = 15

print("\nStart decomposition process...")
print("PCA")
pca = PCA(n_components=N_COMP, random_state=17)
pca_results_X_train = pca.fit_transform(X_train)
pca_results_X_test = pca.transform(X_test)

In [ ]:
%%time
print("Append decomposition components to datasets...")

for i in range(1, N_COMP + 1):
    X_train['pca_' + str(i)] = pca_results_X_train[:, i - 1]
    X_test['pca_' + str(i)] = pca_results_X_test[:, i - 1]

In [24]:
def prepare(data_orig):
    data = pd.DataFrame()
    data['mean'] = data_orig.mean(axis=1)
    data['std'] = data_orig.std(axis=1)
    data['min'] = data_orig.min(axis=1)
    data['max'] = data_orig.max(axis=1)
    data['number_of_different'] = data_orig.nunique(axis=1)               # Number of diferent values in a row.
    data['non_zero_count'] = data_orig.fillna(0).astype(bool).sum(axis=1) # Number of non zero values
    return data

# Replace 0 with NaN to ignore them.
X_test_stats = prepare(X_test.replace(0,np.nan))
X_train_stats = prepare(X_train.replace(0, np.nan))

In [ ]:
from sklearn.cluster import KMeans

flist = [x for x in X_train.columns]

flist_kmeans = []
for ncl in range(2,11):
    cls = KMeans(n_clusters=ncl)
    cls.fit_predict(X_train[flist].values)
    X_train['kmeans_cluster_'+str(ncl)] = cls.predict(X_train[flist].values)
    X_test['kmeans_cluster_'+str(ncl)] = cls.predict(X_test[flist].values)
    flist_kmeans.append('kmeans_cluster_'+str(ncl))
print(flist_kmeans)

In [290]:
X_train_stack, X_test_stack = np.hstack((X_train, X_train_stats)), np.hstack((X_test, X_test_stats))

In [31]:
X_train_stack, X_test_stack = np.hstack((df_raw, stack_train_1)), np.hstack((df_test, stack_test_1))

In [71]:
params = {}
params['booster'] = 'gbtree'
params["objective"] = "binary:logistic"
params['eval_metric'] = 'logloss'
#params['eval_metric'] = 'auc'
params["eta"] = 0.05 #0.03
params["subsample"] = .7 #.85 was tried before
params["silent"] = 0
params['verbose'] = 1
params["max_depth"] = 7
params["seed"] = 1
params["max_delta_step"] = 1
params['scale_pos_weight'] =  0.13208780434664197
params["gamma"] = 10 #.5 #.1 #.2
params['colsample_bytree'] = 0.7
params['nrounds'] = 1000 #3600 #2000 #4000 #using lower no for demo
params['missing'] = 0
#params['max_leaves'] = 511
#params['verbose_eval'] = 50

In [72]:
model_xgb, p_train, p_test  = mlcrate.xgb.train_kfold(params, X_train_stack, target, X_test_stack\
                                                       , folds = 7,skip_checks = True, stratify=target, print_imp='final')


[mlcrate] Training 7 stratified XGBoost models on training set (18359, 54) with test set (15021, 54)
[mlcrate] Running fold 0, 15735 train samples, 2624 validation samples
[0]	train-logloss:0.675063	valid-logloss:0.675072
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.

Will train until valid-logloss hasn't improved in 50 rounds.
[1]	train-logloss:0.657603	valid-logloss:0.657621
[2]	train-logloss:0.640766	valid-logloss:0.640793
[3]	train-logloss:0.624551	valid-logloss:0.624587
[4]	train-logloss:0.608955	valid-logloss:0.609
[5]	train-logloss:0.593974	valid-logloss:0.594028
[6]	train-logloss:0.579604	valid-logloss:0.579666
[7]	train-logloss:0.56584	valid-logloss:0.565912
[8]	train-logloss:0.552677	valid-logloss:0.552757
[9]	train-logloss:0.540108	valid-logloss:0.540197
[10]	train-logloss:0.528127	valid-logloss:0.528225
[11]	train-logloss:0.516725	valid-logloss:0.516832
[12]	train-logloss:0.505896	valid-logloss:0.506012
[13]	train-logloss:0.49563	valid-logloss:0.495755
[14]	train-logloss:0.485918	valid-logloss:0.486052
[15]	train-logloss:0.47675	valid-logloss:0.476893
[16]	train-logloss:0.468118	valid-logloss:0.46827
[17]	train-logloss:0.46001	valid-logloss:0.460171
[18]	train-logloss:0.452416	valid-logloss:0.452585
[19]	train-logloss:0.445324	valid-logloss:0.445503
[20]	train-logloss:0.438724	valid-logloss:0.438911
[21]	train-logloss:0.432604	valid-logloss:0.4328
[22]	train-logloss:0.426952	valid-logloss:0.427158
[23]	train-logloss:0.421757	valid-logloss:0.421971
[24]	train-logloss:0.417007	valid-logloss:0.41723
[25]	train-logloss:0.412689	valid-logloss:0.412922
[26]	train-logloss:0.408793	valid-logloss:0.409034
[27]	train-logloss:0.405305	valid-logloss:0.405555
[28]	train-logloss:0.402213	valid-logloss:0.402472
[29]	train-logloss:0.399507	valid-logloss:0.399775
[30]	train-logloss:0.397173	valid-logloss:0.39745
[31]	train-logloss:0.3952	valid-logloss:0.395486
[32]	train-logloss:0.393577	valid-logloss:0.393872
[33]	train-logloss:0.392292	valid-logloss:0.392595
[34]	train-logloss:0.391333	valid-logloss:0.391646
[35]	train-logloss:0.39069	valid-logloss:0.391011
[36]	train-logloss:0.390351	valid-logloss:0.390681
[37]	train-logloss:0.389964	valid-logloss:0.390355
[38]	train-logloss:0.389834	valid-logloss:0.390213
[39]	train-logloss:0.38994	valid-logloss:0.390355
[40]	train-logloss:0.390233	valid-logloss:0.390665
[41]	train-logloss:0.390718	valid-logloss:0.391125
[42]	train-logloss:0.39141	valid-logloss:0.391887
[43]	train-logloss:0.392269	valid-logloss:0.392782
[44]	train-logloss:0.393303	valid-logloss:0.393886
[45]	train-logloss:0.394501	valid-logloss:0.395023
[46]	train-logloss:0.395808	valid-logloss:0.396384
[47]	train-logloss:0.397265	valid-logloss:0.397859
[48]	train-logloss:0.398799	valid-logloss:0.399355
[49]	train-logloss:0.400415	valid-logloss:0.400889
[50]	train-logloss:0.402178	valid-logloss:0.402643
[51]	train-logloss:0.403957	valid-logloss:0.40433
[52]	train-logloss:0.405863	valid-logloss:0.406226
[53]	train-logloss:0.407802	valid-logloss:0.408204
[54]	train-logloss:0.409802	valid-logloss:0.410164
[55]	train-logloss:0.411832	valid-logloss:0.412231
[56]	train-logloss:0.413902	valid-logloss:0.414302
[57]	train-logloss:0.415991	valid-logloss:0.416282
[58]	train-logloss:0.418131	valid-logloss:0.418412
[59]	train-logloss:0.420314	valid-logloss:0.420631
[60]	train-logloss:0.422488	valid-logloss:0.422849
[61]	train-logloss:0.424671	valid-logloss:0.425004
[62]	train-logloss:0.426916	valid-logloss:0.427277
[63]	train-logloss:0.429123	valid-logloss:0.429476
[64]	train-logloss:0.431278	valid-logloss:0.431584
[65]	train-logloss:0.433408	valid-logloss:0.433691
[66]	train-logloss:0.435526	valid-logloss:0.435754
[67]	train-logloss:0.43767	valid-logloss:0.437871
[68]	train-logloss:0.439696	valid-logloss:0.43986
[69]	train-logloss:0.441852	valid-logloss:0.442113
[70]	train-logloss:0.443822	valid-logloss:0.443906
[71]	train-logloss:0.44583	valid-logloss:0.445957
[72]	train-logloss:0.447845	valid-logloss:0.447941
[73]	train-logloss:0.449859	valid-logloss:0.44995
[74]	train-logloss:0.451745	valid-logloss:0.451805
[75]	train-logloss:0.453632	valid-logloss:0.453519
[76]	train-logloss:0.455553	valid-logloss:0.455431
[77]	train-logloss:0.457408	valid-logloss:0.457143
[78]	train-logloss:0.45917	valid-logloss:0.458873
[79]	train-logloss:0.460918	valid-logloss:0.460587
[80]	train-logloss:0.462671	valid-logloss:0.462449
[81]	train-logloss:0.464275	valid-logloss:0.464166
[82]	train-logloss:0.465785	valid-logloss:0.465719
[83]	train-logloss:0.467353	valid-logloss:0.467256
[84]	train-logloss:0.469208	valid-logloss:0.469105
[85]	train-logloss:0.471068	valid-logloss:0.47096
[86]	train-logloss:0.472536	valid-logloss:0.472398
[87]	train-logloss:0.473954	valid-logloss:0.473791
[88]	train-logloss:0.475122	valid-logloss:0.474908
Stopping. Best iteration:
[38]	train-logloss:0.389834	valid-logloss:0.390213

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 4s - running score 0.390213
[mlcrate] Running fold 1, 15735 train samples, 2624 validation samples
[0]	train-logloss:0.675063	valid-logloss:0.675072
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.

Will train until valid-logloss hasn't improved in 50 rounds.
[1]	train-logloss:0.657603	valid-logloss:0.657621
[2]	train-logloss:0.640766	valid-logloss:0.640793
[3]	train-logloss:0.624551	valid-logloss:0.624587
[4]	train-logloss:0.608955	valid-logloss:0.609
[5]	train-logloss:0.593974	valid-logloss:0.594028
[6]	train-logloss:0.579604	valid-logloss:0.579666
[7]	train-logloss:0.56584	valid-logloss:0.565912
[8]	train-logloss:0.552677	valid-logloss:0.552757
[9]	train-logloss:0.540108	valid-logloss:0.540197
[10]	train-logloss:0.528127	valid-logloss:0.528225
[11]	train-logloss:0.516725	valid-logloss:0.516832
[12]	train-logloss:0.505896	valid-logloss:0.506012
[13]	train-logloss:0.49563	valid-logloss:0.495755
[14]	train-logloss:0.485918	valid-logloss:0.486052
[15]	train-logloss:0.47675	valid-logloss:0.476893
[16]	train-logloss:0.468118	valid-logloss:0.46827
[17]	train-logloss:0.46001	valid-logloss:0.460171
[18]	train-logloss:0.452416	valid-logloss:0.452585
[19]	train-logloss:0.445324	valid-logloss:0.445503
[20]	train-logloss:0.438724	valid-logloss:0.438911
[21]	train-logloss:0.432604	valid-logloss:0.4328
[22]	train-logloss:0.426952	valid-logloss:0.427158
[23]	train-logloss:0.421757	valid-logloss:0.421971
[24]	train-logloss:0.417007	valid-logloss:0.41723
[25]	train-logloss:0.412689	valid-logloss:0.412922
[26]	train-logloss:0.408793	valid-logloss:0.409034
[27]	train-logloss:0.405305	valid-logloss:0.405555
[28]	train-logloss:0.402213	valid-logloss:0.402472
[29]	train-logloss:0.399507	valid-logloss:0.399775
[30]	train-logloss:0.397173	valid-logloss:0.39745
[31]	train-logloss:0.3952	valid-logloss:0.395486
[32]	train-logloss:0.393577	valid-logloss:0.393872
[33]	train-logloss:0.392292	valid-logloss:0.392595
[34]	train-logloss:0.391333	valid-logloss:0.391646
[35]	train-logloss:0.39069	valid-logloss:0.391011
[36]	train-logloss:0.390351	valid-logloss:0.390681
[37]	train-logloss:0.390001	valid-logloss:0.390237
[38]	train-logloss:0.390217	valid-logloss:0.39046
[39]	train-logloss:0.390328	valid-logloss:0.390448
[40]	train-logloss:0.390662	valid-logloss:0.390725
[41]	train-logloss:0.391199	valid-logloss:0.391194
[42]	train-logloss:0.391873	valid-logloss:0.391789
[43]	train-logloss:0.39278	valid-logloss:0.392577
[44]	train-logloss:0.393853	valid-logloss:0.393594
[45]	train-logloss:0.395023	valid-logloss:0.394677
[46]	train-logloss:0.396339	valid-logloss:0.395907
[47]	train-logloss:0.39783	valid-logloss:0.397335
[48]	train-logloss:0.39939	valid-logloss:0.398792
[49]	train-logloss:0.401041	valid-logloss:0.400343
[50]	train-logloss:0.402825	valid-logloss:0.402026
[51]	train-logloss:0.404617	valid-logloss:0.403703
[52]	train-logloss:0.406553	valid-logloss:0.405497
[53]	train-logloss:0.408523	valid-logloss:0.407422
[54]	train-logloss:0.410512	valid-logloss:0.409369
[55]	train-logloss:0.412614	valid-logloss:0.411357
[56]	train-logloss:0.414724	valid-logloss:0.413373
[57]	train-logloss:0.416916	valid-logloss:0.415524
[58]	train-logloss:0.419051	valid-logloss:0.417553
[59]	train-logloss:0.421284	valid-logloss:0.419723
[60]	train-logloss:0.423476	valid-logloss:0.421743
[61]	train-logloss:0.425637	valid-logloss:0.423735
[62]	train-logloss:0.427767	valid-logloss:0.425714
[63]	train-logloss:0.429916	valid-logloss:0.427813
[64]	train-logloss:0.43213	valid-logloss:0.429813
[65]	train-logloss:0.434339	valid-logloss:0.431956
[66]	train-logloss:0.436501	valid-logloss:0.43403
[67]	train-logloss:0.438637	valid-logloss:0.435987
[68]	train-logloss:0.440761	valid-logloss:0.43786
[69]	train-logloss:0.442912	valid-logloss:0.439954
[70]	train-logloss:0.444939	valid-logloss:0.441902
[71]	train-logloss:0.446924	valid-logloss:0.443733
[72]	train-logloss:0.448941	valid-logloss:0.445673
[73]	train-logloss:0.450973	valid-logloss:0.447655
[74]	train-logloss:0.45294	valid-logloss:0.449575
[75]	train-logloss:0.454875	valid-logloss:0.451332
[76]	train-logloss:0.456763	valid-logloss:0.453074
[77]	train-logloss:0.459061	valid-logloss:0.455366
[78]	train-logloss:0.461258	valid-logloss:0.457559
[79]	train-logloss:0.462976	valid-logloss:0.459158
[80]	train-logloss:0.464678	valid-logloss:0.460815
[81]	train-logloss:0.466251	valid-logloss:0.462113
[82]	train-logloss:0.468199	valid-logloss:0.464057
[83]	train-logloss:0.47011	valid-logloss:0.465962
[84]	train-logloss:0.471604	valid-logloss:0.467237
[85]	train-logloss:0.473056	valid-logloss:0.468611
[86]	train-logloss:0.474459	valid-logloss:0.469848
[87]	train-logloss:0.47577	valid-logloss:0.471111
Stopping. Best iteration:
[37]	train-logloss:0.390001	valid-logloss:0.390237

[mlcrate] Finished training fold 1 - took 4s - running score 0.390225
[mlcrate] Running fold 2, 15736 train samples, 2623 validation samples
[0]	train-logloss:0.675062	valid-logloss:0.675074
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.

Will train until valid-logloss hasn't improved in 50 rounds.
[1]	train-logloss:0.657602	valid-logloss:0.657626
[2]	train-logloss:0.640765	valid-logloss:0.640801
[3]	train-logloss:0.62455	valid-logloss:0.624597
[4]	train-logloss:0.608953	valid-logloss:0.609012
[5]	train-logloss:0.593971	valid-logloss:0.594043
[6]	train-logloss:0.579601	valid-logloss:0.579684
[7]	train-logloss:0.565837	valid-logloss:0.565932
[8]	train-logloss:0.552673	valid-logloss:0.55278
[9]	train-logloss:0.540104	valid-logloss:0.540223
[10]	train-logloss:0.528122	valid-logloss:0.528253
[11]	train-logloss:0.51672	valid-logloss:0.516863
[12]	train-logloss:0.50589	valid-logloss:0.506045
[13]	train-logloss:0.495624	valid-logloss:0.49579
[14]	train-logloss:0.485911	valid-logloss:0.486089
[15]	train-logloss:0.476744	valid-logloss:0.476934
[16]	train-logloss:0.468111	valid-logloss:0.468313
[17]	train-logloss:0.460002	valid-logloss:0.460216
[18]	train-logloss:0.452408	valid-logloss:0.452633
[19]	train-logloss:0.445316	valid-logloss:0.445553
[20]	train-logloss:0.438715	valid-logloss:0.438964
[21]	train-logloss:0.432595	valid-logloss:0.432856
[22]	train-logloss:0.426943	valid-logloss:0.427216
[23]	train-logloss:0.421747	valid-logloss:0.422032
[24]	train-logloss:0.416996	valid-logloss:0.417293
[25]	train-logloss:0.412679	valid-logloss:0.412987
[26]	train-logloss:0.408781	valid-logloss:0.409102
[27]	train-logloss:0.405293	valid-logloss:0.405625
[28]	train-logloss:0.402201	valid-logloss:0.402545
[29]	train-logloss:0.399494	valid-logloss:0.39985
[30]	train-logloss:0.39716	valid-logloss:0.397528
[31]	train-logloss:0.395187	valid-logloss:0.395567
[32]	train-logloss:0.393563	valid-logloss:0.393955
[33]	train-logloss:0.392278	valid-logloss:0.392681
[34]	train-logloss:0.391318	valid-logloss:0.391734
[35]	train-logloss:0.390675	valid-logloss:0.391102
[36]	train-logloss:0.390335	valid-logloss:0.390774
[37]	train-logloss:0.389968	valid-logloss:0.390404
[38]	train-logloss:0.389843	valid-logloss:0.390261
[39]	train-logloss:0.389938	valid-logloss:0.390333
[40]	train-logloss:0.390297	valid-logloss:0.390705
[41]	train-logloss:0.390822	valid-logloss:0.391218
[42]	train-logloss:0.391567	valid-logloss:0.391937
[43]	train-logloss:0.39247	valid-logloss:0.392797
[44]	train-logloss:0.393526	valid-logloss:0.393865
[45]	train-logloss:0.394736	valid-logloss:0.395054
[46]	train-logloss:0.396071	valid-logloss:0.396369
[47]	train-logloss:0.397518	valid-logloss:0.397797
[48]	train-logloss:0.399056	valid-logloss:0.399331
[49]	train-logloss:0.400756	valid-logloss:0.401017
[50]	train-logloss:0.402505	valid-logloss:0.40277
[51]	train-logloss:0.404338	valid-logloss:0.404583
[52]	train-logloss:0.406241	valid-logloss:0.406499
[53]	train-logloss:0.408215	valid-logloss:0.408448
[54]	train-logloss:0.410199	valid-logloss:0.41041
[55]	train-logloss:0.412287	valid-logloss:0.412461
[56]	train-logloss:0.414325	valid-logloss:0.414494
[57]	train-logloss:0.416452	valid-logloss:0.416617
[58]	train-logloss:0.418631	valid-logloss:0.418769
[59]	train-logloss:0.420835	valid-logloss:0.420944
[60]	train-logloss:0.423003	valid-logloss:0.42309
[61]	train-logloss:0.425194	valid-logloss:0.425227
[62]	train-logloss:0.427351	valid-logloss:0.427352
[63]	train-logloss:0.429539	valid-logloss:0.429533
[64]	train-logloss:0.431746	valid-logloss:0.431716
[65]	train-logloss:0.433912	valid-logloss:0.433845
[66]	train-logloss:0.436087	valid-logloss:0.435996
[67]	train-logloss:0.438287	valid-logloss:0.438153
[68]	train-logloss:0.440403	valid-logloss:0.440244
[69]	train-logloss:0.442584	valid-logloss:0.442397
[70]	train-logloss:0.444669	valid-logloss:0.444476
[71]	train-logloss:0.446631	valid-logloss:0.446435
[72]	train-logloss:0.448625	valid-logloss:0.448397
[73]	train-logloss:0.450535	valid-logloss:0.450265
[74]	train-logloss:0.452489	valid-logloss:0.452178
[75]	train-logloss:0.454873	valid-logloss:0.454572
[76]	train-logloss:0.456638	valid-logloss:0.456289
[77]	train-logloss:0.458491	valid-logloss:0.458102
[78]	train-logloss:0.46027	valid-logloss:0.460057
[79]	train-logloss:0.462037	valid-logloss:0.461785
[80]	train-logloss:0.464143	valid-logloss:0.4639
[81]	train-logloss:0.465891	valid-logloss:0.465614
[82]	train-logloss:0.467597	valid-logloss:0.467433
[83]	train-logloss:0.469489	valid-logloss:0.469332
[84]	train-logloss:0.470985	valid-logloss:0.470918
[85]	train-logloss:0.472407	valid-logloss:0.472317
[86]	train-logloss:0.473716	valid-logloss:0.473591
[87]	train-logloss:0.475038	valid-logloss:0.474869
[88]	train-logloss:0.47663	valid-logloss:0.476468
Stopping. Best iteration:
[38]	train-logloss:0.389843	valid-logloss:0.390261

[mlcrate] Finished training fold 2 - took 4s - running score 0.390237
[mlcrate] Running fold 3, 15737 train samples, 2622 validation samples
[0]	train-logloss:0.675065	valid-logloss:0.675058
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.

Will train until valid-logloss hasn't improved in 50 rounds.
[1]	train-logloss:0.657607	valid-logloss:0.657593
[2]	train-logloss:0.640773	valid-logloss:0.640751
[3]	train-logloss:0.624561	valid-logloss:0.624531
[4]	train-logloss:0.608967	valid-logloss:0.60893
[5]	train-logloss:0.593988	valid-logloss:0.593943
[6]	train-logloss:0.57962	valid-logloss:0.579568
[7]	train-logloss:0.565859	valid-logloss:0.565799
[8]	train-logloss:0.552698	valid-logloss:0.552631
[9]	train-logloss:0.540132	valid-logloss:0.540057
[10]	train-logloss:0.528152	valid-logloss:0.528071
[11]	train-logloss:0.516753	valid-logloss:0.516664
[12]	train-logloss:0.505926	valid-logloss:0.50583
[13]	train-logloss:0.495662	valid-logloss:0.495558
[14]	train-logloss:0.485953	valid-logloss:0.485841
[15]	train-logloss:0.476788	valid-logloss:0.476669
[16]	train-logloss:0.468158	valid-logloss:0.468031
[17]	train-logloss:0.460052	valid-logloss:0.459918
[18]	train-logloss:0.45246	valid-logloss:0.452319
[19]	train-logloss:0.445371	valid-logloss:0.445222
[20]	train-logloss:0.438773	valid-logloss:0.438617
[21]	train-logloss:0.432655	valid-logloss:0.432492
[22]	train-logloss:0.427006	valid-logloss:0.426835
[23]	train-logloss:0.421813	valid-logloss:0.421635
[24]	train-logloss:0.417065	valid-logloss:0.416879
[25]	train-logloss:0.41275	valid-logloss:0.412557
[26]	train-logloss:0.408856	valid-logloss:0.408655
[27]	train-logloss:0.40537	valid-logloss:0.405162
[28]	train-logloss:0.402281	valid-logloss:0.402065
[29]	train-logloss:0.399577	valid-logloss:0.399354
[30]	train-logloss:0.397245	valid-logloss:0.397015
[31]	train-logloss:0.395275	valid-logloss:0.395037
[32]	train-logloss:0.393654	valid-logloss:0.393409
[33]	train-logloss:0.392371	valid-logloss:0.392119
[34]	train-logloss:0.391415	valid-logloss:0.391155
[35]	train-logloss:0.390774	valid-logloss:0.390506
[36]	train-logloss:0.390437	valid-logloss:0.390162
[37]	train-logloss:0.390393	valid-logloss:0.390111
[38]	train-logloss:0.390237	valid-logloss:0.390043
[39]	train-logloss:0.390329	valid-logloss:0.390235
[40]	train-logloss:0.390617	valid-logloss:0.390615
[41]	train-logloss:0.391086	valid-logloss:0.39119
[42]	train-logloss:0.391735	valid-logloss:0.391963
[43]	train-logloss:0.392613	valid-logloss:0.392931
[44]	train-logloss:0.393617	valid-logloss:0.394068
[45]	train-logloss:0.394768	valid-logloss:0.395322
[46]	train-logloss:0.396066	valid-logloss:0.396746
[47]	train-logloss:0.397478	valid-logloss:0.3983
[48]	train-logloss:0.398992	valid-logloss:0.399895
[49]	train-logloss:0.400615	valid-logloss:0.401644
[50]	train-logloss:0.402336	valid-logloss:0.403511
[51]	train-logloss:0.404045	valid-logloss:0.405398
[52]	train-logloss:0.405941	valid-logloss:0.40738
[53]	train-logloss:0.40784	valid-logloss:0.409407
[54]	train-logloss:0.409803	valid-logloss:0.411495
[55]	train-logloss:0.411771	valid-logloss:0.413618
[56]	train-logloss:0.413811	valid-logloss:0.415834
[57]	train-logloss:0.415819	valid-logloss:0.418042
[58]	train-logloss:0.417851	valid-logloss:0.420225
[59]	train-logloss:0.419973	valid-logloss:0.422483
[60]	train-logloss:0.422121	valid-logloss:0.424817
[61]	train-logloss:0.424263	valid-logloss:0.42707
[62]	train-logloss:0.426444	valid-logloss:0.429394
[63]	train-logloss:0.428687	valid-logloss:0.431747
[64]	train-logloss:0.430793	valid-logloss:0.434029
[65]	train-logloss:0.432877	valid-logloss:0.436235
[66]	train-logloss:0.434968	valid-logloss:0.438475
[67]	train-logloss:0.437022	valid-logloss:0.440673
[68]	train-logloss:0.439221	valid-logloss:0.442979
[69]	train-logloss:0.441352	valid-logloss:0.445279
[70]	train-logloss:0.443341	valid-logloss:0.447384
[71]	train-logloss:0.445436	valid-logloss:0.449598
[72]	train-logloss:0.447435	valid-logloss:0.451795
[73]	train-logloss:0.449445	valid-logloss:0.454036
[74]	train-logloss:0.45139	valid-logloss:0.456107
[75]	train-logloss:0.453244	valid-logloss:0.458185
[76]	train-logloss:0.455095	valid-logloss:0.460206
[77]	train-logloss:0.456879	valid-logloss:0.462144
[78]	train-logloss:0.458655	valid-logloss:0.464145
[79]	train-logloss:0.460381	valid-logloss:0.466144
[80]	train-logloss:0.461961	valid-logloss:0.467889
[81]	train-logloss:0.463613	valid-logloss:0.469678
[82]	train-logloss:0.465261	valid-logloss:0.471405
[83]	train-logloss:0.466806	valid-logloss:0.47308
[84]	train-logloss:0.468217	valid-logloss:0.474641
[85]	train-logloss:0.470019	valid-logloss:0.476445
[86]	train-logloss:0.471843	valid-logloss:0.47827
[87]	train-logloss:0.473529	valid-logloss:0.479957
[88]	train-logloss:0.47522	valid-logloss:0.481649
Stopping. Best iteration:
[38]	train-logloss:0.390237	valid-logloss:0.390043

[mlcrate] Finished training fold 3 - took 4s - running score 0.3901885
[mlcrate] Running fold 4, 15737 train samples, 2622 validation samples
[0]	train-logloss:0.675065	valid-logloss:0.675058
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.

Will train until valid-logloss hasn't improved in 50 rounds.
[1]	train-logloss:0.657607	valid-logloss:0.657593
[2]	train-logloss:0.640773	valid-logloss:0.640751
[3]	train-logloss:0.624561	valid-logloss:0.624531
[4]	train-logloss:0.608967	valid-logloss:0.60893
[5]	train-logloss:0.593988	valid-logloss:0.593943
[6]	train-logloss:0.57962	valid-logloss:0.579568
[7]	train-logloss:0.565859	valid-logloss:0.565799
[8]	train-logloss:0.552698	valid-logloss:0.552631
[9]	train-logloss:0.540132	valid-logloss:0.540057
[10]	train-logloss:0.528152	valid-logloss:0.528071
[11]	train-logloss:0.516753	valid-logloss:0.516664
[12]	train-logloss:0.505926	valid-logloss:0.50583
[13]	train-logloss:0.495662	valid-logloss:0.495558
[14]	train-logloss:0.485953	valid-logloss:0.485841
[15]	train-logloss:0.476788	valid-logloss:0.476669
[16]	train-logloss:0.468158	valid-logloss:0.468031
[17]	train-logloss:0.460052	valid-logloss:0.459918
[18]	train-logloss:0.45246	valid-logloss:0.452319
[19]	train-logloss:0.445371	valid-logloss:0.445222
[20]	train-logloss:0.438773	valid-logloss:0.438617
[21]	train-logloss:0.432655	valid-logloss:0.432492
[22]	train-logloss:0.427006	valid-logloss:0.426835
[23]	train-logloss:0.421813	valid-logloss:0.421635
[24]	train-logloss:0.417065	valid-logloss:0.416879
[25]	train-logloss:0.41275	valid-logloss:0.412557
[26]	train-logloss:0.408856	valid-logloss:0.408655
[27]	train-logloss:0.40537	valid-logloss:0.405162
[28]	train-logloss:0.402281	valid-logloss:0.402065
[29]	train-logloss:0.399577	valid-logloss:0.399354
[30]	train-logloss:0.397245	valid-logloss:0.397015
[31]	train-logloss:0.395275	valid-logloss:0.395037
[32]	train-logloss:0.393654	valid-logloss:0.393409
[33]	train-logloss:0.392371	valid-logloss:0.392119
[34]	train-logloss:0.391415	valid-logloss:0.391155
[35]	train-logloss:0.390774	valid-logloss:0.390506
[36]	train-logloss:0.390438	valid-logloss:0.390163
[37]	train-logloss:0.390393	valid-logloss:0.39011
[38]	train-logloss:0.390264	valid-logloss:0.390051
[39]	train-logloss:0.390354	valid-logloss:0.390224
[40]	train-logloss:0.390654	valid-logloss:0.390545
[41]	train-logloss:0.391181	valid-logloss:0.391086
[42]	train-logloss:0.391857	valid-logloss:0.391851
[43]	train-logloss:0.392733	valid-logloss:0.392746
[44]	train-logloss:0.393724	valid-logloss:0.393837
[45]	train-logloss:0.394898	valid-logloss:0.395026
[46]	train-logloss:0.396147	valid-logloss:0.396377
[47]	train-logloss:0.397517	valid-logloss:0.397761
[48]	train-logloss:0.399054	valid-logloss:0.399404
[49]	train-logloss:0.400673	valid-logloss:0.401048
[50]	train-logloss:0.402424	valid-logloss:0.40281
[51]	train-logloss:0.404197	valid-logloss:0.404625
[52]	train-logloss:0.406095	valid-logloss:0.406539
[53]	train-logloss:0.408015	valid-logloss:0.408483
[54]	train-logloss:0.410001	valid-logloss:0.410485
[55]	train-logloss:0.411982	valid-logloss:0.412578
[56]	train-logloss:0.414041	valid-logloss:0.414757
[57]	train-logloss:0.416087	valid-logloss:0.416843
[58]	train-logloss:0.418195	valid-logloss:0.41901
[59]	train-logloss:0.420361	valid-logloss:0.421217
[60]	train-logloss:0.42255	valid-logloss:0.423471
[61]	train-logloss:0.424771	valid-logloss:0.425708
[62]	train-logloss:0.426979	valid-logloss:0.427926
[63]	train-logloss:0.429128	valid-logloss:0.430106
[64]	train-logloss:0.431309	valid-logloss:0.432389
[65]	train-logloss:0.433493	valid-logloss:0.434601
[66]	train-logloss:0.435668	valid-logloss:0.436854
[67]	train-logloss:0.437799	valid-logloss:0.439057
[68]	train-logloss:0.439893	valid-logloss:0.44119
[69]	train-logloss:0.441884	valid-logloss:0.443227
[70]	train-logloss:0.443889	valid-logloss:0.445271
[71]	train-logloss:0.445881	valid-logloss:0.447273
[72]	train-logloss:0.447899	valid-logloss:0.449395
[73]	train-logloss:0.44982	valid-logloss:0.451396
[74]	train-logloss:0.451752	valid-logloss:0.453367
[75]	train-logloss:0.453722	valid-logloss:0.455463
[76]	train-logloss:0.455556	valid-logloss:0.457314
[77]	train-logloss:0.457364	valid-logloss:0.459134
[78]	train-logloss:0.459121	valid-logloss:0.460895
[79]	train-logloss:0.460768	valid-logloss:0.462598
[80]	train-logloss:0.462428	valid-logloss:0.46438
[81]	train-logloss:0.464072	valid-logloss:0.466112
[82]	train-logloss:0.466	valid-logloss:0.468038
[83]	train-logloss:0.467445	valid-logloss:0.469576
[84]	train-logloss:0.46899	valid-logloss:0.471169
[85]	train-logloss:0.470479	valid-logloss:0.472668
[86]	train-logloss:0.472219	valid-logloss:0.474406
[87]	train-logloss:0.473504	valid-logloss:0.475803
[88]	train-logloss:0.475129	valid-logloss:0.477426
Stopping. Best iteration:
[38]	train-logloss:0.390264	valid-logloss:0.390051

[mlcrate] Finished training fold 4 - took 4s - running score 0.390161
[mlcrate] Running fold 5, 15737 train samples, 2622 validation samples
[0]	train-logloss:0.675065	valid-logloss:0.675058
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.

Will train until valid-logloss hasn't improved in 50 rounds.
[1]	train-logloss:0.657607	valid-logloss:0.657593
[2]	train-logloss:0.640773	valid-logloss:0.640751
[3]	train-logloss:0.624561	valid-logloss:0.624531
[4]	train-logloss:0.608967	valid-logloss:0.60893
[5]	train-logloss:0.593988	valid-logloss:0.593943
[6]	train-logloss:0.57962	valid-logloss:0.579568
[7]	train-logloss:0.565859	valid-logloss:0.565799
[8]	train-logloss:0.552698	valid-logloss:0.552631
[9]	train-logloss:0.540132	valid-logloss:0.540057
[10]	train-logloss:0.528152	valid-logloss:0.528071
[11]	train-logloss:0.516753	valid-logloss:0.516664
[12]	train-logloss:0.505926	valid-logloss:0.50583
[13]	train-logloss:0.495662	valid-logloss:0.495558
[14]	train-logloss:0.485953	valid-logloss:0.485841
[15]	train-logloss:0.476788	valid-logloss:0.476669
[16]	train-logloss:0.468158	valid-logloss:0.468031
[17]	train-logloss:0.460052	valid-logloss:0.459918
[18]	train-logloss:0.45246	valid-logloss:0.452319
[19]	train-logloss:0.445371	valid-logloss:0.445222
[20]	train-logloss:0.438773	valid-logloss:0.438617
[21]	train-logloss:0.432655	valid-logloss:0.432492
[22]	train-logloss:0.427006	valid-logloss:0.426835
[23]	train-logloss:0.421813	valid-logloss:0.421635
[24]	train-logloss:0.417065	valid-logloss:0.416879
[25]	train-logloss:0.41275	valid-logloss:0.412557
[26]	train-logloss:0.408856	valid-logloss:0.408655
[27]	train-logloss:0.40537	valid-logloss:0.405162
[28]	train-logloss:0.402281	valid-logloss:0.402065
[29]	train-logloss:0.399577	valid-logloss:0.399354
[30]	train-logloss:0.397245	valid-logloss:0.397015
[31]	train-logloss:0.395275	valid-logloss:0.395037
[32]	train-logloss:0.393654	valid-logloss:0.393409
[33]	train-logloss:0.392371	valid-logloss:0.392119
[34]	train-logloss:0.391415	valid-logloss:0.391155
[35]	train-logloss:0.390774	valid-logloss:0.390506
[36]	train-logloss:0.390141	valid-logloss:0.389883
[37]	train-logloss:0.389761	valid-logloss:0.389539
[38]	train-logloss:0.389613	valid-logloss:0.389428
[39]	train-logloss:0.389708	valid-logloss:0.389565
[40]	train-logloss:0.390019	valid-logloss:0.38993
[41]	train-logloss:0.390528	valid-logloss:0.390497
[42]	train-logloss:0.391176	valid-logloss:0.391196
[43]	train-logloss:0.392057	valid-logloss:0.392126
[44]	train-logloss:0.393091	valid-logloss:0.393226
[45]	train-logloss:0.394247	valid-logloss:0.394455
[46]	train-logloss:0.395533	valid-logloss:0.395804
[47]	train-logloss:0.396959	valid-logloss:0.397306
[48]	train-logloss:0.398484	valid-logloss:0.398908
[49]	train-logloss:0.400106	valid-logloss:0.400613
[50]	train-logloss:0.401834	valid-logloss:0.402377
[51]	train-logloss:0.403641	valid-logloss:0.404237
[52]	train-logloss:0.405534	valid-logloss:0.406203
[53]	train-logloss:0.407484	valid-logloss:0.408169
[54]	train-logloss:0.409481	valid-logloss:0.41025
[55]	train-logloss:0.411481	valid-logloss:0.412333
[56]	train-logloss:0.413494	valid-logloss:0.414434
[57]	train-logloss:0.415591	valid-logloss:0.416616
[58]	train-logloss:0.417727	valid-logloss:0.418847
[59]	train-logloss:0.41986	valid-logloss:0.421077
[60]	train-logloss:0.421975	valid-logloss:0.423233
[61]	train-logloss:0.424145	valid-logloss:0.425521
[62]	train-logloss:0.426265	valid-logloss:0.427723
[63]	train-logloss:0.428489	valid-logloss:0.429922
[64]	train-logloss:0.430684	valid-logloss:0.432093
[65]	train-logloss:0.432854	valid-logloss:0.434348
[66]	train-logloss:0.434986	valid-logloss:0.436565
[67]	train-logloss:0.437048	valid-logloss:0.438723
[68]	train-logloss:0.439166	valid-logloss:0.440932
[69]	train-logloss:0.441251	valid-logloss:0.443137
[70]	train-logloss:0.443291	valid-logloss:0.445285
[71]	train-logloss:0.445324	valid-logloss:0.447309
[72]	train-logloss:0.447208	valid-logloss:0.449251
[73]	train-logloss:0.449171	valid-logloss:0.451341
[74]	train-logloss:0.451128	valid-logloss:0.453284
[75]	train-logloss:0.453043	valid-logloss:0.45517
[76]	train-logloss:0.45485	valid-logloss:0.457036
[77]	train-logloss:0.456717	valid-logloss:0.458964
[78]	train-logloss:0.458435	valid-logloss:0.46077
[79]	train-logloss:0.460181	valid-logloss:0.462575
[80]	train-logloss:0.461846	valid-logloss:0.464312
[81]	train-logloss:0.46384	valid-logloss:0.466308
[82]	train-logloss:0.465878	valid-logloss:0.468348
[83]	train-logloss:0.467858	valid-logloss:0.47033
[84]	train-logloss:0.469334	valid-logloss:0.471852
[85]	train-logloss:0.47076	valid-logloss:0.473304
[86]	train-logloss:0.472138	valid-logloss:0.474728
[87]	train-logloss:0.473518	valid-logloss:0.476176
[88]	train-logloss:0.474844	valid-logloss:0.477567
Stopping. Best iteration:
[38]	train-logloss:0.389613	valid-logloss:0.389428

[mlcrate] Finished training fold 5 - took 4s - running score 0.39003883333333333
[mlcrate] Running fold 6, 15737 train samples, 2622 validation samples
[0]	train-logloss:0.675065	valid-logloss:0.675058
Multiple eval metrics have been passed: 'valid-logloss' will be used for early stopping.

Will train until valid-logloss hasn't improved in 50 rounds.
[1]	train-logloss:0.657607	valid-logloss:0.657593
[2]	train-logloss:0.640773	valid-logloss:0.640751
[3]	train-logloss:0.624561	valid-logloss:0.624531
[4]	train-logloss:0.608967	valid-logloss:0.60893
[5]	train-logloss:0.593988	valid-logloss:0.593943
[6]	train-logloss:0.57962	valid-logloss:0.579568
[7]	train-logloss:0.565859	valid-logloss:0.565799
[8]	train-logloss:0.552698	valid-logloss:0.552631
[9]	train-logloss:0.540132	valid-logloss:0.540057
[10]	train-logloss:0.528152	valid-logloss:0.528071
[11]	train-logloss:0.516753	valid-logloss:0.516664
[12]	train-logloss:0.505926	valid-logloss:0.50583
[13]	train-logloss:0.495662	valid-logloss:0.495558
[14]	train-logloss:0.485953	valid-logloss:0.485841
[15]	train-logloss:0.476788	valid-logloss:0.476669
[16]	train-logloss:0.468158	valid-logloss:0.468031
[17]	train-logloss:0.460052	valid-logloss:0.459918
[18]	train-logloss:0.45246	valid-logloss:0.452319
[19]	train-logloss:0.445371	valid-logloss:0.445222
[20]	train-logloss:0.438773	valid-logloss:0.438617
[21]	train-logloss:0.432655	valid-logloss:0.432492
[22]	train-logloss:0.427006	valid-logloss:0.426835
[23]	train-logloss:0.421813	valid-logloss:0.421635
[24]	train-logloss:0.417065	valid-logloss:0.416879
[25]	train-logloss:0.41275	valid-logloss:0.412557
[26]	train-logloss:0.408856	valid-logloss:0.408655
[27]	train-logloss:0.40537	valid-logloss:0.405162
[28]	train-logloss:0.402281	valid-logloss:0.402065
[29]	train-logloss:0.399577	valid-logloss:0.399354
[30]	train-logloss:0.397245	valid-logloss:0.397015
[31]	train-logloss:0.395275	valid-logloss:0.395037
[32]	train-logloss:0.393654	valid-logloss:0.393409
[33]	train-logloss:0.392371	valid-logloss:0.392119
[34]	train-logloss:0.391415	valid-logloss:0.391155
[35]	train-logloss:0.390774	valid-logloss:0.390506
[36]	train-logloss:0.390437	valid-logloss:0.390162
[37]	train-logloss:0.390393	valid-logloss:0.390111
[38]	train-logloss:0.390282	valid-logloss:0.389988
[39]	train-logloss:0.39038	valid-logloss:0.390084
[40]	train-logloss:0.390722	valid-logloss:0.390483
[41]	train-logloss:0.391236	valid-logloss:0.391005
[42]	train-logloss:0.391926	valid-logloss:0.391705
[43]	train-logloss:0.392797	valid-logloss:0.392586
[44]	train-logloss:0.393817	valid-logloss:0.393617
[45]	train-logloss:0.395043	valid-logloss:0.394842
[46]	train-logloss:0.396382	valid-logloss:0.396192
[47]	train-logloss:0.397805	valid-logloss:0.397628
[48]	train-logloss:0.399346	valid-logloss:0.39922
[49]	train-logloss:0.400957	valid-logloss:0.400851
[50]	train-logloss:0.402661	valid-logloss:0.402591
[51]	train-logloss:0.40449	valid-logloss:0.404444
[52]	train-logloss:0.406406	valid-logloss:0.406344
[53]	train-logloss:0.408357	valid-logloss:0.408278
[54]	train-logloss:0.41029	valid-logloss:0.410217
[55]	train-logloss:0.412349	valid-logloss:0.412342
[56]	train-logloss:0.414385	valid-logloss:0.414395
[57]	train-logloss:0.416484	valid-logloss:0.416506
[58]	train-logloss:0.418563	valid-logloss:0.418618
[59]	train-logloss:0.420673	valid-logloss:0.420769
[60]	train-logloss:0.422852	valid-logloss:0.423001
[61]	train-logloss:0.425093	valid-logloss:0.425279
[62]	train-logloss:0.427198	valid-logloss:0.427447
[63]	train-logloss:0.429377	valid-logloss:0.429665
[64]	train-logloss:0.431592	valid-logloss:0.431855
[65]	train-logloss:0.433676	valid-logloss:0.434
[66]	train-logloss:0.435789	valid-logloss:0.436164
[67]	train-logloss:0.437868	valid-logloss:0.438259
[68]	train-logloss:0.440033	valid-logloss:0.44049
[69]	train-logloss:0.44206	valid-logloss:0.442627
[70]	train-logloss:0.444083	valid-logloss:0.444719
[71]	train-logloss:0.446138	valid-logloss:0.446837
[72]	train-logloss:0.448039	valid-logloss:0.448755
[73]	train-logloss:0.450048	valid-logloss:0.450838
[74]	train-logloss:0.451986	valid-logloss:0.452795
[75]	train-logloss:0.453885	valid-logloss:0.454758
[76]	train-logloss:0.455696	valid-logloss:0.456647
[77]	train-logloss:0.457475	valid-logloss:0.458512
[78]	train-logloss:0.459289	valid-logloss:0.460395
[79]	train-logloss:0.46106	valid-logloss:0.462085
[80]	train-logloss:0.462772	valid-logloss:0.463808
[81]	train-logloss:0.464478	valid-logloss:0.465548
[82]	train-logloss:0.466487	valid-logloss:0.467558
[83]	train-logloss:0.468449	valid-logloss:0.469519
[84]	train-logloss:0.470233	valid-logloss:0.471304
[85]	train-logloss:0.4717	valid-logloss:0.472775
[86]	train-logloss:0.473134	valid-logloss:0.474156
[87]	train-logloss:0.474895	valid-logloss:0.475917
[88]	train-logloss:0.476139	valid-logloss:0.477237
Stopping. Best iteration:
[38]	train-logloss:0.390282	valid-logloss:0.389988

[mlcrate] Finished training fold 6 - took 4s - running score 0.3900315714285715
[mlcrate] Finished training 7 XGBoost models, took 30s

In [73]:
def make_submission(probs):
    sample = pd.read_csv(f'{PATH}\\AV_Stud_2\\sample_submission.csv')
    submit = sample.copy()
    submit['target'] = probs
    return submit

In [32]:
submit = make_submission(p_test)
submit.to_csv(f'{PATH}\\AV_Stud_2\\xgb_with_eid.csv', index=False)
submit.head(2)

# np.save(f'{PATH}\\AV_Stud_2\\xgb_oof_1207.npy', p_train)
# np.save(f'{PATH}\\AV_Stud_2\\raw_train_dummy_impact_train67.npy', X_train_stack)
# np.save(f'{PATH}\\AV_Stud_2\\raw_test_dummy_impact_test67.npy', X_test_stack)

# np.save(f'{PATH}\\AV_Stud_2\\train_no_cat_with_std_pca.npy', df_raw)
# np.save(f'{PATH}\\AV_Stud_2\\test_no_cat_with_std_pca.npy', df_test)


Out[32]:
enrollee_id target
0 16548 0.229773
1 12036 0.019617

In [272]:
df_raw.to_csv(f'{PATH}\\AV_Stud_2\\clean_train_1207.csv', index=False)
df_test.to_csv(f'{PATH}\\AV_Stud_2\\clean_test_1207.csv', index=False)

In [4]:
train = np.load(f'{PATH}\\AV_Stud_2\\train_dummy_impact_train67.npy')

In [5]:
np.savetxt(f'{PATH}\\AV_Stud_2\\train_dummy_impact_train67.csv',train, delimiter=',')

In [6]:
test = np.load(f'{PATH}\\AV_Stud_2\\test_dummy_impact_test67.npy')
np.savetxt(f'{PATH}\\AV_Stud_2\\test_dummy_impact_train67.csv',test, delimiter=',')

In [15]:
clf_et = ExtraTreesClassifier(criterion='entropy',max_leaf_nodes=0,n_estimators=500,\
                             min_impurity_split=0.0001,n_jobs=4,max_features=0.7,max_depth=8,min_samples_leaf=1,\
                             class_weight='balanced')

In [295]:
stack_test = pd.DataFrame()
stack_train = pd.DataFrame()
log_cols=["Classifier", "Accuracy"]
log = pd.DataFrame(columns=log_cols)

In [ ]:
classifiers = [
    GradientBoostingClassifier(max_depth=8,subsample=0.8,max_features='auto'),
    MLPClassifier(hidden_layer_sizes=(50,25),alpha=1,validation_fraction=0.2),
    ExtraTreesClassifier(100,max_depth=10,n_jobs=-1,class_weight='balanced_subsample',bootstrap=True,oob_score=True),
    DecisionTreeClassifier(min_samples_leaf= 3, class_weight ='balanced', max_features=.85, max_leaf_nodes=5, max_depth = 10),
    RandomForestClassifier(n_estimators=100,max_features=.85, max_leaf_nodes=3,n_jobs=-1,class_weight='balanced'),
    AdaBoostClassifier(),
    LogisticRegression(penalty='l2', C=1.2, fit_intercept=True, intercept_scaling=1, class_weight='balanced', solver='lbfgs', max_iter=500, multi_class='ovr', n_jobs=-1)
    ]

# Logging for Visual Comparison ( see above cell)

for clf in classifiers:    
    name = clf.__class__.__name__
    print("="*60, name)

    models, p_train, p_test, scores = train_k_fold(X_train_stack, target, clf, X_test_stack, 7, target)
    
    stack_test[name] = p_test
    stack_train[name] = p_train
    
    print("Accuracy: {:.4%}".format(np.mean(scores)))
    
    log_entry = pd.DataFrame([[name, np.mean(scores)*100]], columns=log_cols)
    log = log.append(log_entry)
    
    del models, p_train, p_test, scores
    print(gc.collect())

In [ ]:
sns.set_color_codes("muted")
sns.barplot(x='Accuracy', y='Classifier', data=log, color="b")
plt.xlabel('Accuracy %')
plt.title('Classifier Accuracy');

In [298]:
stack_train.to_csv(f'{PATH}\\AV_Stud_2\\stack_train_2.csv', index = False)
stack_test.to_csv(f'{PATH}\\AV_Stud_2\\stack_test_2.csv', index = False)
log.to_csv(f'{PATH}\\AV_Stud_2\\log.csv', index = False)

In [304]:
name = model_xgb[3].__class__.__name__    
stack_test[name] = p_test
stack_train[name] = p_train

In [306]:
_, _, p_test  = mlcrate.xgb.train_kfold(params, stack_train, target, stack_test\
                                                       , folds = 7,skip_checks = True, stratify=target, print_imp='final')


[mlcrate] Training 7 stratified XGBoost models on training set (18359, 8) with test set (15021, 8)
[mlcrate] Running fold 0, 15735 train samples, 2624 validation samples
[0]	train-auc:0.660126	valid-auc:0.669168
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:0.664186	valid-auc:0.688596
[2]	train-auc:0.664263	valid-auc:0.6899
[3]	train-auc:0.676224	valid-auc:0.692548
[4]	train-auc:0.677235	valid-auc:0.694662
[5]	train-auc:0.677262	valid-auc:0.694701
[6]	train-auc:0.686145	valid-auc:0.708831
[7]	train-auc:0.686155	valid-auc:0.708793
[8]	train-auc:0.686158	valid-auc:0.708742
[9]	train-auc:0.686152	valid-auc:0.70886
[10]	train-auc:0.686155	valid-auc:0.708861
[11]	train-auc:0.686155	valid-auc:0.708865
[12]	train-auc:0.686125	valid-auc:0.708509
[13]	train-auc:0.686121	valid-auc:0.708477
[14]	train-auc:0.686111	valid-auc:0.70901
[15]	train-auc:0.689476	valid-auc:0.709069
[16]	train-auc:0.690096	valid-auc:0.711619
[17]	train-auc:0.690198	valid-auc:0.711228
[18]	train-auc:0.69021	valid-auc:0.711217
[19]	train-auc:0.690206	valid-auc:0.711228
[20]	train-auc:0.690307	valid-auc:0.711213
[21]	train-auc:0.69035	valid-auc:0.710871
[22]	train-auc:0.690351	valid-auc:0.710874
[23]	train-auc:0.690351	valid-auc:0.710807
[24]	train-auc:0.690387	valid-auc:0.710771
[25]	train-auc:0.690706	valid-auc:0.709613
[26]	train-auc:0.691061	valid-auc:0.709444
[27]	train-auc:0.691102	valid-auc:0.709426
[28]	train-auc:0.691278	valid-auc:0.709101
[29]	train-auc:0.716753	valid-auc:0.70876
[30]	train-auc:0.717025	valid-auc:0.708705
[31]	train-auc:0.717137	valid-auc:0.708668
[32]	train-auc:0.717134	valid-auc:0.708678
[33]	train-auc:0.716953	valid-auc:0.708415
[34]	train-auc:0.717031	valid-auc:0.708536
[35]	train-auc:0.717151	valid-auc:0.70794
[36]	train-auc:0.717119	valid-auc:0.707928
[37]	train-auc:0.717918	valid-auc:0.707894
[38]	train-auc:0.718527	valid-auc:0.707438
[39]	train-auc:0.724032	valid-auc:0.708042
[40]	train-auc:0.724523	valid-auc:0.707009
[41]	train-auc:0.725136	valid-auc:0.707333
[42]	train-auc:0.725287	valid-auc:0.707342
[43]	train-auc:0.725484	valid-auc:0.703836
[44]	train-auc:0.725551	valid-auc:0.703589
[45]	train-auc:0.726966	valid-auc:0.702837
[46]	train-auc:0.72713	valid-auc:0.702661
[47]	train-auc:0.727482	valid-auc:0.702545
[48]	train-auc:0.727874	valid-auc:0.70189
[49]	train-auc:0.728271	valid-auc:0.702149
[50]	train-auc:0.728587	valid-auc:0.702204
[51]	train-auc:0.730097	valid-auc:0.701092
[52]	train-auc:0.731281	valid-auc:0.700929
[53]	train-auc:0.731914	valid-auc:0.700809
[54]	train-auc:0.732287	valid-auc:0.699812
[55]	train-auc:0.732451	valid-auc:0.69976
[56]	train-auc:0.733533	valid-auc:0.700658
[57]	train-auc:0.733745	valid-auc:0.70036
[58]	train-auc:0.735301	valid-auc:0.700317
[59]	train-auc:0.735457	valid-auc:0.700156
[60]	train-auc:0.735667	valid-auc:0.699652
[61]	train-auc:0.736004	valid-auc:0.699365
[62]	train-auc:0.737802	valid-auc:0.699561
[63]	train-auc:0.738683	valid-auc:0.698611
[64]	train-auc:0.738902	valid-auc:0.698592
[65]	train-auc:0.739226	valid-auc:0.698885
[66]	train-auc:0.739326	valid-auc:0.697292
Stopping. Best iteration:
[16]	train-auc:0.690096	valid-auc:0.711619

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 1s - running score 0.711619
[mlcrate] Running fold 1, 15735 train samples, 2624 validation samples
[0]	train-auc:0.646852	valid-auc:0.660876
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:0.665349	valid-auc:0.67368
[2]	train-auc:0.667164	valid-auc:0.67754
[3]	train-auc:0.667132	valid-auc:0.677222
[4]	train-auc:0.671255	valid-auc:0.683354
[5]	train-auc:0.671263	valid-auc:0.683036
[6]	train-auc:0.673693	valid-auc:0.688326
[7]	train-auc:0.673664	valid-auc:0.688291
[8]	train-auc:0.675425	valid-auc:0.68961
[9]	train-auc:0.675833	valid-auc:0.690361
[10]	train-auc:0.675836	valid-auc:0.690287
[11]	train-auc:0.675829	valid-auc:0.690363
[12]	train-auc:0.679024	valid-auc:0.696537
[13]	train-auc:0.679006	valid-auc:0.696674
[14]	train-auc:0.679008	valid-auc:0.696632
[15]	train-auc:0.678966	valid-auc:0.69666
[16]	train-auc:0.678994	valid-auc:0.696809
[17]	train-auc:0.678976	valid-auc:0.696807
[18]	train-auc:0.678965	valid-auc:0.696751
[19]	train-auc:0.678985	valid-auc:0.696705
[20]	train-auc:0.680238	valid-auc:0.697677
[21]	train-auc:0.685015	valid-auc:0.699329
[22]	train-auc:0.684998	valid-auc:0.699309
[23]	train-auc:0.684916	valid-auc:0.699469
[24]	train-auc:0.685082	valid-auc:0.699754
[25]	train-auc:0.685032	valid-auc:0.699776
[26]	train-auc:0.685279	valid-auc:0.699565
[27]	train-auc:0.685257	valid-auc:0.699571
[28]	train-auc:0.699842	valid-auc:0.720852
[29]	train-auc:0.70952	valid-auc:0.727897
[30]	train-auc:0.709559	valid-auc:0.727774
[31]	train-auc:0.710217	valid-auc:0.726391
[32]	train-auc:0.710721	valid-auc:0.725689
[33]	train-auc:0.710667	valid-auc:0.725676
[34]	train-auc:0.71073	valid-auc:0.72652
[35]	train-auc:0.711136	valid-auc:0.726318
[36]	train-auc:0.711402	valid-auc:0.726379
[37]	train-auc:0.711575	valid-auc:0.726303
[38]	train-auc:0.719211	valid-auc:0.736518
[39]	train-auc:0.719354	valid-auc:0.736682
[40]	train-auc:0.719665	valid-auc:0.736581
[41]	train-auc:0.720446	valid-auc:0.735926
[42]	train-auc:0.720387	valid-auc:0.736071
[43]	train-auc:0.721215	valid-auc:0.735685
[44]	train-auc:0.722045	valid-auc:0.735837
[45]	train-auc:0.723263	valid-auc:0.733994
[46]	train-auc:0.723559	valid-auc:0.73375
[47]	train-auc:0.724056	valid-auc:0.733914
[48]	train-auc:0.724797	valid-auc:0.734263
[49]	train-auc:0.725853	valid-auc:0.732951
[50]	train-auc:0.727227	valid-auc:0.734763
[51]	train-auc:0.727836	valid-auc:0.734767
[52]	train-auc:0.728368	valid-auc:0.735145
[53]	train-auc:0.728841	valid-auc:0.734879
[54]	train-auc:0.729706	valid-auc:0.733465
[55]	train-auc:0.730651	valid-auc:0.734105
[56]	train-auc:0.731344	valid-auc:0.733977
[57]	train-auc:0.731515	valid-auc:0.734417
[58]	train-auc:0.732455	valid-auc:0.73406
[59]	train-auc:0.734017	valid-auc:0.733521
[60]	train-auc:0.73459	valid-auc:0.73402
[61]	train-auc:0.734708	valid-auc:0.734104
[62]	train-auc:0.736179	valid-auc:0.733751
[63]	train-auc:0.736062	valid-auc:0.733568
[64]	train-auc:0.738136	valid-auc:0.735577
[65]	train-auc:0.738731	valid-auc:0.735718
[66]	train-auc:0.740126	valid-auc:0.735707
[67]	train-auc:0.741667	valid-auc:0.735821
[68]	train-auc:0.743033	valid-auc:0.735615
[69]	train-auc:0.743255	valid-auc:0.735277
[70]	train-auc:0.743899	valid-auc:0.735327
[71]	train-auc:0.743885	valid-auc:0.735988
[72]	train-auc:0.744974	valid-auc:0.73668
[73]	train-auc:0.745151	valid-auc:0.736688
[74]	train-auc:0.746361	valid-auc:0.736428
[75]	train-auc:0.746786	valid-auc:0.736699
[76]	train-auc:0.748568	valid-auc:0.735635
[77]	train-auc:0.749178	valid-auc:0.735686
[78]	train-auc:0.74947	valid-auc:0.735685
[79]	train-auc:0.750007	valid-auc:0.735309
[80]	train-auc:0.750671	valid-auc:0.735399
[81]	train-auc:0.75154	valid-auc:0.735839
[82]	train-auc:0.75283	valid-auc:0.735684
[83]	train-auc:0.753991	valid-auc:0.735478
[84]	train-auc:0.755453	valid-auc:0.735619
[85]	train-auc:0.756109	valid-auc:0.73572
[86]	train-auc:0.756694	valid-auc:0.735303
[87]	train-auc:0.757283	valid-auc:0.734608
[88]	train-auc:0.758215	valid-auc:0.734295
[89]	train-auc:0.759091	valid-auc:0.734594
[90]	train-auc:0.759633	valid-auc:0.735164
[91]	train-auc:0.760234	valid-auc:0.73513
[92]	train-auc:0.760956	valid-auc:0.735123
[93]	train-auc:0.761478	valid-auc:0.735261
[94]	train-auc:0.762233	valid-auc:0.735372
[95]	train-auc:0.7632	valid-auc:0.735468
[96]	train-auc:0.764533	valid-auc:0.734621
[97]	train-auc:0.765884	valid-auc:0.734263
[98]	train-auc:0.76678	valid-auc:0.733889
[99]	train-auc:0.767888	valid-auc:0.732954
[100]	train-auc:0.768359	valid-auc:0.733167
[101]	train-auc:0.768952	valid-auc:0.733517
[102]	train-auc:0.769687	valid-auc:0.733448
[103]	train-auc:0.770352	valid-auc:0.733908
[104]	train-auc:0.770642	valid-auc:0.733384
[105]	train-auc:0.771377	valid-auc:0.733145
[106]	train-auc:0.77204	valid-auc:0.733205
[107]	train-auc:0.772754	valid-auc:0.733062
[108]	train-auc:0.77355	valid-auc:0.732877
[109]	train-auc:0.77384	valid-auc:0.732647
[110]	train-auc:0.774459	valid-auc:0.732577
[111]	train-auc:0.775166	valid-auc:0.73276
[112]	train-auc:0.775718	valid-auc:0.732886
[113]	train-auc:0.776188	valid-auc:0.732514
[114]	train-auc:0.777122	valid-auc:0.732854
[115]	train-auc:0.777336	valid-auc:0.73248
[116]	train-auc:0.778215	valid-auc:0.732392
[117]	train-auc:0.778424	valid-auc:0.732374
[118]	train-auc:0.778705	valid-auc:0.732546
[119]	train-auc:0.779036	valid-auc:0.732403
[120]	train-auc:0.779308	valid-auc:0.732222
[121]	train-auc:0.779668	valid-auc:0.732427
[122]	train-auc:0.780489	valid-auc:0.732745
[123]	train-auc:0.781238	valid-auc:0.732697
[124]	train-auc:0.781434	valid-auc:0.733165
[125]	train-auc:0.781771	valid-auc:0.733044
Stopping. Best iteration:
[75]	train-auc:0.746786	valid-auc:0.736699

[mlcrate] Finished training fold 1 - took 3s - running score 0.724159
[mlcrate] Running fold 2, 15736 train samples, 2623 validation samples
[0]	train-auc:0.631006	valid-auc:0.639916
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:0.632765	valid-auc:0.641089
[2]	train-auc:0.632765	valid-auc:0.641089
[3]	train-auc:0.6652	valid-auc:0.668216
[4]	train-auc:0.672536	valid-auc:0.685736
[5]	train-auc:0.672398	valid-auc:0.685668
[6]	train-auc:0.672378	valid-auc:0.685673
[7]	train-auc:0.67237	valid-auc:0.685681
[8]	train-auc:0.672549	valid-auc:0.685308
[9]	train-auc:0.67322	valid-auc:0.687597
[10]	train-auc:0.673227	valid-auc:0.687588
[11]	train-auc:0.673368	valid-auc:0.687571
[12]	train-auc:0.67338	valid-auc:0.687577
[13]	train-auc:0.675798	valid-auc:0.692425
[14]	train-auc:0.675799	valid-auc:0.69242
[15]	train-auc:0.676022	valid-auc:0.692221
[16]	train-auc:0.67685	valid-auc:0.695112
[17]	train-auc:0.676843	valid-auc:0.695182
[18]	train-auc:0.676826	valid-auc:0.695398
[19]	train-auc:0.676825	valid-auc:0.695402
[20]	train-auc:0.676954	valid-auc:0.69559
[21]	train-auc:0.677028	valid-auc:0.695961
[22]	train-auc:0.677117	valid-auc:0.695925
[23]	train-auc:0.677111	valid-auc:0.695693
[24]	train-auc:0.702329	valid-auc:0.723785
[25]	train-auc:0.702498	valid-auc:0.723785
[26]	train-auc:0.702514	valid-auc:0.723782
[27]	train-auc:0.707861	valid-auc:0.732119
[28]	train-auc:0.708613	valid-auc:0.732491
[29]	train-auc:0.717121	valid-auc:0.73516
[30]	train-auc:0.717348	valid-auc:0.734651
[31]	train-auc:0.717107	valid-auc:0.733977
[32]	train-auc:0.717962	valid-auc:0.735868
[33]	train-auc:0.71854	valid-auc:0.736602
[34]	train-auc:0.718589	valid-auc:0.737034
[35]	train-auc:0.718951	valid-auc:0.737217
[36]	train-auc:0.719559	valid-auc:0.737167
[37]	train-auc:0.720118	valid-auc:0.736546
[38]	train-auc:0.720217	valid-auc:0.736832
[39]	train-auc:0.72074	valid-auc:0.737179
[40]	train-auc:0.720846	valid-auc:0.73667
[41]	train-auc:0.721178	valid-auc:0.736824
[42]	train-auc:0.722138	valid-auc:0.736275
[43]	train-auc:0.722722	valid-auc:0.735788
[44]	train-auc:0.723047	valid-auc:0.736367
[45]	train-auc:0.723519	valid-auc:0.736096
[46]	train-auc:0.723485	valid-auc:0.736062
[47]	train-auc:0.724296	valid-auc:0.736119
[48]	train-auc:0.724598	valid-auc:0.736163
[49]	train-auc:0.724659	valid-auc:0.736149
[50]	train-auc:0.725421	valid-auc:0.73641
[51]	train-auc:0.725761	valid-auc:0.736586
[52]	train-auc:0.72649	valid-auc:0.736361
[53]	train-auc:0.726877	valid-auc:0.736435
[54]	train-auc:0.727484	valid-auc:0.736563
[55]	train-auc:0.728965	valid-auc:0.7358
[56]	train-auc:0.729948	valid-auc:0.735793
[57]	train-auc:0.730296	valid-auc:0.735659
[58]	train-auc:0.730847	valid-auc:0.736251
[59]	train-auc:0.731944	valid-auc:0.736013
[60]	train-auc:0.732331	valid-auc:0.736665
[61]	train-auc:0.732619	valid-auc:0.737426
[62]	train-auc:0.733172	valid-auc:0.737403
[63]	train-auc:0.733472	valid-auc:0.737173
[64]	train-auc:0.735859	valid-auc:0.736251
[65]	train-auc:0.738535	valid-auc:0.73591
[66]	train-auc:0.738844	valid-auc:0.735817
[67]	train-auc:0.739655	valid-auc:0.734659
[68]	train-auc:0.741083	valid-auc:0.734126
[69]	train-auc:0.741905	valid-auc:0.73483
[70]	train-auc:0.743914	valid-auc:0.734289
[71]	train-auc:0.745829	valid-auc:0.734342
[72]	train-auc:0.745935	valid-auc:0.73509
[73]	train-auc:0.746322	valid-auc:0.735338
[74]	train-auc:0.747548	valid-auc:0.735495
[75]	train-auc:0.749448	valid-auc:0.735313
[76]	train-auc:0.749928	valid-auc:0.73445
[77]	train-auc:0.752043	valid-auc:0.732764
[78]	train-auc:0.752958	valid-auc:0.732773
[79]	train-auc:0.753031	valid-auc:0.732926
[80]	train-auc:0.753964	valid-auc:0.732002
[81]	train-auc:0.753959	valid-auc:0.73229
[82]	train-auc:0.755037	valid-auc:0.733062
[83]	train-auc:0.755803	valid-auc:0.732679
[84]	train-auc:0.756406	valid-auc:0.73222
[85]	train-auc:0.758152	valid-auc:0.732636
[86]	train-auc:0.758508	valid-auc:0.731921
[87]	train-auc:0.759625	valid-auc:0.731015
[88]	train-auc:0.760071	valid-auc:0.730752
[89]	train-auc:0.760165	valid-auc:0.7308
[90]	train-auc:0.76085	valid-auc:0.730465
[91]	train-auc:0.761492	valid-auc:0.72983
[92]	train-auc:0.76235	valid-auc:0.730684
[93]	train-auc:0.763265	valid-auc:0.729964
[94]	train-auc:0.764045	valid-auc:0.729976
[95]	train-auc:0.76507	valid-auc:0.730032
[96]	train-auc:0.765526	valid-auc:0.729587
[97]	train-auc:0.765873	valid-auc:0.729348
[98]	train-auc:0.766768	valid-auc:0.728876
[99]	train-auc:0.767336	valid-auc:0.728774
[100]	train-auc:0.768011	valid-auc:0.7286
[101]	train-auc:0.768606	valid-auc:0.729111
[102]	train-auc:0.769219	valid-auc:0.729511
[103]	train-auc:0.76964	valid-auc:0.729493
[104]	train-auc:0.770109	valid-auc:0.730108
[105]	train-auc:0.770509	valid-auc:0.729881
[106]	train-auc:0.771508	valid-auc:0.729586
[107]	train-auc:0.772095	valid-auc:0.72933
[108]	train-auc:0.773305	valid-auc:0.729721
[109]	train-auc:0.773881	valid-auc:0.729488
[110]	train-auc:0.774057	valid-auc:0.729582
[111]	train-auc:0.77442	valid-auc:0.729994
Stopping. Best iteration:
[61]	train-auc:0.732619	valid-auc:0.737426

[mlcrate] Finished training fold 2 - took 3s - running score 0.7285813333333334
[mlcrate] Running fold 3, 15737 train samples, 2622 validation samples
[0]	train-auc:0.652459	valid-auc:0.621593
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:0.654276	valid-auc:0.622378
[2]	train-auc:0.669193	valid-auc:0.633067
[3]	train-auc:0.669732	valid-auc:0.633976
[4]	train-auc:0.669822	valid-auc:0.634108
[5]	train-auc:0.670768	valid-auc:0.63427
[6]	train-auc:0.682164	valid-auc:0.640194
[7]	train-auc:0.682445	valid-auc:0.640518
[8]	train-auc:0.682551	valid-auc:0.640576
[9]	train-auc:0.682695	valid-auc:0.640566
[10]	train-auc:0.683141	valid-auc:0.640572
[11]	train-auc:0.683137	valid-auc:0.640577
[12]	train-auc:0.683106	valid-auc:0.640457
[13]	train-auc:0.68312	valid-auc:0.640499
[14]	train-auc:0.692081	valid-auc:0.642593
[15]	train-auc:0.692126	valid-auc:0.642573
[16]	train-auc:0.693091	valid-auc:0.642359
[17]	train-auc:0.693096	valid-auc:0.64237
[18]	train-auc:0.693382	valid-auc:0.64211
[19]	train-auc:0.693619	valid-auc:0.642022
[20]	train-auc:0.710077	valid-auc:0.669687
[21]	train-auc:0.710163	valid-auc:0.669686
[22]	train-auc:0.710073	valid-auc:0.669701
[23]	train-auc:0.710652	valid-auc:0.670024
[24]	train-auc:0.710574	valid-auc:0.670116
[25]	train-auc:0.710608	valid-auc:0.669957
[26]	train-auc:0.710583	valid-auc:0.669948
[27]	train-auc:0.710547	valid-auc:0.669984
[28]	train-auc:0.711101	valid-auc:0.66992
[29]	train-auc:0.723378	valid-auc:0.691274
[30]	train-auc:0.723326	valid-auc:0.691368
[31]	train-auc:0.723686	valid-auc:0.691093
[32]	train-auc:0.724072	valid-auc:0.691113
[33]	train-auc:0.724502	valid-auc:0.696587
[34]	train-auc:0.725332	valid-auc:0.695393
[35]	train-auc:0.726881	valid-auc:0.698918
[36]	train-auc:0.727137	valid-auc:0.699453
[37]	train-auc:0.72708	valid-auc:0.698881
[38]	train-auc:0.727374	valid-auc:0.699248
[39]	train-auc:0.728736	valid-auc:0.700019
[40]	train-auc:0.728957	valid-auc:0.700186
[41]	train-auc:0.729378	valid-auc:0.700569
[42]	train-auc:0.729559	valid-auc:0.701365
[43]	train-auc:0.730607	valid-auc:0.700802
[44]	train-auc:0.730175	valid-auc:0.7014
[45]	train-auc:0.730422	valid-auc:0.701379
[46]	train-auc:0.730594	valid-auc:0.701659
[47]	train-auc:0.730551	valid-auc:0.702388
[48]	train-auc:0.730846	valid-auc:0.702063
[49]	train-auc:0.731115	valid-auc:0.702317
[50]	train-auc:0.731408	valid-auc:0.702304
[51]	train-auc:0.731833	valid-auc:0.702489
[52]	train-auc:0.732549	valid-auc:0.702207
[53]	train-auc:0.733199	valid-auc:0.702195
[54]	train-auc:0.733442	valid-auc:0.702176
[55]	train-auc:0.734152	valid-auc:0.702162
[56]	train-auc:0.735583	valid-auc:0.70003
[57]	train-auc:0.736248	valid-auc:0.699536
[58]	train-auc:0.736769	valid-auc:0.700029
[59]	train-auc:0.738795	valid-auc:0.697998
[60]	train-auc:0.739205	valid-auc:0.698076
[61]	train-auc:0.739419	valid-auc:0.697644
[62]	train-auc:0.739757	valid-auc:0.697838
[63]	train-auc:0.740418	valid-auc:0.698824
[64]	train-auc:0.741394	valid-auc:0.697998
[65]	train-auc:0.742606	valid-auc:0.698179
[66]	train-auc:0.742687	valid-auc:0.698276
[67]	train-auc:0.743186	valid-auc:0.697093
[68]	train-auc:0.743728	valid-auc:0.697209
[69]	train-auc:0.744831	valid-auc:0.69679
[70]	train-auc:0.745898	valid-auc:0.696758
[71]	train-auc:0.748176	valid-auc:0.697555
[72]	train-auc:0.748633	valid-auc:0.697101
[73]	train-auc:0.749306	valid-auc:0.697696
[74]	train-auc:0.75052	valid-auc:0.697608
[75]	train-auc:0.750514	valid-auc:0.697619
[76]	train-auc:0.752671	valid-auc:0.698845
[77]	train-auc:0.752796	valid-auc:0.698572
[78]	train-auc:0.752983	valid-auc:0.698435
[79]	train-auc:0.753426	valid-auc:0.698009
[80]	train-auc:0.755052	valid-auc:0.696707
[81]	train-auc:0.755456	valid-auc:0.696223
[82]	train-auc:0.756117	valid-auc:0.696402
[83]	train-auc:0.757151	valid-auc:0.697011
[84]	train-auc:0.7581	valid-auc:0.696852
[85]	train-auc:0.759764	valid-auc:0.698172
[86]	train-auc:0.760547	valid-auc:0.699158
[87]	train-auc:0.761475	valid-auc:0.699546
[88]	train-auc:0.763412	valid-auc:0.700049
[89]	train-auc:0.764303	valid-auc:0.70049
[90]	train-auc:0.765272	valid-auc:0.700618
[91]	train-auc:0.766182	valid-auc:0.701613
[92]	train-auc:0.766719	valid-auc:0.7011
[93]	train-auc:0.767527	valid-auc:0.701277
[94]	train-auc:0.768458	valid-auc:0.701791
[95]	train-auc:0.768378	valid-auc:0.701673
[96]	train-auc:0.769049	valid-auc:0.701207
[97]	train-auc:0.769548	valid-auc:0.701513
[98]	train-auc:0.770622	valid-auc:0.701492
[99]	train-auc:0.771419	valid-auc:0.701793
[100]	train-auc:0.77201	valid-auc:0.70206
[101]	train-auc:0.773221	valid-auc:0.701764
Stopping. Best iteration:
[51]	train-auc:0.731833	valid-auc:0.702489

[mlcrate] Finished training fold 3 - took 2s - running score 0.72205825
[mlcrate] Running fold 4, 15737 train samples, 2622 validation samples
[0]	train-auc:0.639458	valid-auc:0.614249
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:0.663714	valid-auc:0.648619
[2]	train-auc:0.669056	valid-auc:0.65424
[3]	train-auc:0.672366	valid-auc:0.655633
[4]	train-auc:0.680115	valid-auc:0.656745
[5]	train-auc:0.68014	valid-auc:0.656706
[6]	train-auc:0.682217	valid-auc:0.65594
[7]	train-auc:0.682185	valid-auc:0.655674
[8]	train-auc:0.682212	valid-auc:0.655897
[9]	train-auc:0.682172	valid-auc:0.655862
[10]	train-auc:0.68213	valid-auc:0.655829
[11]	train-auc:0.682068	valid-auc:0.655707
[12]	train-auc:0.682071	valid-auc:0.655707
[13]	train-auc:0.682098	valid-auc:0.655674
[14]	train-auc:0.683092	valid-auc:0.655002
[15]	train-auc:0.683277	valid-auc:0.65483
[16]	train-auc:0.683194	valid-auc:0.654784
[17]	train-auc:0.683194	valid-auc:0.654749
[18]	train-auc:0.684265	valid-auc:0.658142
[19]	train-auc:0.685671	valid-auc:0.661257
[20]	train-auc:0.685678	valid-auc:0.661351
[21]	train-auc:0.685752	valid-auc:0.66152
[22]	train-auc:0.685746	valid-auc:0.661513
[23]	train-auc:0.685753	valid-auc:0.661489
[24]	train-auc:0.690137	valid-auc:0.661911
[25]	train-auc:0.690129	valid-auc:0.66192
[26]	train-auc:0.690093	valid-auc:0.662217
[27]	train-auc:0.711458	valid-auc:0.679751
[28]	train-auc:0.711486	valid-auc:0.680175
[29]	train-auc:0.717085	valid-auc:0.682422
[30]	train-auc:0.717109	valid-auc:0.682613
[31]	train-auc:0.717839	valid-auc:0.681492
[32]	train-auc:0.72512	valid-auc:0.690481
[33]	train-auc:0.725651	valid-auc:0.690503
[34]	train-auc:0.727791	valid-auc:0.691364
[35]	train-auc:0.727849	valid-auc:0.691447
[36]	train-auc:0.729367	valid-auc:0.691211
[37]	train-auc:0.729356	valid-auc:0.690967
[38]	train-auc:0.729829	valid-auc:0.691138
[39]	train-auc:0.730109	valid-auc:0.69197
[40]	train-auc:0.730573	valid-auc:0.69163
[41]	train-auc:0.730628	valid-auc:0.691664
[42]	train-auc:0.730833	valid-auc:0.69147
[43]	train-auc:0.731969	valid-auc:0.691416
[44]	train-auc:0.732722	valid-auc:0.692448
[45]	train-auc:0.732799	valid-auc:0.693087
[46]	train-auc:0.73292	valid-auc:0.693016
[47]	train-auc:0.733632	valid-auc:0.692989
[48]	train-auc:0.733871	valid-auc:0.692751
[49]	train-auc:0.734898	valid-auc:0.693362
[50]	train-auc:0.736195	valid-auc:0.692836
[51]	train-auc:0.73723	valid-auc:0.692676
[52]	train-auc:0.737898	valid-auc:0.692906
[53]	train-auc:0.737953	valid-auc:0.693021
[54]	train-auc:0.738774	valid-auc:0.692272
[55]	train-auc:0.739071	valid-auc:0.6927
[56]	train-auc:0.74002	valid-auc:0.69177
[57]	train-auc:0.741393	valid-auc:0.691833
[58]	train-auc:0.741842	valid-auc:0.692088
[59]	train-auc:0.742045	valid-auc:0.691753
[60]	train-auc:0.742336	valid-auc:0.691832
[61]	train-auc:0.742992	valid-auc:0.691987
[62]	train-auc:0.743488	valid-auc:0.692442
[63]	train-auc:0.743597	valid-auc:0.692254
[64]	train-auc:0.744247	valid-auc:0.692974
[65]	train-auc:0.744795	valid-auc:0.692451
[66]	train-auc:0.747324	valid-auc:0.692376
[67]	train-auc:0.748735	valid-auc:0.692995
[68]	train-auc:0.749739	valid-auc:0.692078
[69]	train-auc:0.750301	valid-auc:0.691385
[70]	train-auc:0.750778	valid-auc:0.691611
[71]	train-auc:0.75119	valid-auc:0.69152
[72]	train-auc:0.75113	valid-auc:0.691704
[73]	train-auc:0.751451	valid-auc:0.691563
[74]	train-auc:0.753387	valid-auc:0.692321
[75]	train-auc:0.753793	valid-auc:0.692566
[76]	train-auc:0.754577	valid-auc:0.693139
[77]	train-auc:0.755278	valid-auc:0.693491
[78]	train-auc:0.756572	valid-auc:0.692418
[79]	train-auc:0.757623	valid-auc:0.692941
[80]	train-auc:0.758483	valid-auc:0.69321
[81]	train-auc:0.758871	valid-auc:0.692484
[82]	train-auc:0.759543	valid-auc:0.691821
[83]	train-auc:0.759928	valid-auc:0.691334
[84]	train-auc:0.760706	valid-auc:0.690044
[85]	train-auc:0.761377	valid-auc:0.688734
[86]	train-auc:0.762196	valid-auc:0.688788
[87]	train-auc:0.763152	valid-auc:0.687744
[88]	train-auc:0.764308	valid-auc:0.687868
[89]	train-auc:0.764811	valid-auc:0.687603
[90]	train-auc:0.765384	valid-auc:0.687612
[91]	train-auc:0.766055	valid-auc:0.687693
[92]	train-auc:0.767083	valid-auc:0.688559
[93]	train-auc:0.768236	valid-auc:0.688966
[94]	train-auc:0.769114	valid-auc:0.687812
[95]	train-auc:0.769689	valid-auc:0.68733
[96]	train-auc:0.771628	valid-auc:0.687671
[97]	train-auc:0.772392	valid-auc:0.687729
[98]	train-auc:0.773712	valid-auc:0.687634
[99]	train-auc:0.774165	valid-auc:0.68758
[100]	train-auc:0.774664	valid-auc:0.688205
[101]	train-auc:0.775342	valid-auc:0.688035
[102]	train-auc:0.776184	valid-auc:0.687377
[103]	train-auc:0.776529	valid-auc:0.687681
[104]	train-auc:0.777196	valid-auc:0.687874
[105]	train-auc:0.77789	valid-auc:0.687481
[106]	train-auc:0.77895	valid-auc:0.688293
[107]	train-auc:0.780135	valid-auc:0.688719
[108]	train-auc:0.780856	valid-auc:0.688887
[109]	train-auc:0.781577	valid-auc:0.688338
[110]	train-auc:0.782665	valid-auc:0.687619
[111]	train-auc:0.783306	valid-auc:0.687564
[112]	train-auc:0.783658	valid-auc:0.686937
[113]	train-auc:0.783992	valid-auc:0.687259
[114]	train-auc:0.78486	valid-auc:0.687
[115]	train-auc:0.785562	valid-auc:0.687251
[116]	train-auc:0.785639	valid-auc:0.68731
[117]	train-auc:0.785934	valid-auc:0.687147
[118]	train-auc:0.786432	valid-auc:0.687129
[119]	train-auc:0.787358	valid-auc:0.686746
[120]	train-auc:0.787567	valid-auc:0.686678
[121]	train-auc:0.788001	valid-auc:0.686831
[122]	train-auc:0.789033	valid-auc:0.686598
[123]	train-auc:0.789593	valid-auc:0.687057
[124]	train-auc:0.78979	valid-auc:0.68653
[125]	train-auc:0.790264	valid-auc:0.686605
[126]	train-auc:0.7905	valid-auc:0.686934
[127]	train-auc:0.791502	valid-auc:0.687108
Stopping. Best iteration:
[77]	train-auc:0.755278	valid-auc:0.693491

[mlcrate] Finished training fold 4 - took 3s - running score 0.7163448
[mlcrate] Running fold 5, 15737 train samples, 2622 validation samples
[0]	train-auc:0.647615	valid-auc:0.655158
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:0.661352	valid-auc:0.666683
[2]	train-auc:0.675545	valid-auc:0.671906
[3]	train-auc:0.676901	valid-auc:0.67432
[4]	train-auc:0.680698	valid-auc:0.674233
[5]	train-auc:0.68078	valid-auc:0.674655
[6]	train-auc:0.680777	valid-auc:0.674541
[7]	train-auc:0.680726	valid-auc:0.674081
[8]	train-auc:0.680702	valid-auc:0.673988
[9]	train-auc:0.681012	valid-auc:0.67496
[10]	train-auc:0.680999	valid-auc:0.674868
[11]	train-auc:0.681955	valid-auc:0.674888
[12]	train-auc:0.681934	valid-auc:0.674844
[13]	train-auc:0.681925	valid-auc:0.674936
[14]	train-auc:0.681928	valid-auc:0.675016
[15]	train-auc:0.682694	valid-auc:0.67479
[16]	train-auc:0.682779	valid-auc:0.674924
[17]	train-auc:0.682778	valid-auc:0.674932
[18]	train-auc:0.683518	valid-auc:0.674429
[19]	train-auc:0.683902	valid-auc:0.674593
[20]	train-auc:0.683908	valid-auc:0.674604
[21]	train-auc:0.683918	valid-auc:0.674578
[22]	train-auc:0.684053	valid-auc:0.674572
[23]	train-auc:0.683989	valid-auc:0.674531
[24]	train-auc:0.687417	valid-auc:0.677078
[25]	train-auc:0.687429	valid-auc:0.677061
[26]	train-auc:0.713505	valid-auc:0.700605
[27]	train-auc:0.713442	valid-auc:0.700654
[28]	train-auc:0.713449	valid-auc:0.700705
[29]	train-auc:0.721414	valid-auc:0.702705
[30]	train-auc:0.721622	valid-auc:0.703567
[31]	train-auc:0.722641	valid-auc:0.703588
[32]	train-auc:0.722493	valid-auc:0.703153
[33]	train-auc:0.72298	valid-auc:0.703643
[34]	train-auc:0.723137	valid-auc:0.7032
[35]	train-auc:0.723558	valid-auc:0.702669
[36]	train-auc:0.723599	valid-auc:0.702639
[37]	train-auc:0.723555	valid-auc:0.702617
[38]	train-auc:0.723599	valid-auc:0.70255
[39]	train-auc:0.7238	valid-auc:0.702928
[40]	train-auc:0.724176	valid-auc:0.702354
[41]	train-auc:0.724368	valid-auc:0.702223
[42]	train-auc:0.725206	valid-auc:0.704012
[43]	train-auc:0.72534	valid-auc:0.70418
[44]	train-auc:0.725259	valid-auc:0.704303
[45]	train-auc:0.725832	valid-auc:0.704665
[46]	train-auc:0.726656	valid-auc:0.703831
[47]	train-auc:0.727707	valid-auc:0.703842
[48]	train-auc:0.729211	valid-auc:0.703155
[49]	train-auc:0.729421	valid-auc:0.70289
[50]	train-auc:0.729598	valid-auc:0.703
[51]	train-auc:0.730445	valid-auc:0.703278
[52]	train-auc:0.73178	valid-auc:0.703499
[53]	train-auc:0.731881	valid-auc:0.704373
[54]	train-auc:0.732065	valid-auc:0.704195
[55]	train-auc:0.732429	valid-auc:0.704303
[56]	train-auc:0.732861	valid-auc:0.703849
[57]	train-auc:0.734439	valid-auc:0.703515
[58]	train-auc:0.736065	valid-auc:0.703894
[59]	train-auc:0.736619	valid-auc:0.70403
[60]	train-auc:0.737528	valid-auc:0.704295
[61]	train-auc:0.738064	valid-auc:0.703964
[62]	train-auc:0.73833	valid-auc:0.703715
[63]	train-auc:0.739045	valid-auc:0.704351
[64]	train-auc:0.740375	valid-auc:0.704846
[65]	train-auc:0.741153	valid-auc:0.704758
[66]	train-auc:0.741887	valid-auc:0.705037
[67]	train-auc:0.742741	valid-auc:0.705152
[68]	train-auc:0.743567	valid-auc:0.705198
[69]	train-auc:0.745218	valid-auc:0.705028
[70]	train-auc:0.746125	valid-auc:0.705299
[71]	train-auc:0.746661	valid-auc:0.705775
[72]	train-auc:0.747022	valid-auc:0.706002
[73]	train-auc:0.747723	valid-auc:0.706799
[74]	train-auc:0.74809	valid-auc:0.707078
[75]	train-auc:0.748821	valid-auc:0.707409
[76]	train-auc:0.749608	valid-auc:0.706884
[77]	train-auc:0.750254	valid-auc:0.706877
[78]	train-auc:0.752295	valid-auc:0.706971
[79]	train-auc:0.752873	valid-auc:0.70827
[80]	train-auc:0.754112	valid-auc:0.708586
[81]	train-auc:0.755275	valid-auc:0.708229
[82]	train-auc:0.756296	valid-auc:0.708246
[83]	train-auc:0.757794	valid-auc:0.707941
[84]	train-auc:0.759026	valid-auc:0.707941
[85]	train-auc:0.760528	valid-auc:0.707758
[86]	train-auc:0.761503	valid-auc:0.707777
[87]	train-auc:0.762446	valid-auc:0.707834
[88]	train-auc:0.763358	valid-auc:0.70748
[89]	train-auc:0.763617	valid-auc:0.707118
[90]	train-auc:0.764845	valid-auc:0.707183
[91]	train-auc:0.765389	valid-auc:0.707187
[92]	train-auc:0.766412	valid-auc:0.707704
[93]	train-auc:0.767334	valid-auc:0.706801
[94]	train-auc:0.768886	valid-auc:0.706879
[95]	train-auc:0.770038	valid-auc:0.706801
[96]	train-auc:0.771068	valid-auc:0.706407
[97]	train-auc:0.771194	valid-auc:0.706334
[98]	train-auc:0.7715	valid-auc:0.706197
[99]	train-auc:0.772468	valid-auc:0.706186
[100]	train-auc:0.773622	valid-auc:0.705334
[101]	train-auc:0.774446	valid-auc:0.705006
[102]	train-auc:0.775293	valid-auc:0.705386
[103]	train-auc:0.775542	valid-auc:0.705218
[104]	train-auc:0.776049	valid-auc:0.705279
[105]	train-auc:0.776766	valid-auc:0.704779
[106]	train-auc:0.777785	valid-auc:0.704317
[107]	train-auc:0.778492	valid-auc:0.703544
[108]	train-auc:0.779705	valid-auc:0.703035
[109]	train-auc:0.780116	valid-auc:0.702619
[110]	train-auc:0.780403	valid-auc:0.702804
[111]	train-auc:0.780908	valid-auc:0.702939
[112]	train-auc:0.781482	valid-auc:0.702682
[113]	train-auc:0.782321	valid-auc:0.701925
[114]	train-auc:0.782352	valid-auc:0.702076
[115]	train-auc:0.782833	valid-auc:0.702179
[116]	train-auc:0.783643	valid-auc:0.70145
[117]	train-auc:0.784387	valid-auc:0.701282
[118]	train-auc:0.785102	valid-auc:0.702144
[119]	train-auc:0.785496	valid-auc:0.701625
[120]	train-auc:0.786214	valid-auc:0.700709
[121]	train-auc:0.78683	valid-auc:0.700713
[122]	train-auc:0.787042	valid-auc:0.700652
[123]	train-auc:0.787286	valid-auc:0.70052
[124]	train-auc:0.78842	valid-auc:0.700248
[125]	train-auc:0.788787	valid-auc:0.700191
[126]	train-auc:0.789203	valid-auc:0.700023
[127]	train-auc:0.789767	valid-auc:0.700064
[128]	train-auc:0.789862	valid-auc:0.700067
[129]	train-auc:0.790193	valid-auc:0.699717
[130]	train-auc:0.79021	valid-auc:0.699604
Stopping. Best iteration:
[80]	train-auc:0.754112	valid-auc:0.708586

[mlcrate] Finished training fold 5 - took 3s - running score 0.7150516666666666
[mlcrate] Running fold 6, 15737 train samples, 2622 validation samples
[0]	train-auc:0.648749	valid-auc:0.655459
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:0.649304	valid-auc:0.656058
[2]	train-auc:0.662293	valid-auc:0.675483
[3]	train-auc:0.671687	valid-auc:0.68316
[4]	train-auc:0.671688	valid-auc:0.683855
[5]	train-auc:0.671673	valid-auc:0.683867
[6]	train-auc:0.673504	valid-auc:0.685194
[7]	train-auc:0.67387	valid-auc:0.685386
[8]	train-auc:0.674919	valid-auc:0.686057
[9]	train-auc:0.677669	valid-auc:0.686233
[10]	train-auc:0.677669	valid-auc:0.686308
[11]	train-auc:0.677718	valid-auc:0.686356
[12]	train-auc:0.677806	valid-auc:0.686245
[13]	train-auc:0.677811	valid-auc:0.686245
[14]	train-auc:0.678378	valid-auc:0.687358
[15]	train-auc:0.679232	valid-auc:0.689088
[16]	train-auc:0.679347	valid-auc:0.688783
[17]	train-auc:0.67934	valid-auc:0.688776
[18]	train-auc:0.679355	valid-auc:0.688491
[19]	train-auc:0.703298	valid-auc:0.696572
[20]	train-auc:0.703311	valid-auc:0.696566
[21]	train-auc:0.70336	valid-auc:0.69673
[22]	train-auc:0.703741	valid-auc:0.696193
[23]	train-auc:0.703949	valid-auc:0.696149
[24]	train-auc:0.704811	valid-auc:0.697803
[25]	train-auc:0.712637	valid-auc:0.710334
[26]	train-auc:0.712788	valid-auc:0.710204
[27]	train-auc:0.712797	valid-auc:0.7102
[28]	train-auc:0.712775	valid-auc:0.708049
[29]	train-auc:0.713266	valid-auc:0.707539
[30]	train-auc:0.713283	valid-auc:0.707376
[31]	train-auc:0.713649	valid-auc:0.707609
[32]	train-auc:0.713891	valid-auc:0.707459
[33]	train-auc:0.716481	valid-auc:0.707113
[34]	train-auc:0.716467	valid-auc:0.707277
[35]	train-auc:0.716826	valid-auc:0.708281
[36]	train-auc:0.722427	valid-auc:0.721916
[37]	train-auc:0.722505	valid-auc:0.721717
[38]	train-auc:0.722748	valid-auc:0.721512
[39]	train-auc:0.723391	valid-auc:0.720493
[40]	train-auc:0.723591	valid-auc:0.72011
[41]	train-auc:0.724111	valid-auc:0.720495
[42]	train-auc:0.725901	valid-auc:0.719985
[43]	train-auc:0.725929	valid-auc:0.718907
[44]	train-auc:0.72635	valid-auc:0.71931
[45]	train-auc:0.726956	valid-auc:0.720078
[46]	train-auc:0.726992	valid-auc:0.720506
[47]	train-auc:0.727428	valid-auc:0.720489
[48]	train-auc:0.727402	valid-auc:0.720491
[49]	train-auc:0.727402	valid-auc:0.720428
[50]	train-auc:0.728177	valid-auc:0.719905
[51]	train-auc:0.728605	valid-auc:0.719602
[52]	train-auc:0.729156	valid-auc:0.720291
[53]	train-auc:0.729759	valid-auc:0.721091
[54]	train-auc:0.730442	valid-auc:0.721835
[55]	train-auc:0.730822	valid-auc:0.721198
[56]	train-auc:0.732201	valid-auc:0.721933
[57]	train-auc:0.733097	valid-auc:0.721781
[58]	train-auc:0.73354	valid-auc:0.721563
[59]	train-auc:0.734793	valid-auc:0.720343
[60]	train-auc:0.735595	valid-auc:0.721579
[61]	train-auc:0.736202	valid-auc:0.722028
[62]	train-auc:0.736571	valid-auc:0.722317
[63]	train-auc:0.737944	valid-auc:0.722491
[64]	train-auc:0.73938	valid-auc:0.722232
[65]	train-auc:0.740004	valid-auc:0.721861
[66]	train-auc:0.741314	valid-auc:0.722505
[67]	train-auc:0.742443	valid-auc:0.72388
[68]	train-auc:0.744065	valid-auc:0.724713
[69]	train-auc:0.744917	valid-auc:0.725061
[70]	train-auc:0.745623	valid-auc:0.724781
[71]	train-auc:0.746131	valid-auc:0.725004
[72]	train-auc:0.746295	valid-auc:0.725568
[73]	train-auc:0.746704	valid-auc:0.726251
[74]	train-auc:0.748438	valid-auc:0.725851
[75]	train-auc:0.749826	valid-auc:0.725635
[76]	train-auc:0.75082	valid-auc:0.725938
[77]	train-auc:0.751086	valid-auc:0.725999
[78]	train-auc:0.751669	valid-auc:0.726231
[79]	train-auc:0.752489	valid-auc:0.727117
[80]	train-auc:0.753949	valid-auc:0.727134
[81]	train-auc:0.755291	valid-auc:0.726178
[82]	train-auc:0.756321	valid-auc:0.726569
[83]	train-auc:0.756968	valid-auc:0.726117
[84]	train-auc:0.757323	valid-auc:0.726592
[85]	train-auc:0.75847	valid-auc:0.726169
[86]	train-auc:0.759412	valid-auc:0.726631
[87]	train-auc:0.760295	valid-auc:0.727103
[88]	train-auc:0.76096	valid-auc:0.727332
[89]	train-auc:0.761466	valid-auc:0.72738
[90]	train-auc:0.761956	valid-auc:0.727124
[91]	train-auc:0.762598	valid-auc:0.727343
[92]	train-auc:0.763284	valid-auc:0.727288
[93]	train-auc:0.764034	valid-auc:0.727762
[94]	train-auc:0.765169	valid-auc:0.727744
[95]	train-auc:0.765654	valid-auc:0.727952
[96]	train-auc:0.766339	valid-auc:0.727543
[97]	train-auc:0.767103	valid-auc:0.727882
[98]	train-auc:0.767698	valid-auc:0.7276
[99]	train-auc:0.768139	valid-auc:0.727896
[100]	train-auc:0.769056	valid-auc:0.727844
[101]	train-auc:0.769664	valid-auc:0.727662
[102]	train-auc:0.770348	valid-auc:0.72775
[103]	train-auc:0.771012	valid-auc:0.727865
[104]	train-auc:0.771502	valid-auc:0.727361
[105]	train-auc:0.771718	valid-auc:0.727267
[106]	train-auc:0.772413	valid-auc:0.727144
[107]	train-auc:0.773591	valid-auc:0.727614
[108]	train-auc:0.774238	valid-auc:0.727936
[109]	train-auc:0.774888	valid-auc:0.727492
[110]	train-auc:0.775292	valid-auc:0.727703
[111]	train-auc:0.775726	valid-auc:0.727524
[112]	train-auc:0.77647	valid-auc:0.727861
[113]	train-auc:0.776746	valid-auc:0.727953
[114]	train-auc:0.777214	valid-auc:0.72824
[115]	train-auc:0.777767	valid-auc:0.728335
[116]	train-auc:0.778324	valid-auc:0.728767
[117]	train-auc:0.778621	valid-auc:0.728686
[118]	train-auc:0.779474	valid-auc:0.728408
[119]	train-auc:0.779594	valid-auc:0.728552
[120]	train-auc:0.780434	valid-auc:0.728811
[121]	train-auc:0.780942	valid-auc:0.728929
[122]	train-auc:0.781611	valid-auc:0.728638
[123]	train-auc:0.782058	valid-auc:0.728213
[124]	train-auc:0.782497	valid-auc:0.728345
[125]	train-auc:0.782695	valid-auc:0.728355
[126]	train-auc:0.783278	valid-auc:0.728187
[127]	train-auc:0.783857	valid-auc:0.728307
[128]	train-auc:0.785357	valid-auc:0.728558
[129]	train-auc:0.786408	valid-auc:0.728605
[130]	train-auc:0.786641	valid-auc:0.728204
[131]	train-auc:0.78699	valid-auc:0.728205
[132]	train-auc:0.78734	valid-auc:0.728547
[133]	train-auc:0.787531	valid-auc:0.728642
[134]	train-auc:0.787674	valid-auc:0.728555
[135]	train-auc:0.788111	valid-auc:0.728288
[136]	train-auc:0.788085	valid-auc:0.728336
[137]	train-auc:0.788301	valid-auc:0.72827
[138]	train-auc:0.788895	valid-auc:0.728084
[139]	train-auc:0.789172	valid-auc:0.728324
[140]	train-auc:0.789289	valid-auc:0.728274
[141]	train-auc:0.790212	valid-auc:0.72758
[142]	train-auc:0.790522	valid-auc:0.727458
[143]	train-auc:0.790869	valid-auc:0.727763
[144]	train-auc:0.791014	valid-auc:0.727799
[145]	train-auc:0.792016	valid-auc:0.727851
[146]	train-auc:0.792136	valid-auc:0.727567
[147]	train-auc:0.792912	valid-auc:0.728168
[148]	train-auc:0.793167	valid-auc:0.728093
[149]	train-auc:0.793554	valid-auc:0.728092
[150]	train-auc:0.793799	valid-auc:0.728014
[151]	train-auc:0.79438	valid-auc:0.727636
[152]	train-auc:0.79488	valid-auc:0.727206
[153]	train-auc:0.794954	valid-auc:0.727216
[154]	train-auc:0.795286	valid-auc:0.727147
[155]	train-auc:0.795409	valid-auc:0.726973
[156]	train-auc:0.795672	valid-auc:0.726886
[157]	train-auc:0.795644	valid-auc:0.726914
[158]	train-auc:0.79585	valid-auc:0.726809
[159]	train-auc:0.796192	valid-auc:0.726799
[160]	train-auc:0.796334	valid-auc:0.726809
[161]	train-auc:0.796569	valid-auc:0.726809
[162]	train-auc:0.796767	valid-auc:0.727005
[163]	train-auc:0.797646	valid-auc:0.727079
[164]	train-auc:0.79776	valid-auc:0.727111
[165]	train-auc:0.798153	valid-auc:0.727248
[166]	train-auc:0.798327	valid-auc:0.727091
[167]	train-auc:0.798947	valid-auc:0.727658
[168]	train-auc:0.79919	valid-auc:0.727506
[169]	train-auc:0.799856	valid-auc:0.727332
[170]	train-auc:0.800184	valid-auc:0.727276
[171]	train-auc:0.80018	valid-auc:0.72726
Stopping. Best iteration:
[121]	train-auc:0.780942	valid-auc:0.728929

[mlcrate] Finished training fold 6 - took 5s - running score 0.7170341428571428
[mlcrate] Finished training 7 XGBoost models, took 24s

In [40]:
# Create parameters to search
gridParams = {
    'learning_rate': [0.05],
    'n_estimators': [100, 150, 250, 500],
    'num_leaves': [255,511],
    'boosting_type' : ['gbdt'],
    'objective' : ['binary'],
    'random_state' : [501], # Updated from 'seed'
    'colsample_bytree' : [.7, 0.74, 0.75, 0.76, .85],
    'subsample' : [0.7,0.75, .8],
    'reg_alpha' : [1,1.2],
    'reg_lambda' : [1,1.2,1.4],
    }

# Create classifier to use. Note that parameters have to be input manually
# not as a dict!
mdl = lgb.LGBMClassifier(boosting_type= 'gbdt', 
          objective = 'binary', 
          n_jobs = -1, # Updated from 'nthread' 
          silent = False,
          max_depth = 8,
          max_bin = 128, 
          subsample_for_bin = 1,
          subsample = .7, 
          subsample_freq = 1, 
          min_split_gain = .5,  
          scale_pos_weight = 0.1346)

# To view the default model params:
mdl.get_params().keys()


Out[40]:
dict_keys(['boosting_type', 'class_weight', 'colsample_bytree', 'learning_rate', 'max_depth', 'min_child_samples', 'min_child_weight', 'min_split_gain', 'n_estimators', 'n_jobs', 'num_leaves', 'objective', 'random_state', 'reg_alpha', 'reg_lambda', 'silent', 'subsample', 'subsample_for_bin', 'subsample_freq', 'max_bin', 'scale_pos_weight'])

In [41]:
# Create the grid
grid = GridSearchCV(mdl, gridParams, verbose=1, cv=5, n_jobs=-1)
# Run the grid
grid.fit(X_train_stack, target)

# Print the best parameters found
print(grid.best_params_)
print(grid.best_score_)


Fitting 5 folds for each of 720 candidates, totalling 3600 fits
[Parallel(n_jobs=-1)]: Done  42 tasks      | elapsed:   11.1s
[Parallel(n_jobs=-1)]: Done 192 tasks      | elapsed:   33.7s
[Parallel(n_jobs=-1)]: Done 442 tasks      | elapsed:  1.6min
[Parallel(n_jobs=-1)]: Done 792 tasks      | elapsed:  4.1min
[Parallel(n_jobs=-1)]: Done 1242 tasks      | elapsed:  5.7min
[Parallel(n_jobs=-1)]: Done 1792 tasks      | elapsed:  8.5min
[Parallel(n_jobs=-1)]: Done 2442 tasks      | elapsed: 11.8min
[Parallel(n_jobs=-1)]: Done 3192 tasks      | elapsed: 15.4min
[Parallel(n_jobs=-1)]: Done 3600 out of 3600 | elapsed: 18.0min finished
{'boosting_type': 'gbdt', 'colsample_bytree': 0.7, 'learning_rate': 0.05, 'n_estimators': 100, 'num_leaves': 255, 'objective': 'binary', 'random_state': 501, 'reg_alpha': 1, 'reg_lambda': 1, 'subsample': 0.7}
0.867912195653

In [45]:
params = {}

In [46]:
# Using parameters already set above, replace in the best from the grid search
params['colsample_bytree'] = grid.best_params_['colsample_bytree']
params['learning_rate'] = 0.05 
# params['max_bin'] = grid.best_params_['max_bin']
params['num_leaves'] = grid.best_params_['num_leaves']
params['reg_alpha'] = grid.best_params_['reg_alpha']
params['reg_lambda'] = grid.best_params_['reg_lambda']
params['subsample'] = grid.best_params_['subsample']

print('Fitting with params: ')
print(params)

# Kit k models with early-stopping on different training/validation splits
k = 2;
predsValid = 0 
predsTrain = 0
predsTest = 0
l = ['split','gain']
for i in range(0, k): 
    print('Fitting model', i)
    
    # Prepare the data set for fold
    X_train, X_test, y_train, y_test = train_test_split(X_train_stack, target, test_size=0.2, random_state=7, shuffle=True)
    
    d_train = lgb.Dataset(X_train, label=y_train)
    d_test = lgb.Dataset(X_test, label=y_test)
    
    # Train     
    gbm = lgb.train(params,
                    d_train, 
                    1000, 
                    verbose_eval=1)

    # Plot importance
    lgb.plot_importance(gbm,max_num_features=10, importance_type=l[i])
    plt.show()


Fitting with params: 
{'colsample_bytree': 0.7, 'learning_rate': 0.05, 'num_leaves': 255, 'reg_alpha': 1, 'reg_lambda': 1, 'subsample': 0.7}
Fitting model 0
Fitting model 1

In [ ]:

random


In [45]:
def train_k_fold(x_train, y_train, model=None, x_test=None, folds=7, stratify=None, random_state=1337):

    assert model is not None, "model can't be none, Please pass your model."
    
    if hasattr(x_train, 'columns'):
        columns = x_train.columns.values
        columns_exists = True
    else:
        columns = np.arange(x_train.shape[1])
        columns_exists = False

    x_train = np.asarray(x_train)
    y_train = np.array(y_train)

    if x_test is not None:
        if columns_exists:
            try:
                x_test = x_test[columns]
            except Exception as e:
                print('x_test columns doesn\'t match x_train columns.')
                raise e
        x_test = np.asarray(x_test)

    assert x_train.shape[1] == x_test.shape[1], "x_train and x_test have different numbers of features."

    print('Training {} {}models on training set {} {}'.format(folds, 'stratified ' if stratify is not None else '',
        x_train.shape, 'with test set {}'.format(x_test.shape) if x_test is not None else 'without a test set'))

    if stratify is not None:
        kf = StratifiedKFold(n_splits=folds, shuffle=True, random_state=random_state)
        splits = kf.split(x_train, stratify)
    else:
        kf = KFold(n_splits=folds, shuffle=True, random_state=4242)
        splits = kf.split(x_train)

    p_train = np.zeros_like(y_train, dtype=np.float32)
    ps_test = []
    models = []
    scores = []
    fold_i = 0

    for train_kf, valid_kf in splits:
        
        print('Running fold {}, {} train samples, {} validation samples'.format(fold_i, len(train_kf), len(valid_kf)))
        
        d_train, label_train = x_train[train_kf], y_train[train_kf]
        d_valid, label_valid = x_train[valid_kf], y_train[valid_kf]

        mdl = model.fit(d_train,label_train)
        scores.append(mdl.score(d_valid, label_valid))
        print('Finished training fold {} - running score {} '.format(fold_i, np.mean(scores)))

        # Get predictions from the model 

        if hasattr(mdl,'predict_proba'):
            #print('Using model.predict_proba')
            p_valid = mdl.predict_proba(d_valid)[:,1]
            if x_test is not None:
                p_test = mdl.predict_proba(x_test)[:,1]
        else:
            #print('Using model.predict')
            p_valid = mdl.predict(d_valid)
            if x_test is not None:
                p_test = mdl.predict(x_test)

        p_train[valid_kf] = p_valid

        ps_test.append(p_test)
        models.append(mdl)

        fold_i += 1

    if x_test is not None:
        p_test = np.mean(ps_test, axis=0)

    print('Finished training {} models '.format(folds))

    if x_test is None:
        p_test = None

    return models, p_train, p_test, scores

In [46]:
model=CatBoostClassifier(iterations=1020, depth=8, learning_rate=0.06, loss_function= 'Logloss')

In [47]:
stack_test = pd.DataFrame()
stack_train = pd.DataFrame()
log_cols=["Classifier", "Accuracy"]
log = pd.DataFrame(columns=log_cols)

In [ ]:
classifiers = [
    model,
#     GradientBoostingClassifier(max_depth=10,subsample=0.8,max_features='auto'),
#     MLPClassifier((50,30,10), alpha=0.01,validation_fraction=0.2),
#     ExtraTreesClassifier(100,max_depth=10,n_jobs=-1,class_weight='balanced_subsample',bootstrap=True,oob_score=True),
#     DecisionTreeClassifier(min_samples_leaf= 3, class_weight ='balanced', max_features=.85, max_leaf_nodes=5, max_depth = 10),
#     RandomForestClassifier(n_estimators=100,max_features=.85,n_jobs=-1,class_weight='balanced'),
#     AdaBoostClassifier(n_estimators=200, learning_rate=0.15),
#     RandomForestClassifier(n_estimators=200,max_features=.85,max_depth=7,n_jobs=-1,class_weight='balanced'),
#     LogisticRegression(class_weight='balanced',max_iter=500, multi_class='ovr', n_jobs=-1),
    ]

# Logging for Visual Comparison ( see above cell)
count = 0
for clf in classifiers:    
    name = clf.__class__.__name__+'{}'.format(count)
    print("="*60, name)

    models, p_train, p_test, scores = train_k_fold( X_train_stack, target, clf, X_test_stack, 7, target)
    
    stack_test[name] = p_test
    stack_train[name] = p_train
    
    print("Accuracy: {:.4%}".format(np.mean(scores)))
    
    log_entry = pd.DataFrame([[name, np.mean(scores)*100]], columns=log_cols)
    log = log.append(log_entry)
    
    del models, p_train, p_test, scores
    
    count += 1
    print(gc.collect())
#     submit = make_submission(p_test)
#     submit.to_csv(f'{PATH}\\AV_Stud_2\\stacked_sklearn_showdown.csv', index=False)
#     submit.head(2)

In [56]:
preds = stack_test['CatBoostClassifier0'].values

In [58]:
submit = make_submission(preds)
submit.to_csv(f'{PATH}\\AV_Stud_2\\cat_with_eid.csv', index=False)
submit.head(2)


Out[58]:
enrollee_id target
0 16548 0.835014
1 12036 0.060623

cat


In [104]:
params = {'depth':[7,8,9,10,11],
          'iterations':[500,1000, 1500],
          'learning_rate':[0.05,0.001,0.01,0.1,0.2,0.3], 
          'l2_leaf_reg':[3,1,5,10,100],
          'border_count':[32,5,10,20,50,100,200],
          'thread_count':4,
          'loss_function': 'Logloss'}

# this function does 3-fold crossvalidation with catboostclassifier          
def crossvaltest(params, train_set, train_label, cat_dims, n_splits=3):
    
    kf = KFold(n_splits=n_splits,shuffle=True) 
    res = []
    
    for train_kf, valid_kf in kf.split(train_set):
        
        d_train, label_train = train_set[train_kf], train_label[train_kf]
        d_valid, label_valid = train_set[valid_kf], train_label[valid_kf]
        
        clf = CatBoostClassifier(**params)
        clf.fit(d_train, np.ravel(label_train), cat_features=cat_dims)

        res.append(np.mean(clf.predict_proba(d_valid)==np.ravel(label_valid)))
        
    return np.mean(res)

In [105]:
from fastai.paramsearch import paramsearch
from itertools import product,chain

In [106]:
# this function runs grid search on several parameters

def catboost_param_tune(params, train_set, train_label, cat_dims=None,n_splits=7):
    
    ps = paramsearch(params)
    for prms in chain(ps.grid_search(['border_count']),
                      ps.grid_search(['ctr_border_count']),
                      ps.grid_search(['l2_leaf_reg']),
                      ps.grid_search(['iterations','learning_rate']),
                      ps.grid_search(['depth'])):
        
        res = crossvaltest(prms,train_set,train_label,cat_dims,n_splits)
        
        # save the crossvalidation result so that future iterations can reuse the best parameters
        ps.register_result(res,prms)
        print(res,prms,'best:',ps.bestscore(),ps.bestparam())
    return ps.bestparam()

bestparams = catboost_param_tune(params,X_train_stack,target,cat_dims = None)


0:	learn: 0.6555258	total: 59.3ms	remaining: 29.6s
1:	learn: 0.6220049	total: 113ms	remaining: 28.1s
2:	learn: 0.5925177	total: 157ms	remaining: 26s
3:	learn: 0.5668134	total: 204ms	remaining: 25.2s
4:	learn: 0.5442018	total: 249ms	remaining: 24.6s
5:	learn: 0.5263823	total: 273ms	remaining: 22.4s
6:	learn: 0.5088891	total: 321ms	remaining: 22.6s
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C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:23: DeprecationWarning: elementwise == comparison failed; this will raise an error in the future.
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