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)
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)]
------------------------------------------------------------------------------------------------------------------------
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))
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]
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>
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)
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)
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')
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
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 [ ]:
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
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)
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0.0 {'depth': 7, 'iterations': 500, 'learning_rate': 0.05, 'l2_leaf_reg': 3, 'border_count': 200, 'thread_count': 4, 'loss_function': 'Logloss'} best: 1.64308451576e-10 {'depth': 7, 'iterations': 500, 'learning_rate': 0.05, 'l2_leaf_reg': 3, 'border_count': 200, 'thread_count': 4, 'loss_function': 'Logloss'}
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-106-79ae2b5ff0c3> in <module>()
17 return ps.bestparam()
18
---> 19 bestparams = catboost_param_tune(params,X_train_stack,target,cat_dims = None)
<ipython-input-106-79ae2b5ff0c3> in catboost_param_tune(params, train_set, train_label, cat_dims, n_splits)
8 ps.grid_search(['l2_leaf_reg']),
9 ps.grid_search(['iterations','learning_rate']),
---> 10 ps.grid_search(['depth'])):
11
12 res = crossvaltest(prms,train_set,train_label,cat_dims,n_splits)
D:\Github\fastai\courses\ml1\fastai\paramsearch.py in grid_search(self, keys)
41
42 listoflists = [] # this will be list of lists of key,value pairs
---> 43 for key in keylist: listoflists.append([(key,i) for i in self.pdict[key]])
44 for p in product(*listoflists):
45 # do any changes to the current best parameter set
KeyError: 'ctr_border_count'
In [ ]:
Content source: AdityaSoni19031997/Machine-Learning
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