av_student_datafest_2_v1


the_comp_details

To understand the factors that lead a person to look for a job change, the agency wants you to design a model that uses the current credentials/demographics/experience to predict the probability of an enrollee to look for a new job.

Data Dictionary

Variable Description

  • enrollee_id - Unique ID for enrollee
  • city - City code
  • city_development_index - Developement index of the city (scaled)
  • gender - Gender
  • relevent_experience - Relevent experience
  • enrolled_university - Type of University course enrolled if any
  • education_level - Education level
  • major_discipline - Major discipline
  • experience Total - experience in years
  • company_size - No of employees in current employer's company
  • company_type - Type of current employer
  • last_new_job - Difference in years between previous job and current job
  • training_hours - training hours completed
  • target 0 – Not looking for job change, 1 – Looking for a job change

imports


In [1]:
%load_ext autoreload
%autoreload 2

%matplotlib inline

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

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

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

from sklearn.metrics import accuracy_score, log_loss
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", 1000);
pd.option_context("display.max_columns", 1000);

In [3]:
PATH = os.getcwd();
PATH


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

read the datasets


In [11]:
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)

# STEM - Science, Technology, Engineering, Management

In [12]:
def display_all(df):
    with pd.option_context("display.max_rows", 100): 
        with pd.option_context("display.max_columns", 100): 
            display(df)

In [13]:
df_raw.shape,


Out[13]:
((18359, 14),)

In [14]:
df_raw.get_ftype_counts()


Out[14]:
float64:dense     1
int64:dense       3
object:dense     10
dtype: int64

initial processing


In [40]:
m = RandomForestRegressor(n_jobs=-1)
m.fit(df_raw.drop('target', axis=1), df_raw.target)


---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-40-e7e89172fae8> in <module>()
      1 m = RandomForestRegressor(n_jobs=-1)
----> 2 m.fit(df_raw.drop('target', axis=1), df_raw.target)

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\forest.py in fit(self, X, y, sample_weight)
    245         """
    246         # Validate or convert input data
--> 247         X = check_array(X, accept_sparse="csc", dtype=DTYPE)
    248         y = check_array(y, accept_sparse='csc', ensure_2d=False, dtype=None)
    249         if sample_weight is not None:

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    431                                       force_all_finite)
    432     else:
--> 433         array = np.array(array, dtype=dtype, order=order, copy=copy)
    434 
    435         if ensure_2d:

ValueError: could not convert string to float: 'never'

This dataset contains a mix of continuous and categorical variables.


In [15]:
cols = ['enrollee_id', 'city', 'city_development_index', 'gender',
       'relevent_experience', 'enrolled_university', 'enrolled_university_degree',
       'major_discipline', 'experience', 'company_size', 'company_type',
       'last_new_job', 'training_hours', 'target']
df_raw.columns = cols
df_test.columns = cols[:-1]

In [18]:
train_cats(X_train_od);
apply_cats(X_test_od, X_train_od);


D:\Github\fastai\courses\ml1\fastai\structured.py:204: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  df[n] = pd.Categorical(c, categories=trn[n].cat.categories, ordered=True)

In [19]:
X_train_od.enrolled_university.cat.categories;
X_train_od.enrolled_university.cat.set_categories(['no_enrollment', 'Part time course', 'Full time course' ],\
                                    ordered=True, inplace=True)
X_train_od.enrolled_university = X_train_od.enrolled_university.cat.codes

X_train_od.enrolled_university_degree.cat.categories;
X_train_od.enrolled_university_degree.cat.set_categories(['Primary School','High School','Graduate', 'Masters', 'Phd',],\
                                    ordered=True, inplace=True)
X_train_od.enrolled_university_degree = X_train_od.enrolled_university_degree.cat.codes

X_train_od.relevent_experience.cat.set_categories(['No relevent experience','Has relevent experience'],\
                                    ordered=True, inplace=True)
X_train_od.relevent_experience = X_train_od.relevent_experience.cat.codes


C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\generic.py:3110: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  self[name] = value

In [20]:
X_test_od.enrolled_university.cat.categories;
X_test_od.enrolled_university.cat.set_categories(['no_enrollment', 'Part time course', 'Full time course' ],\
                                    ordered=True, inplace=True)
X_test_od.enrolled_university = X_test_od.enrolled_university.cat.codes

X_test_od.enrolled_university_degree.cat.categories;
X_test_od.enrolled_university_degree.cat.set_categories(['Primary School','High School','Graduate', 'Masters', 'Phd',],\
                                    ordered=True, inplace=True)
X_test_od.enrolled_university_degree = X_test_od.enrolled_university_degree.cat.codes

X_test_od.relevent_experience.cat.set_categories(['No relevent experience','Has relevent experience'],\
                                    ordered=True, inplace=True)
X_test_od.relevent_experience = X_test_od.relevent_experience.cat.codes


C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\generic.py:3110: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  self[name] = value

In [16]:
drop = ['enrollee_id']
df_raw.drop(drop, axis=1,inplace=True)
df_test.drop(drop, axis=1,inplace=True)

In [17]:
df_raw['enrolled_university_degree'].fillna(df_raw['enrolled_university_degree'].mode()[0],inplace=True)
df_raw['enrolled_university'].fillna(df_raw['enrolled_university'].mode()[0],inplace=True)

df_test['enrolled_university_degree'].fillna(df_test['enrolled_university_degree'].mode()[0],inplace=True)
df_test['enrolled_university'].fillna(df_test['enrolled_university'].mode()[0],inplace=True)

In [114]:
df, y, nas,  = proc_df(df_raw, 'target', max_n_cat=20,)

In [66]:
from sklearn.model_selection import train_test_split
X_train, X_valid, y_train, y_valid = train_test_split(df, y, test_size=0.2, random_state=42, stratify = y)

def split_vals(a,n): return a[:n].copy(), a[n:].copy()

n_valid = 2000  # same as Kaggle's test set size
n_trn = len(df)-n_valid
raw_train, raw_valid = split_vals(df_raw, n_trn)
X_train, X_valid = split_vals(df, n_trn)
y_train, y_valid = split_vals(y, n_trn)

X_train.shape, y_train.shape, X_valid.shape


Out[66]:
((16359, 41), (16359,), (2000, 41))

Baseline RF


In [70]:
def logloss(x,y): return metrics.log_loss(y_true = y, y_pred = x)

def print_score(m):
    
    print('Train Loss, Valid Loss, R**2 Train, R**2 Valid, OOB_Score(optional)')
    res = [logloss(m.predict_proba(X_train), y_train), logloss(m.predict_proba(X_valid), y_valid),
                m.score(X_train, y_train), m.score(X_valid, y_valid)]
    if hasattr(m, 'oob_score_'): res.append(m.oob_score_)
    print(res)

In [68]:
m = RandomForestClassifier(n_estimators=20,n_jobs=-1, max_depth=5, max_features='auto')
m.fit(X_train, y_train)
print_score(m)


Train Loss, Valid Loss, R**2 Train, R**2 Valid, OOB_Score(optional)
[0.36985482373263973, 0.36430670428351336, 0.86710679136866553, 0.87450000000000006]

In [77]:
draw_tree(m.estimators_[0], df, precision=3)


Tree 0 col_city_development_index ≤ 0.625 gini = 0.223 samples = 11562 value = [16010, 2349] 1 col_experience_2 ≤ -0.413 gini = 0.379 samples = 1316 value = [1542, 526] 0->1 True 24 col_city_103 ≤ 0.337 gini = 0.199 samples = 10246 value = [14468, 1823] 0->24 False 2 col_company_size_3 ≤ -0.167 gini = 0.438 samples = 178 value = [192, 92] 1->2 15 col_enrollee_id ≤ 1579.5 gini = 0.368 samples = 1138 value = [1350, 434] 1->15 3 col_enrollee_id ≤ 22460.0 gini = 0.463 samples = 103 value = [103, 59] 2->3 10 col_enrolled_university_1 ≤ -0.25 gini = 0.395 samples = 75 value = [89, 33] 2->10 4 col_experience_1 ≤ -0.457 gini = 0.496 samples = 69 value = [59, 49] 3->4 7 col_city_65 ≤ 0.028 gini = 0.302 samples = 34 value = [44, 10] 3->7 5 gini = 0.0 samples = 2 value = [4, 0] 4->5 6 gini = 0.498 samples = 67 value = [55, 49] 4->6 8 gini = 0.34 samples = 29 value = [36, 10] 7->8 9 gini = 0.0 samples = 5 value = [8, 0] 7->9 11 gini = 0.0 samples = 1 value = [0, 4] 10->11 12 col_company_type_4 ≤ 0.071 gini = 0.371 samples = 74 value = [89, 29] 10->12 13 gini = 0.377 samples = 69 value = [83, 28] 12->13 14 gini = 0.245 samples = 5 value = [6, 1] 12->14 16 gini = 0.0 samples = 12 value = [21, 0] 15->16 17 col_city_74 ≤ 0.102 gini = 0.371 samples = 1126 value = [1329, 434] 15->17 18 col_company_size_7 ≤ 0.278 gini = 0.366 samples = 1074 value = [1274, 405] 17->18 21 col_city_95 ≤ 0.272 gini = 0.452 samples = 52 value = [55, 29] 17->21 19 gini = 0.358 samples = 930 value = [1124, 343] 18->19 20 gini = 0.414 samples = 144 value = [150, 62] 18->20 22 gini = 0.492 samples = 37 value = [35, 27] 21->22 23 gini = 0.165 samples = 15 value = [20, 2] 21->23 25 col_company_size_1 ≤ -0.389 gini = 0.198 samples = 10151 value = [14343, 1796] 24->25 40 col_last_new_job_5 ≤ 0.214 gini = 0.292 samples = 95 value = [125, 27] 24->40 26 col_city_10 ≤ -0.419 gini = 0.281 samples = 2580 value = [3338, 681] 25->26 33 col_last_new_job_6 ≤ 0.357 gini = 0.167 samples = 7571 value = [11005, 1115] 25->33 27 col_major_discipline_1 ≤ -0.357 gini = 0.305 samples = 1332 value = [1682, 389] 26->27 30 col_experience_10 ≤ -0.065 gini = 0.255 samples = 1248 value = [1656, 292] 26->30 28 gini = 0.21 samples = 454 value = [615, 83] 27->28 29 gini = 0.346 samples = 878 value = [1067, 306] 27->29 31 gini = 0.22 samples = 697 value = [939, 135] 30->31 32 gini = 0.295 samples = 551 value = [717, 157] 30->32 34 col_city_75 ≤ 0.11 gini = 0.162 samples = 7069 value = [10313, 1007] 33->34 37 col_training_hours ≤ 110.5 gini = 0.234 samples = 502 value = [692, 108] 33->37 35 gini = 0.16 samples = 6874 value = [10044, 968] 34->35 36 gini = 0.221 samples = 195 value = [269, 39] 34->36 38 gini = 0.212 samples = 421 value = [592, 81] 37->38 39 gini = 0.335 samples = 81 value = [100, 27] 37->39 41 col_training_hours ≤ 3.0 gini = 0.326 samples = 75 value = [97, 25] 40->41 46 col_training_hours ≤ 14.5 gini = 0.124 samples = 20 value = [28, 2] 40->46 42 gini = 0.0 samples = 1 value = [0, 2] 41->42 43 col_experience_14 ≤ 0.109 gini = 0.31 samples = 74 value = [97, 23] 41->43 44 gini = 0.225 samples = 52 value = [74, 11] 43->44 45 gini = 0.451 samples = 22 value = [23, 12] 43->45 47 col_company_size_1 ≤ -0.389 gini = 0.5 samples = 2 value = [1, 1] 46->47 50 col_experience_5 ≤ -0.283 gini = 0.069 samples = 18 value = [27, 1] 46->50 48 gini = 0.0 samples = 1 value = [1, 0] 47->48 49 gini = 0.0 samples = 1 value = [0, 1] 47->49 51 gini = 0.245 samples = 5 value = [6, 1] 50->51 52 gini = 0.0 samples = 13 value = [21, 0] 50->52

In [73]:
m = RandomForestClassifier(n_estimators=10, n_jobs=-1, oob_score=True, max_depth=5)
m.fit(X_train, y_train)
print_score(m)


C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\forest.py:453: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.
  warn("Some inputs do not have OOB scores. "
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\forest.py:458: RuntimeWarning: invalid value encountered in true_divide
  predictions[k].sum(axis=1)[:, np.newaxis])
Train Loss, Valid Loss, R**2 Train, R**2 Valid, OOB_Score(optional)
[0.36979450404333986, 0.36537382401608476, 0.86710679136866553, 0.87450000000000006, 0.86692340607616603]

In [74]:
m = RandomForestClassifier(n_estimators=200, min_samples_leaf=3,max_features=0.5, n_jobs=-1, oob_score=True)
m.fit(X_train, y_train)
print_score(m)


Train Loss, Valid Loss, R**2 Train, R**2 Valid, OOB_Score(optional)
[0.20387255010799227, 0.38711766787524177, 0.89974937343358397, 0.87350000000000005, 0.86484503942783786]

In [75]:
fi = rf_feat_importance(m, df); fi[:10]


Out[75]:
cols imp
6 training_hours 0.257144
5 experience 0.149266
1 city_development_index 0.118949
0 city 0.094909
4 enrolled_university_degree 0.038956
3 enrolled_university 0.031631
34 last_new_job_1 0.025014
8 gender_Male 0.019788
2 relevent_experience 0.019407
10 gender_nan 0.017197

In [76]:
fi.plot('cols', 'imp', figsize=(10,6), legend=False);



In [77]:
def plot_fi(fi): return fi.plot('cols', 'imp', 'barh', figsize=(12,7), legend=False)

In [78]:
plot_fi(fi[:30]);



In [85]:
to_keep = fi[fi.imp>0.002].cols; len(to_keep)


Out[85]:
42

In [87]:
df_keep = df[to_keep].copy()
X_train, X_valid = split_vals(df_keep, n_trn)

In [97]:
m = RandomForestRegressor(n_estimators=200, max_features=0.5,
                          n_jobs=-1, oob_score=True)
m.fit(X_train, y_train)
print_score(m)


Train Loss, Valid Loss, R**2 Train, R**2 Valid
[0.094103076588840262, 0.38694119197589372, 0.86124718844888137, -0.026665415171825746, -0.013963884820979544]

In [91]:
fi = rf_feat_importance(m, df_keep)
plot_fi(fi[:25]);



In [92]:
from scipy.cluster import hierarchy as hc

In [93]:
corr = np.round(scipy.stats.spearmanr(df_keep).correlation, 4)
corr_condensed = hc.distance.squareform(1-corr)
z = hc.linkage(corr_condensed, method='average')
fig = plt.figure(figsize=(16,10))
dendrogram = hc.dendrogram(z, labels=df_keep.columns, orientation='left', leaf_font_size=16)
plt.show()



In [94]:
def get_oob(df):
    m = RandomForestRegressor(n_estimators=200, max_features=0.6, n_jobs=-1, oob_score=True)
    x, _ = split_vals(df, n_trn)
    m.fit(x, y_train)
    return m.oob_score_

preds


In [79]:
m


Out[79]:
RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features=0.5, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=3, min_samples_split=2,
            min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=-1,
            oob_score=True, random_state=None, verbose=0, warm_start=False)

In [118]:
m = RandomForestClassifier(n_estimators=200, min_samples_leaf=3,max_features=0.5, n_jobs=-1, oob_score=True)
m.fit(df, y)
print_score(m)


Train Loss, Valid Loss, R**2 Train, R**2 Valid, OOB_Score(optional)
[0.20420249978426797, 0.2008521456131836, 0.89730423620025679, 0.89949999999999997, 0.86507979737458462]

In [119]:
df_test['target'] = y[:df_test.shape[0]]
df_test_, _, _ = proc_df(df_test, 'target', na_dict=nas, max_n_cat=20)

In [120]:
set(df.columns) - set(df_test_.columns)


Out[120]:
set()

In [121]:
set(df_test_.columns) - set(df.columns)


Out[121]:
set()

In [122]:
preds = m.predict_proba(df_test_)

In [49]:
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 [112]:
submit = make_submission(preds)
submit.to_csv(f'{PATH}\\AV_Stud_2\\rf.csv', index=False)
submit.head(2)


Out[112]:
enrollee_id target
0 16548 0.815661
1 12036 0.027220

9th July


In [17]:
#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
categorical_features;

train_cats(df_raw);
apply_cats(df_test, df_raw)

X_train_num = df_raw.drop(categorical_features,axis=1)  #numeric ones
X_test_num  = df_test.drop(categorical_features,axis=1) #numeric ones

X_train_od = df_raw[categorical_features]  #numeric ones
X_test_od  = df_test[categorical_features] #numeric ones

In [69]:
import category_encoders as cat_ed

In [70]:
encoder = cat_ed.backward_difference.BackwardDifferenceEncoder(drop_invariant=True,cols=categorical_features)

df_raw = encoder.fit_transform(df_raw, verbose=1)
df_test = encoder.transform(df_test)

In [71]:
df_raw.shape, df_test.shape


Out[71]:
((18359, 183), (15021, 183))

In [51]:
def Intersection(lst1, lst2):
    return list(set(lst1).intersection(lst2))

In [70]:
model=CatBoostClassifier(iterations=1020, depth=8, learning_rate=0.06, loss_function= 'Logloss')
model.fit(X_stack_train, target)


0:	learn: 0.6503847	total: 282ms	remaining: 4m 47s
1:	learn: 0.6129987	total: 351ms	remaining: 2m 58s
2:	learn: 0.5796037	total: 421ms	remaining: 2m 22s
3:	learn: 0.5514890	total: 485ms	remaining: 2m 3s
4:	learn: 0.5270214	total: 552ms	remaining: 1m 52s
5:	learn: 0.5064879	total: 618ms	remaining: 1m 44s
6:	learn: 0.4889237	total: 685ms	remaining: 1m 39s
7:	learn: 0.4731387	total: 749ms	remaining: 1m 34s
8:	learn: 0.4591367	total: 825ms	remaining: 1m 32s
9:	learn: 0.4473548	total: 894ms	remaining: 1m 30s
10:	learn: 0.4366933	total: 961ms	remaining: 1m 28s
11:	learn: 0.4274822	total: 1.03s	remaining: 1m 26s
12:	learn: 0.4196324	total: 1.09s	remaining: 1m 24s
13:	learn: 0.4130140	total: 1.15s	remaining: 1m 22s
14:	learn: 0.4067396	total: 1.22s	remaining: 1m 21s
15:	learn: 0.4013903	total: 1.28s	remaining: 1m 20s
16:	learn: 0.3965975	total: 1.35s	remaining: 1m 19s
17:	learn: 0.3923884	total: 1.41s	remaining: 1m 18s
18:	learn: 0.3893522	total: 1.48s	remaining: 1m 17s
19:	learn: 0.3860844	total: 1.54s	remaining: 1m 17s
20:	learn: 0.3832146	total: 1.61s	remaining: 1m 16s
21:	learn: 0.3806680	total: 1.68s	remaining: 1m 16s
22:	learn: 0.3786167	total: 1.75s	remaining: 1m 15s
23:	learn: 0.3764654	total: 1.82s	remaining: 1m 15s
24:	learn: 0.3746455	total: 1.88s	remaining: 1m 15s
25:	learn: 0.3728226	total: 1.96s	remaining: 1m 14s
26:	learn: 0.3712116	total: 2.02s	remaining: 1m 14s
27:	learn: 0.3700237	total: 2.08s	remaining: 1m 13s
28:	learn: 0.3689403	total: 2.15s	remaining: 1m 13s
29:	learn: 0.3676505	total: 2.21s	remaining: 1m 13s
30:	learn: 0.3663333	total: 2.28s	remaining: 1m 12s
31:	learn: 0.3655494	total: 2.34s	remaining: 1m 12s
32:	learn: 0.3646756	total: 2.41s	remaining: 1m 12s
33:	learn: 0.3636094	total: 2.47s	remaining: 1m 11s
34:	learn: 0.3624883	total: 2.54s	remaining: 1m 11s
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---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
<ipython-input-70-3f6458ffdb95> in <module>()
      1 model=CatBoostClassifier(iterations=1020, depth=8, learning_rate=0.06, loss_function= 'Logloss')
----> 2 model.fit(X_stack_train, target)

C:\ProgramData\Anaconda3\lib\site-packages\catboost\core.py in fit(self, X, y, cat_features, sample_weight, baseline, use_best_model, eval_set, verbose, logging_level, plot)
   1298         model : CatBoost
   1299         """
-> 1300         self._fit(X, y, cat_features, None, sample_weight, None, None, baseline, use_best_model, eval_set, verbose, logging_level, plot)
   1301         if y is not None:
   1302             setattr(self, "_classes", np.unique(y))

C:\ProgramData\Anaconda3\lib\site-packages\catboost\core.py in _fit(self, X, y, cat_features, pairs, sample_weight, query_id, pairs_weight, baseline, use_best_model, eval_set, verbose, logging_level, plot)
    571                 raise ImportError(str(e))
    572         with log_fixup():
--> 573             self._train(X, eval_set, params)
    574         if calc_feature_importance:
    575             setattr(self, "_feature_importance", self.get_feature_importance(X))

_catboost.pyx in _catboost._CatBoostBase._train()

_catboost.pyx in _catboost._CatBoost._train()

_catboost.pyx in _catboost._CatBoost._train()

KeyboardInterrupt: 

In [89]:
preds = model.predict_proba(df_test)[:,1]

In [91]:
m = RandomForestClassifier(n_estimators=200,n_jobs=-1, max_features='auto')
m.fit(df_raw, target)


Out[91]:
RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=-1,
            oob_score=False, random_state=None, verbose=0,
            warm_start=False)

In [92]:
fi = rf_feat_importance(m, df_raw); fi[:10]


Out[92]:
cols imp
182 col_training_hours 0.216338
181 col_city_development_index 0.051304
121 col_gender_1 0.030305
129 col_enrolled_university_degree_2 0.027899
176 col_last_new_job_2 0.026573
124 col_relevent_experience_1 0.023331
126 col_enrolled_university_2 0.023020
177 col_last_new_job_3 0.018803
178 col_last_new_job_4 0.017770
169 col_company_type_1 0.015755

In [97]:
preds = m.predict_proba(df_test)[:,1]

In [108]:
X_train['target'] = target

In [106]:
X_train['city_development_index'].value_counts().sort_values(ascending=False).head(20)


Out[106]:
0.920    5185
0.624    1672
0.910    1654
0.926    1472
0.698     655
0.897     624
0.939     544
0.855     455
0.924     318
0.804     313
0.884     281
0.887     271
0.754     264
0.913     217
0.899     194
0.802     188
0.925     178
0.893     175
0.878     156
0.743     152
Name: city_development_index, dtype: int64

In [113]:
X_train.columns


Out[113]:
Index(['enrollee_id', 'city_development_index', 'training_hours', 'target'], dtype='object')

In [116]:
with sns.axes_style("white"):
    sns.jointplot(x='training_hours', y='city_development_index',data=X_train, kind="hex", color='k');



In [142]:
sns.lmplot('training_hours','city_development_index', X_train, 'target', x_bins= 50)


Out[142]:
<seaborn.axisgrid.FacetGrid at 0x2be282f8550>

In [147]:
X_test.columns, X_train.columns


Out[147]:
(Index(['city_development_index', 'training_hours'], dtype='object'),
 Index(['city_development_index', 'training_hours', 'target'], dtype='object'))

In [155]:
preds = X_test.training_hours.map(X_train.groupby('training_hours')['target'].mean())

In [156]:
preds


Out[156]:
0        0.101852
1        0.120690
2        0.118367
3        0.115385
4        0.118421
5        0.150000
6        0.147982
7        0.081818
8        0.075758
9        0.142857
10       0.123404
11       0.107345
12       0.206897
13       0.177778
14       0.120833
15       0.166667
16       0.129825
17       0.138996
18       0.118143
19       0.123404
20       0.119048
21       0.131868
22       0.103093
23       0.113043
24       0.000000
25       0.092896
26       0.076923
27       0.117318
28       0.159091
29       0.109091
           ...   
14991    0.108108
14992    0.166667
14993    0.149194
14994    0.428571
14995    0.120833
14996    0.139665
14997    0.160494
14998    0.153846
14999    0.104348
15000    0.131579
15001    0.131868
15002    0.125000
15003    0.152344
15004    0.171429
15005    0.092593
15006    0.169231
15007    0.134831
15008    0.129825
15009    0.123404
15010    0.088889
15011    0.155340
15012    0.081818
15013    0.095238
15014    0.152344
15015    0.103093
15016    0.171429
15017    0.000000
15018    0.180000
15019    0.104762
15020    0.069307
Name: training_hours, Length: 15021, dtype: float64

In [57]:
X_train_od['y'] = target
# plt.figure(figsize=(30,32))
for i in range(len(categorical_features)):
    
    plt.figure(figsize=(10,10))
    c = categorical_features[i]
    
    means = X_train_od.groupby(c).y.mean()
    stds = X_train_od.groupby(c).y.std().fillna(0)
    maxs = X_train_od.groupby(c).y.max()
    mins = X_train_od.groupby(c).y.min()
    
    ddd = pd.concat([means, stds, maxs, mins], axis=1); 
    ddd.columns = ['means', 'stds', 'maxs', 'mins']
    ddd.sort_values('means', inplace=True)
    

    ax = sns.countplot(x=c, order=ddd.index.values,data=X_train_od, hue='y')
    plt.title(c)
    for p in ax.patches:
        x=p.get_bbox().get_points()[:,0]
        y=p.get_bbox().get_points()[1,1]
        ax.annotate('{:.0f}'.format(y), (x.mean(), y), ha='center', va='bottom')


C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  """Entry point for launching an IPython kernel.
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\formatters.py in __call__(self, obj)
    330                 pass
    331             else:
--> 332                 return printer(obj)
    333             # Finally look for special method names
    334             method = get_real_method(obj, self.print_method)

C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\pylabtools.py in <lambda>(fig)
    235 
    236     if 'png' in formats:
--> 237         png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))
    238     if 'retina' in formats or 'png2x' in formats:
    239         png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs))

C:\ProgramData\Anaconda3\lib\site-packages\IPython\core\pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs)
    119 
    120     bytes_io = BytesIO()
--> 121     fig.canvas.print_figure(bytes_io, **kw)
    122     data = bytes_io.getvalue()
    123     if fmt == 'svg':

C:\ProgramData\Anaconda3\lib\site-packages\matplotlib\backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)
   2257                 orientation=orientation,
   2258                 bbox_inches_restore=_bbox_inches_restore,
-> 2259                 **kwargs)
   2260         finally:
   2261             if bbox_inches and restore_bbox:

C:\ProgramData\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs)
    505 
    506     def print_png(self, filename_or_obj, *args, **kwargs):
--> 507         FigureCanvasAgg.draw(self)
    508         renderer = self.get_renderer()
    509         original_dpi = renderer.dpi

C:\ProgramData\Anaconda3\lib\site-packages\matplotlib\backends\backend_agg.py in draw(self)
    428             if toolbar:
    429                 toolbar.set_cursor(cursors.WAIT)
--> 430             self.figure.draw(self.renderer)
    431         finally:
    432             if toolbar:

C:\ProgramData\Anaconda3\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
     53                 renderer.start_filter()
     54 
---> 55             return draw(artist, renderer, *args, **kwargs)
     56         finally:
     57             if artist.get_agg_filter() is not None:

C:\ProgramData\Anaconda3\lib\site-packages\matplotlib\figure.py in draw(self, renderer)
   1293 
   1294             mimage._draw_list_compositing_images(
-> 1295                 renderer, self, artists, self.suppressComposite)
   1296 
   1297             renderer.close_group('figure')

C:\ProgramData\Anaconda3\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
    136     if not_composite or not has_images:
    137         for a in artists:
--> 138             a.draw(renderer)
    139     else:
    140         # Composite any adjacent images together

C:\ProgramData\Anaconda3\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
     53                 renderer.start_filter()
     54 
---> 55             return draw(artist, renderer, *args, **kwargs)
     56         finally:
     57             if artist.get_agg_filter() is not None:

C:\ProgramData\Anaconda3\lib\site-packages\matplotlib\axes\_base.py in draw(self, renderer, inframe)
   2397             renderer.stop_rasterizing()
   2398 
-> 2399         mimage._draw_list_compositing_images(renderer, self, artists)
   2400 
   2401         renderer.close_group('axes')

C:\ProgramData\Anaconda3\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
    136     if not_composite or not has_images:
    137         for a in artists:
--> 138             a.draw(renderer)
    139     else:
    140         # Composite any adjacent images together

C:\ProgramData\Anaconda3\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
     53                 renderer.start_filter()
     54 
---> 55             return draw(artist, renderer, *args, **kwargs)
     56         finally:
     57             if artist.get_agg_filter() is not None:

C:\ProgramData\Anaconda3\lib\site-packages\matplotlib\axis.py in draw(self, renderer, *args, **kwargs)
   1145         self._update_label_position(ticklabelBoxes, ticklabelBoxes2)
   1146 
-> 1147         self.label.draw(renderer)
   1148 
   1149         self._update_offset_text_position(ticklabelBoxes, ticklabelBoxes2)

C:\ProgramData\Anaconda3\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
     53                 renderer.start_filter()
     54 
---> 55             return draw(artist, renderer, *args, **kwargs)
     56         finally:
     57             if artist.get_agg_filter() is not None:

C:\ProgramData\Anaconda3\lib\site-packages\matplotlib\text.py in draw(self, renderer)
    761             posy = float(textobj.convert_yunits(textobj._y))
    762             if not np.isfinite(posx) or not np.isfinite(posy):
--> 763                 raise ValueError("posx and posy should be finite values")
    764             posx, posy = trans.transform_point((posx, posy))
    765             canvasw, canvash = renderer.get_canvas_width_height()

ValueError: posx and posy should be finite values
<matplotlib.figure.Figure at 0x2478a3a6438>

In [64]:
# This way we have randomness and are able to reproduce the behaviour within this cell.
np.random.seed(13)

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

# Apply the encoding to training and test data, and preserve the mapping

X_train_od['y'] = target
impact_coding_map = {}
for f in categorical_features:
    print("Impact coding for {}".format(f))
    X_train_od["impact_encoded_{}".format(f)], impact_coding_mapping, default_coding = impact_coding(X_train_od, f)
    impact_coding_map[f] = (impact_coding_mapping, default_coding)
    mapping, default_mean = impact_coding_map[f]
    X_test_od["impact_encoded_{}".format(f)] = X_test_od.apply(lambda x: mapping[x[f]]
                                                                         if x[f] in mapping
                                                                         else default_mean
                                                               , axis=1)


C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:55: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
Impact coding for city
C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:65: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
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 experience
Impact coding for company_size
Impact coding for company_type
Impact coding for last_new_job

In [67]:
X_train_od.drop(categorical_features, axis=1, inplace=True)
X_test_od.drop(categorical_features, axis=1, inplace=True)
X_train_od.drop('y', axis=1, inplace=True)

In [104]:
X_stack_train, X_stack_test = np.hstack((X_train_num, X_train_od)), np.hstack((X_test_num, X_test_od))

In [105]:
model=CatBoostClassifier(iterations=1024, depth=8, learning_rate=0.06, loss_function= 'Logloss')
model.fit(X_stack_train, target)


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1003:	learn: 0.2051438	total: 1m 14s	remaining: 1.49s
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1009:	learn: 0.2046512	total: 1m 15s	remaining: 1.04s
1010:	learn: 0.2046505	total: 1m 15s	remaining: 966ms
1011:	learn: 0.2045644	total: 1m 15s	remaining: 892ms
1012:	learn: 0.2044870	total: 1m 15s	remaining: 818ms
1013:	learn: 0.2044496	total: 1m 15s	remaining: 743ms
1014:	learn: 0.2044016	total: 1m 15s	remaining: 669ms
1015:	learn: 0.2043694	total: 1m 15s	remaining: 595ms
1016:	learn: 0.2043396	total: 1m 15s	remaining: 520ms
1017:	learn: 0.2042074	total: 1m 15s	remaining: 446ms
1018:	learn: 0.2041256	total: 1m 15s	remaining: 372ms
1019:	learn: 0.2040450	total: 1m 15s	remaining: 298ms
1020:	learn: 0.2040450	total: 1m 15s	remaining: 223ms
1021:	learn: 0.2039529	total: 1m 15s	remaining: 149ms
1022:	learn: 0.2038499	total: 1m 16s	remaining: 74.3ms
1023:	learn: 0.2038017	total: 1m 16s	remaining: 0us
Out[105]:
<catboost.core.CatBoostClassifier at 0x247933b1978>

In [111]:
preds = model.predict_proba(X_stack_test)[:,1]

In [86]:
X_stack_train.shape, X_stack_test.shape


Out[86]:
((18359, 12), (15021, 12))

In [91]:
m = RandomForestClassifier(n_estimators=200,max_features=0.5, n_jobs=-1, oob_score=True)
m.fit(X_stack_train, target)


Out[91]:
RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features=0.5, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=-1,
            oob_score=True, random_state=None, verbose=0, warm_start=False)

In [92]:
preds


Out[92]:
array([ 0.86975,  0.02263,  0.39715, ...,  0.06079,  0.18257,  0.06129])

In [95]:
preds_rf = m.predict_proba(X_stack_test)[:, 1]

In [97]:
import xgboost as xgb
import gc, mlcrate

In [100]:
%%time
N_COMP = 10

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


Start decomposition process...
PCA
Wall time: 652 ms

In [103]:
%%time
print("Append decomposition components to datasets...")

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


Append decomposition components to datasets...
Wall time: 19.5 ms

In [108]:
2425/(2425+15934)


Out[108]:
0.13208780434664197

In [107]:
np.bincount(target)


Out[107]:
array([15934,  2425], dtype=int64)

In [75]:
params = {}
params['booster'] = 'gbtree'
params["objective"] = "binary:logistic"
# params['eval_metric'] = 'logloss'
params['eval_metric'] = 'auc'
params["eta"] = 0.05 #0.03
params["subsample"] = .85 #.85 was tried before
params["silent"] = 0
params['verbose'] = 1
params["max_depth"] = 9
params["seed"] = 1
params["max_delta_step"] = 4
params['scale_pos_weight'] =  0.13208780434664197
params["gamma"] = 1.0 #.5 #.1 #.2
params['colsample_bytree'] = 0.9
params['nrounds'] = 1000 #3600 #2000 #4000 #using lower no for demo
#params['max_leaves'] = 511
#params['verbose_eval'] = 50

In [128]:
submit = make_submission(p_test)
submit.to_csv(f'{PATH}\\AV_Stud_2\\xgb_depth_9.csv', index=False)
submit.head(2)


Out[128]:
enrollee_id target
0 16548 0.127784
1 12036 0.011873

In [76]:
model_xgb, p_train, p_test  = mlcrate.xgb.train_kfold(params, X_stack_train, target, X_stack_test\
                                                       , folds = 7,skip_checks = True, stratify=target, print_imp='final')


[mlcrate] Training 7 stratified XGBoost models on training set (18359, 22) with test set (15021, 22)
[mlcrate] Running fold 0, 15735 train samples, 2624 validation samples
[0]	train-auc:0.579363	valid-auc:0.571273
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:0.585361	valid-auc:0.57603
[2]	train-auc:0.585361	valid-auc:0.57603
[3]	train-auc:0.585361	valid-auc:0.57603
[4]	train-auc:0.594184	valid-auc:0.581385
[5]	train-auc:0.594184	valid-auc:0.581385
[6]	train-auc:0.594184	valid-auc:0.581385
[7]	train-auc:0.594186	valid-auc:0.581443
[8]	train-auc:0.594186	valid-auc:0.581443
[9]	train-auc:0.594191	valid-auc:0.581432
[10]	train-auc:0.594191	valid-auc:0.581432
[11]	train-auc:0.594885	valid-auc:0.582977
[12]	train-auc:0.594902	valid-auc:0.583005
[13]	train-auc:0.595196	valid-auc:0.582943
[14]	train-auc:0.595207	valid-auc:0.582946
[15]	train-auc:0.595226	valid-auc:0.583045
[16]	train-auc:0.649343	valid-auc:0.646431
[17]	train-auc:0.649148	valid-auc:0.646527
[18]	train-auc:0.649129	valid-auc:0.646479
[19]	train-auc:0.648933	valid-auc:0.646183
[20]	train-auc:0.650184	valid-auc:0.648182
[21]	train-auc:0.65012	valid-auc:0.648137
[22]	train-auc:0.650097	valid-auc:0.648063
[23]	train-auc:0.654736	valid-auc:0.651182
[24]	train-auc:0.654643	valid-auc:0.651208
[25]	train-auc:0.65562	valid-auc:0.651559
[26]	train-auc:0.657359	valid-auc:0.65164
[27]	train-auc:0.658165	valid-auc:0.654741
[28]	train-auc:0.659723	valid-auc:0.654604
[29]	train-auc:0.659669	valid-auc:0.655506
[30]	train-auc:0.659772	valid-auc:0.655803
[31]	train-auc:0.660039	valid-auc:0.655461
[32]	train-auc:0.660395	valid-auc:0.655487
[33]	train-auc:0.659682	valid-auc:0.655689
[34]	train-auc:0.659774	valid-auc:0.655906
[35]	train-auc:0.66267	valid-auc:0.658003
[36]	train-auc:0.663068	valid-auc:0.657718
[37]	train-auc:0.663172	valid-auc:0.658027
[38]	train-auc:0.663886	valid-auc:0.658292
[39]	train-auc:0.66439	valid-auc:0.658586
[40]	train-auc:0.665747	valid-auc:0.658787
[41]	train-auc:0.666603	valid-auc:0.658961
[42]	train-auc:0.667311	valid-auc:0.659031
[43]	train-auc:0.667071	valid-auc:0.658953
[44]	train-auc:0.667532	valid-auc:0.658555
[45]	train-auc:0.669817	valid-auc:0.658505
[46]	train-auc:0.670373	valid-auc:0.658959
[47]	train-auc:0.6705	valid-auc:0.658784
[48]	train-auc:0.670936	valid-auc:0.65899
[49]	train-auc:0.671203	valid-auc:0.65852
[50]	train-auc:0.673582	valid-auc:0.663165
[51]	train-auc:0.676313	valid-auc:0.664597
[52]	train-auc:0.677545	valid-auc:0.666452
[53]	train-auc:0.677721	valid-auc:0.666412
[54]	train-auc:0.678805	valid-auc:0.666422
[55]	train-auc:0.678863	valid-auc:0.667165
[56]	train-auc:0.679019	valid-auc:0.666912
[57]	train-auc:0.67972	valid-auc:0.668003
[58]	train-auc:0.681118	valid-auc:0.669511
[59]	train-auc:0.682038	valid-auc:0.669137
[60]	train-auc:0.68285	valid-auc:0.669431
[61]	train-auc:0.683069	valid-auc:0.66958
[62]	train-auc:0.683858	valid-auc:0.669047
[63]	train-auc:0.685733	valid-auc:0.669715
[64]	train-auc:0.686524	valid-auc:0.669852
[65]	train-auc:0.68732	valid-auc:0.66971
[66]	train-auc:0.688247	valid-auc:0.670195
[67]	train-auc:0.690928	valid-auc:0.673913
[68]	train-auc:0.698423	valid-auc:0.672159
[69]	train-auc:0.698847	valid-auc:0.671953
[70]	train-auc:0.699466	valid-auc:0.671252
[71]	train-auc:0.703895	valid-auc:0.670295
[72]	train-auc:0.706417	valid-auc:0.670154
[73]	train-auc:0.709407	valid-auc:0.671212
[74]	train-auc:0.709947	valid-auc:0.672456
[75]	train-auc:0.710429	valid-auc:0.672115
[76]	train-auc:0.711806	valid-auc:0.672142
[77]	train-auc:0.713072	valid-auc:0.671611
[78]	train-auc:0.713086	valid-auc:0.671266
[79]	train-auc:0.713301	valid-auc:0.671382
[80]	train-auc:0.713505	valid-auc:0.67099
[81]	train-auc:0.716542	valid-auc:0.670595
[82]	train-auc:0.718128	valid-auc:0.670908
[83]	train-auc:0.719265	valid-auc:0.670702
[84]	train-auc:0.720825	valid-auc:0.672394
[85]	train-auc:0.722469	valid-auc:0.672938
[86]	train-auc:0.723663	valid-auc:0.673185
[87]	train-auc:0.724702	valid-auc:0.673737
[88]	train-auc:0.725637	valid-auc:0.673826
[89]	train-auc:0.725701	valid-auc:0.673999
[90]	train-auc:0.727236	valid-auc:0.67399
[91]	train-auc:0.729874	valid-auc:0.675717
[92]	train-auc:0.73162	valid-auc:0.676617
[93]	train-auc:0.73438	valid-auc:0.675086
[94]	train-auc:0.735972	valid-auc:0.677114
[95]	train-auc:0.735892	valid-auc:0.676415
[96]	train-auc:0.737478	valid-auc:0.676574
[97]	train-auc:0.73808	valid-auc:0.676108
[98]	train-auc:0.738866	valid-auc:0.675971
[99]	train-auc:0.740181	valid-auc:0.674128
[100]	train-auc:0.740768	valid-auc:0.673742
[101]	train-auc:0.742062	valid-auc:0.673457
[102]	train-auc:0.742793	valid-auc:0.674167
[103]	train-auc:0.743766	valid-auc:0.67354
[104]	train-auc:0.744633	valid-auc:0.673183
[105]	train-auc:0.746553	valid-auc:0.673073
[106]	train-auc:0.748868	valid-auc:0.673401
[107]	train-auc:0.751	valid-auc:0.672642
[108]	train-auc:0.75191	valid-auc:0.672001
[109]	train-auc:0.753374	valid-auc:0.671394
[110]	train-auc:0.755044	valid-auc:0.671928
[111]	train-auc:0.755343	valid-auc:0.671755
[112]	train-auc:0.756461	valid-auc:0.67122
[113]	train-auc:0.758118	valid-auc:0.671288
[114]	train-auc:0.758739	valid-auc:0.671728
[115]	train-auc:0.759854	valid-auc:0.671758
[116]	train-auc:0.760731	valid-auc:0.671352
[117]	train-auc:0.761923	valid-auc:0.671167
[118]	train-auc:0.762582	valid-auc:0.671581
[119]	train-auc:0.763335	valid-auc:0.671108
[120]	train-auc:0.764053	valid-auc:0.671487
[121]	train-auc:0.764195	valid-auc:0.671371
[122]	train-auc:0.765064	valid-auc:0.671341
[123]	train-auc:0.765869	valid-auc:0.671169
[124]	train-auc:0.76684	valid-auc:0.671289
[125]	train-auc:0.767645	valid-auc:0.671084
[126]	train-auc:0.768745	valid-auc:0.67018
[127]	train-auc:0.769566	valid-auc:0.670028
[128]	train-auc:0.770967	valid-auc:0.669805
[129]	train-auc:0.771459	valid-auc:0.670181
[130]	train-auc:0.772441	valid-auc:0.670252
[131]	train-auc:0.773079	valid-auc:0.669117
[132]	train-auc:0.773419	valid-auc:0.669412
[133]	train-auc:0.774734	valid-auc:0.669276
[134]	train-auc:0.775381	valid-auc:0.669102
[135]	train-auc:0.776146	valid-auc:0.668604
[136]	train-auc:0.776665	valid-auc:0.668799
[137]	train-auc:0.77702	valid-auc:0.668831
[138]	train-auc:0.777982	valid-auc:0.66908
[139]	train-auc:0.778248	valid-auc:0.66885
[140]	train-auc:0.779318	valid-auc:0.668875
[141]	train-auc:0.780037	valid-auc:0.668035
[142]	train-auc:0.780472	valid-auc:0.66771
[143]	train-auc:0.780815	valid-auc:0.667737
[144]	train-auc:0.781552	valid-auc:0.667779
Stopping. Best iteration:
[94]	train-auc:0.735972	valid-auc:0.677114

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 7s - running score 0.677114
[mlcrate] Running fold 1, 15735 train samples, 2624 validation samples
[0]	train-auc:0.5	valid-auc:0.5
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:0.5	valid-auc:0.5
[2]	train-auc:0.577177	valid-auc:0.59677
[3]	train-auc:0.579448	valid-auc:0.599072
[4]	train-auc:0.579448	valid-auc:0.599074
[5]	train-auc:0.579448	valid-auc:0.599074
[6]	train-auc:0.595003	valid-auc:0.605398
[7]	train-auc:0.595244	valid-auc:0.606562
[8]	train-auc:0.595244	valid-auc:0.606562
[9]	train-auc:0.595253	valid-auc:0.606838
[10]	train-auc:0.595253	valid-auc:0.606838
[11]	train-auc:0.595228	valid-auc:0.607948
[12]	train-auc:0.595264	valid-auc:0.608207
[13]	train-auc:0.595729	valid-auc:0.608362
[14]	train-auc:0.595737	valid-auc:0.60842
[15]	train-auc:0.595741	valid-auc:0.60841
[16]	train-auc:0.596053	valid-auc:0.608676
[17]	train-auc:0.596063	valid-auc:0.608772
[18]	train-auc:0.596453	valid-auc:0.608815
[19]	train-auc:0.596453	valid-auc:0.608815
[20]	train-auc:0.596583	valid-auc:0.608813
[21]	train-auc:0.598297	valid-auc:0.606795
[22]	train-auc:0.598236	valid-auc:0.606678
[23]	train-auc:0.646166	valid-auc:0.666086
[24]	train-auc:0.64618	valid-auc:0.666288
[25]	train-auc:0.647582	valid-auc:0.668354
[26]	train-auc:0.648738	valid-auc:0.670505
[27]	train-auc:0.648719	valid-auc:0.670343
[28]	train-auc:0.649128	valid-auc:0.670812
[29]	train-auc:0.650453	valid-auc:0.67273
[30]	train-auc:0.653344	valid-auc:0.675567
[31]	train-auc:0.65471	valid-auc:0.676982
[32]	train-auc:0.657395	valid-auc:0.673468
[33]	train-auc:0.658107	valid-auc:0.673753
[34]	train-auc:0.658552	valid-auc:0.673954
[35]	train-auc:0.659692	valid-auc:0.676023
[36]	train-auc:0.662049	valid-auc:0.678025
[37]	train-auc:0.662641	valid-auc:0.679348
[38]	train-auc:0.662466	valid-auc:0.678943
[39]	train-auc:0.662535	valid-auc:0.679733
[40]	train-auc:0.662666	valid-auc:0.679956
[41]	train-auc:0.662762	valid-auc:0.680224
[42]	train-auc:0.664045	valid-auc:0.680234
[43]	train-auc:0.663946	valid-auc:0.679653
[44]	train-auc:0.664861	valid-auc:0.679576
[45]	train-auc:0.666695	valid-auc:0.680234
[46]	train-auc:0.667662	valid-auc:0.680687
[47]	train-auc:0.667702	valid-auc:0.680263
[48]	train-auc:0.668006	valid-auc:0.680328
[49]	train-auc:0.668501	valid-auc:0.680659
[50]	train-auc:0.670655	valid-auc:0.680332
[51]	train-auc:0.670541	valid-auc:0.680517
[52]	train-auc:0.671554	valid-auc:0.680268
[53]	train-auc:0.673326	valid-auc:0.681383
[54]	train-auc:0.674251	valid-auc:0.682114
[55]	train-auc:0.674974	valid-auc:0.681607
[56]	train-auc:0.675101	valid-auc:0.682521
[57]	train-auc:0.676038	valid-auc:0.682483
[58]	train-auc:0.680291	valid-auc:0.685219
[59]	train-auc:0.68094	valid-auc:0.685109
[60]	train-auc:0.683343	valid-auc:0.685218
[61]	train-auc:0.685785	valid-auc:0.687191
[62]	train-auc:0.686255	valid-auc:0.687651
[63]	train-auc:0.687851	valid-auc:0.688167
[64]	train-auc:0.68829	valid-auc:0.688539
[65]	train-auc:0.690828	valid-auc:0.692013
[66]	train-auc:0.69123	valid-auc:0.691648
[67]	train-auc:0.692173	valid-auc:0.691355
[68]	train-auc:0.693052	valid-auc:0.693175
[69]	train-auc:0.694747	valid-auc:0.692542
[70]	train-auc:0.696433	valid-auc:0.692526
[71]	train-auc:0.697965	valid-auc:0.691786
[72]	train-auc:0.698559	valid-auc:0.692319
[73]	train-auc:0.699884	valid-auc:0.692773
[74]	train-auc:0.701125	valid-auc:0.693191
[75]	train-auc:0.703	valid-auc:0.693163
[76]	train-auc:0.70334	valid-auc:0.692874
[77]	train-auc:0.704715	valid-auc:0.693443
[78]	train-auc:0.706902	valid-auc:0.693153
[79]	train-auc:0.708692	valid-auc:0.694483
[80]	train-auc:0.709615	valid-auc:0.694661
[81]	train-auc:0.712187	valid-auc:0.69298
[82]	train-auc:0.712906	valid-auc:0.692812
[83]	train-auc:0.714024	valid-auc:0.693828
[84]	train-auc:0.714875	valid-auc:0.693814
[85]	train-auc:0.71718	valid-auc:0.694842
[86]	train-auc:0.718194	valid-auc:0.694838
[87]	train-auc:0.719846	valid-auc:0.694132
[88]	train-auc:0.720379	valid-auc:0.694744
[89]	train-auc:0.722243	valid-auc:0.695427
[90]	train-auc:0.723651	valid-auc:0.695112
[91]	train-auc:0.724271	valid-auc:0.694892
[92]	train-auc:0.724682	valid-auc:0.695497
[93]	train-auc:0.724819	valid-auc:0.695346
[94]	train-auc:0.726739	valid-auc:0.694958
[95]	train-auc:0.727615	valid-auc:0.694759
[96]	train-auc:0.728421	valid-auc:0.69487
[97]	train-auc:0.73012	valid-auc:0.694498
[98]	train-auc:0.730728	valid-auc:0.694561
[99]	train-auc:0.732711	valid-auc:0.694407
[100]	train-auc:0.734646	valid-auc:0.693637
[101]	train-auc:0.735801	valid-auc:0.693016
[102]	train-auc:0.736716	valid-auc:0.692886
[103]	train-auc:0.737911	valid-auc:0.692991
[104]	train-auc:0.739161	valid-auc:0.693814
[105]	train-auc:0.740049	valid-auc:0.693897
[106]	train-auc:0.740764	valid-auc:0.692737
[107]	train-auc:0.741719	valid-auc:0.69185
[108]	train-auc:0.742272	valid-auc:0.692527
[109]	train-auc:0.743298	valid-auc:0.693188
[110]	train-auc:0.743873	valid-auc:0.69311
[111]	train-auc:0.744341	valid-auc:0.692967
[112]	train-auc:0.745141	valid-auc:0.692755
[113]	train-auc:0.746627	valid-auc:0.692
[114]	train-auc:0.747429	valid-auc:0.692444
[115]	train-auc:0.748162	valid-auc:0.69231
[116]	train-auc:0.749506	valid-auc:0.69215
[117]	train-auc:0.750602	valid-auc:0.691893
[118]	train-auc:0.751863	valid-auc:0.691553
[119]	train-auc:0.752881	valid-auc:0.69064
[120]	train-auc:0.75411	valid-auc:0.691303
[121]	train-auc:0.75493	valid-auc:0.691817
[122]	train-auc:0.755948	valid-auc:0.691372
[123]	train-auc:0.756035	valid-auc:0.691221
[124]	train-auc:0.757646	valid-auc:0.691062
[125]	train-auc:0.758246	valid-auc:0.690811
[126]	train-auc:0.759496	valid-auc:0.691303
[127]	train-auc:0.76104	valid-auc:0.690825
[128]	train-auc:0.762016	valid-auc:0.690469
[129]	train-auc:0.763122	valid-auc:0.690287
[130]	train-auc:0.763502	valid-auc:0.690404
[131]	train-auc:0.764314	valid-auc:0.690608
[132]	train-auc:0.764831	valid-auc:0.690862
[133]	train-auc:0.765468	valid-auc:0.690945
[134]	train-auc:0.766729	valid-auc:0.69182
[135]	train-auc:0.767563	valid-auc:0.692027
[136]	train-auc:0.768154	valid-auc:0.692324
[137]	train-auc:0.769015	valid-auc:0.692307
[138]	train-auc:0.769608	valid-auc:0.692022
[139]	train-auc:0.770426	valid-auc:0.692599
[140]	train-auc:0.771882	valid-auc:0.692917
[141]	train-auc:0.772725	valid-auc:0.692498
[142]	train-auc:0.773182	valid-auc:0.692474
Stopping. Best iteration:
[92]	train-auc:0.724682	valid-auc:0.695497

[mlcrate] Finished training fold 1 - took 7s - running score 0.6863055
[mlcrate] Running fold 2, 15736 train samples, 2623 validation samples
[0]	train-auc:0.5	valid-auc:0.5
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:0.5	valid-auc:0.5
[2]	train-auc:0.575752	valid-auc:0.594618
[3]	train-auc:0.58004	valid-auc:0.597841
[4]	train-auc:0.580224	valid-auc:0.597852
[5]	train-auc:0.586076	valid-auc:0.604532
[6]	train-auc:0.586076	valid-auc:0.604532
[7]	train-auc:0.586196	valid-auc:0.604195
[8]	train-auc:0.586196	valid-auc:0.604195
[9]	train-auc:0.586196	valid-auc:0.604195
[10]	train-auc:0.587541	valid-auc:0.602599
[11]	train-auc:0.587541	valid-auc:0.602599
[12]	train-auc:0.587719	valid-auc:0.602744
[13]	train-auc:0.589489	valid-auc:0.602627
[14]	train-auc:0.589489	valid-auc:0.602627
[15]	train-auc:0.642637	valid-auc:0.651837
[16]	train-auc:0.643049	valid-auc:0.652062
[17]	train-auc:0.642926	valid-auc:0.652072
[18]	train-auc:0.642951	valid-auc:0.652069
[19]	train-auc:0.647811	valid-auc:0.659317
[20]	train-auc:0.647636	valid-auc:0.659411
[21]	train-auc:0.648731	valid-auc:0.658046
[22]	train-auc:0.649539	valid-auc:0.657273
[23]	train-auc:0.650094	valid-auc:0.658795
[24]	train-auc:0.653645	valid-auc:0.670306
[25]	train-auc:0.655794	valid-auc:0.67182
[26]	train-auc:0.655911	valid-auc:0.6709
[27]	train-auc:0.655827	valid-auc:0.670724
[28]	train-auc:0.659211	valid-auc:0.672093
[29]	train-auc:0.659101	valid-auc:0.671909
[30]	train-auc:0.659721	valid-auc:0.671433
[31]	train-auc:0.660945	valid-auc:0.669886
[32]	train-auc:0.661662	valid-auc:0.671902
[33]	train-auc:0.66209	valid-auc:0.67209
[34]	train-auc:0.662352	valid-auc:0.67229
[35]	train-auc:0.662291	valid-auc:0.672323
[36]	train-auc:0.662486	valid-auc:0.673068
[37]	train-auc:0.662594	valid-auc:0.672785
[38]	train-auc:0.664149	valid-auc:0.673563
[39]	train-auc:0.664329	valid-auc:0.673579
[40]	train-auc:0.664461	valid-auc:0.67448
[41]	train-auc:0.66487	valid-auc:0.674474
[42]	train-auc:0.665136	valid-auc:0.673744
[43]	train-auc:0.665499	valid-auc:0.673803
[44]	train-auc:0.665182	valid-auc:0.674248
[45]	train-auc:0.666534	valid-auc:0.674993
[46]	train-auc:0.667214	valid-auc:0.674711
[47]	train-auc:0.667437	valid-auc:0.674629
[48]	train-auc:0.667367	valid-auc:0.67468
[49]	train-auc:0.667595	valid-auc:0.675145
[50]	train-auc:0.669462	valid-auc:0.677406
[51]	train-auc:0.671449	valid-auc:0.677819
[52]	train-auc:0.671346	valid-auc:0.677886
[53]	train-auc:0.672242	valid-auc:0.678481
[54]	train-auc:0.672123	valid-auc:0.678022
[55]	train-auc:0.672818	valid-auc:0.678535
[56]	train-auc:0.673508	valid-auc:0.678776
[57]	train-auc:0.674231	valid-auc:0.678622
[58]	train-auc:0.674516	valid-auc:0.678251
[59]	train-auc:0.678774	valid-auc:0.678912
[60]	train-auc:0.678766	valid-auc:0.679226
[61]	train-auc:0.67979	valid-auc:0.683644
[62]	train-auc:0.685762	valid-auc:0.684377
[63]	train-auc:0.686399	valid-auc:0.684204
[64]	train-auc:0.686663	valid-auc:0.683288
[65]	train-auc:0.689963	valid-auc:0.684216
[66]	train-auc:0.691092	valid-auc:0.684777
[67]	train-auc:0.691347	valid-auc:0.68462
[68]	train-auc:0.695423	valid-auc:0.686399
[69]	train-auc:0.696497	valid-auc:0.686607
[70]	train-auc:0.696684	valid-auc:0.686884
[71]	train-auc:0.699006	valid-auc:0.687105
[72]	train-auc:0.700049	valid-auc:0.687173
[73]	train-auc:0.701102	valid-auc:0.688389
[74]	train-auc:0.701407	valid-auc:0.68847
[75]	train-auc:0.702337	valid-auc:0.688989
[76]	train-auc:0.705491	valid-auc:0.691798
[77]	train-auc:0.706068	valid-auc:0.691208
[78]	train-auc:0.707378	valid-auc:0.691382
[79]	train-auc:0.709776	valid-auc:0.690402
[80]	train-auc:0.711121	valid-auc:0.690464
[81]	train-auc:0.712354	valid-auc:0.691074
[82]	train-auc:0.713532	valid-auc:0.690837
[83]	train-auc:0.715064	valid-auc:0.691401
[84]	train-auc:0.715823	valid-auc:0.691208
[85]	train-auc:0.716135	valid-auc:0.691805
[86]	train-auc:0.717516	valid-auc:0.691148
[87]	train-auc:0.718897	valid-auc:0.691229
[88]	train-auc:0.719224	valid-auc:0.691097
[89]	train-auc:0.720247	valid-auc:0.691269
[90]	train-auc:0.720934	valid-auc:0.691403
[91]	train-auc:0.721594	valid-auc:0.691584
[92]	train-auc:0.722308	valid-auc:0.691593
[93]	train-auc:0.722942	valid-auc:0.691453
[94]	train-auc:0.723632	valid-auc:0.691439
[95]	train-auc:0.725741	valid-auc:0.691067
[96]	train-auc:0.726299	valid-auc:0.691638
[97]	train-auc:0.726782	valid-auc:0.690765
[98]	train-auc:0.727896	valid-auc:0.691601
[99]	train-auc:0.728555	valid-auc:0.691236
[100]	train-auc:0.730601	valid-auc:0.691347
[101]	train-auc:0.730985	valid-auc:0.691636
[102]	train-auc:0.73281	valid-auc:0.691725
[103]	train-auc:0.733751	valid-auc:0.690927
[104]	train-auc:0.734671	valid-auc:0.691848
[105]	train-auc:0.735283	valid-auc:0.692167
[106]	train-auc:0.736182	valid-auc:0.692277
[107]	train-auc:0.737082	valid-auc:0.691995
[108]	train-auc:0.73778	valid-auc:0.692067
[109]	train-auc:0.738588	valid-auc:0.692535
[110]	train-auc:0.740176	valid-auc:0.692539
[111]	train-auc:0.741011	valid-auc:0.693371
[112]	train-auc:0.741772	valid-auc:0.693401
[113]	train-auc:0.742525	valid-auc:0.693436
[114]	train-auc:0.743586	valid-auc:0.693965
[115]	train-auc:0.74424	valid-auc:0.693519
[116]	train-auc:0.745536	valid-auc:0.694383
[117]	train-auc:0.746165	valid-auc:0.694739
[118]	train-auc:0.747842	valid-auc:0.694956
[119]	train-auc:0.748276	valid-auc:0.695031
[120]	train-auc:0.749057	valid-auc:0.69478
[121]	train-auc:0.749811	valid-auc:0.695717
[122]	train-auc:0.75043	valid-auc:0.695115
[123]	train-auc:0.750711	valid-auc:0.694779
[124]	train-auc:0.75221	valid-auc:0.694228
[125]	train-auc:0.753022	valid-auc:0.694023
[126]	train-auc:0.754556	valid-auc:0.694034
[127]	train-auc:0.755686	valid-auc:0.69385
[128]	train-auc:0.75723	valid-auc:0.69319
[129]	train-auc:0.757782	valid-auc:0.693447
[130]	train-auc:0.758187	valid-auc:0.693591
[131]	train-auc:0.759149	valid-auc:0.693545
[132]	train-auc:0.760357	valid-auc:0.693534
[133]	train-auc:0.761961	valid-auc:0.694079
[134]	train-auc:0.762404	valid-auc:0.693403
[135]	train-auc:0.763574	valid-auc:0.693377
[136]	train-auc:0.764196	valid-auc:0.693024
[137]	train-auc:0.7645	valid-auc:0.692834
[138]	train-auc:0.765293	valid-auc:0.693268
[139]	train-auc:0.76549	valid-auc:0.693063
[140]	train-auc:0.766121	valid-auc:0.692716
[141]	train-auc:0.766731	valid-auc:0.693042
[142]	train-auc:0.767641	valid-auc:0.692559
[143]	train-auc:0.768591	valid-auc:0.692114
[144]	train-auc:0.76996	valid-auc:0.692736
[145]	train-auc:0.771284	valid-auc:0.692723
[146]	train-auc:0.771655	valid-auc:0.692572
[147]	train-auc:0.772379	valid-auc:0.691941
[148]	train-auc:0.773109	valid-auc:0.691872
[149]	train-auc:0.773591	valid-auc:0.691819
[150]	train-auc:0.774	valid-auc:0.691568
[151]	train-auc:0.774563	valid-auc:0.691488
[152]	train-auc:0.775204	valid-auc:0.690851
[153]	train-auc:0.775459	valid-auc:0.691102
[154]	train-auc:0.77593	valid-auc:0.691135
[155]	train-auc:0.776787	valid-auc:0.691007
[156]	train-auc:0.777007	valid-auc:0.69147
[157]	train-auc:0.77771	valid-auc:0.691767
[158]	train-auc:0.778518	valid-auc:0.692074
[159]	train-auc:0.779438	valid-auc:0.6916
[160]	train-auc:0.780594	valid-auc:0.692163
[161]	train-auc:0.781592	valid-auc:0.692456
[162]	train-auc:0.782317	valid-auc:0.692594
[163]	train-auc:0.782734	valid-auc:0.692756
[164]	train-auc:0.782907	valid-auc:0.692738
[165]	train-auc:0.783306	valid-auc:0.69261
[166]	train-auc:0.783681	valid-auc:0.691972
[167]	train-auc:0.783729	valid-auc:0.691753
[168]	train-auc:0.784399	valid-auc:0.691691
[169]	train-auc:0.785033	valid-auc:0.691931
[170]	train-auc:0.785271	valid-auc:0.691658
[171]	train-auc:0.787042	valid-auc:0.692065
Stopping. Best iteration:
[121]	train-auc:0.749811	valid-auc:0.695717

[mlcrate] Finished training fold 2 - took 9s - running score 0.6894426666666668
[mlcrate] Running fold 3, 15737 train samples, 2622 validation samples
[0]	train-auc:0.588072	valid-auc:0.559539
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:0.589141	valid-auc:0.560326
[2]	train-auc:0.58914	valid-auc:0.560266
[3]	train-auc:0.589148	valid-auc:0.560268
[4]	train-auc:0.589148	valid-auc:0.560268
[5]	train-auc:0.589148	valid-auc:0.560268
[6]	train-auc:0.589148	valid-auc:0.560268
[7]	train-auc:0.589148	valid-auc:0.560268
[8]	train-auc:0.590097	valid-auc:0.561932
[9]	train-auc:0.593031	valid-auc:0.569522
[10]	train-auc:0.592843	valid-auc:0.57471
[11]	train-auc:0.593049	valid-auc:0.574647
[12]	train-auc:0.593093	valid-auc:0.574652
[13]	train-auc:0.595983	valid-auc:0.576094
[14]	train-auc:0.596087	valid-auc:0.576094
[15]	train-auc:0.596057	valid-auc:0.576094
[16]	train-auc:0.59605	valid-auc:0.576094
[17]	train-auc:0.596006	valid-auc:0.576094
[18]	train-auc:0.616019	valid-auc:0.589628
[19]	train-auc:0.658676	valid-auc:0.622753
[20]	train-auc:0.658705	valid-auc:0.622753
[21]	train-auc:0.658424	valid-auc:0.623466
[22]	train-auc:0.658962	valid-auc:0.623942
[23]	train-auc:0.659274	valid-auc:0.626883
[24]	train-auc:0.659707	valid-auc:0.627418
[25]	train-auc:0.65981	valid-auc:0.627592
[26]	train-auc:0.659502	valid-auc:0.627692
[27]	train-auc:0.661124	valid-auc:0.62539
[28]	train-auc:0.660676	valid-auc:0.625098
[29]	train-auc:0.661964	valid-auc:0.624043
[30]	train-auc:0.663446	valid-auc:0.624771
[31]	train-auc:0.66455	valid-auc:0.624988
[32]	train-auc:0.663791	valid-auc:0.628446
[33]	train-auc:0.666666	valid-auc:0.629283
[34]	train-auc:0.667262	valid-auc:0.628337
[35]	train-auc:0.667547	valid-auc:0.628664
[36]	train-auc:0.66747	valid-auc:0.628394
[37]	train-auc:0.668332	valid-auc:0.627508
[38]	train-auc:0.668858	valid-auc:0.626062
[39]	train-auc:0.669415	valid-auc:0.625675
[40]	train-auc:0.669126	valid-auc:0.625415
[41]	train-auc:0.669267	valid-auc:0.625473
[42]	train-auc:0.669824	valid-auc:0.625506
[43]	train-auc:0.670373	valid-auc:0.626619
[44]	train-auc:0.670668	valid-auc:0.627144
[45]	train-auc:0.671686	valid-auc:0.628315
[46]	train-auc:0.672817	valid-auc:0.62807
[47]	train-auc:0.678175	valid-auc:0.634511
[48]	train-auc:0.679672	valid-auc:0.636167
[49]	train-auc:0.680058	valid-auc:0.63589
[50]	train-auc:0.682068	valid-auc:0.63632
[51]	train-auc:0.682721	valid-auc:0.636039
[52]	train-auc:0.684952	valid-auc:0.639215
[53]	train-auc:0.684977	valid-auc:0.639411
[54]	train-auc:0.686812	valid-auc:0.639445
[55]	train-auc:0.689352	valid-auc:0.639672
[56]	train-auc:0.690192	valid-auc:0.639259
[57]	train-auc:0.690995	valid-auc:0.6393
[58]	train-auc:0.691623	valid-auc:0.638922
[59]	train-auc:0.692889	valid-auc:0.639185
[60]	train-auc:0.693885	valid-auc:0.639937
[61]	train-auc:0.695553	valid-auc:0.635241
[62]	train-auc:0.695802	valid-auc:0.635394
[63]	train-auc:0.69848	valid-auc:0.640526
[64]	train-auc:0.698913	valid-auc:0.639742
[65]	train-auc:0.700357	valid-auc:0.641735
[66]	train-auc:0.700587	valid-auc:0.642159
[67]	train-auc:0.701459	valid-auc:0.642796
[68]	train-auc:0.701992	valid-auc:0.642123
[69]	train-auc:0.702762	valid-auc:0.642066
[70]	train-auc:0.702862	valid-auc:0.642212
[71]	train-auc:0.70376	valid-auc:0.64153
[72]	train-auc:0.704771	valid-auc:0.642904
[73]	train-auc:0.706998	valid-auc:0.643481
[74]	train-auc:0.709577	valid-auc:0.642008
[75]	train-auc:0.71065	valid-auc:0.642098
[76]	train-auc:0.711815	valid-auc:0.641294
[77]	train-auc:0.714167	valid-auc:0.640111
[78]	train-auc:0.714668	valid-auc:0.640421
[79]	train-auc:0.717341	valid-auc:0.641939
[80]	train-auc:0.718695	valid-auc:0.641587
[81]	train-auc:0.719325	valid-auc:0.641733
[82]	train-auc:0.719966	valid-auc:0.642262
[83]	train-auc:0.721654	valid-auc:0.643154
[84]	train-auc:0.722669	valid-auc:0.643295
[85]	train-auc:0.724511	valid-auc:0.642743
[86]	train-auc:0.726738	valid-auc:0.643135
[87]	train-auc:0.727088	valid-auc:0.642389
[88]	train-auc:0.728057	valid-auc:0.642947
[89]	train-auc:0.729158	valid-auc:0.644064
[90]	train-auc:0.729727	valid-auc:0.644632
[91]	train-auc:0.730351	valid-auc:0.644652
[92]	train-auc:0.731951	valid-auc:0.644799
[93]	train-auc:0.733124	valid-auc:0.644048
[94]	train-auc:0.733537	valid-auc:0.643925
[95]	train-auc:0.735502	valid-auc:0.643626
[96]	train-auc:0.736322	valid-auc:0.642631
[97]	train-auc:0.736964	valid-auc:0.642883
[98]	train-auc:0.737475	valid-auc:0.643354
[99]	train-auc:0.738495	valid-auc:0.642982
[100]	train-auc:0.73961	valid-auc:0.643303
[101]	train-auc:0.74048	valid-auc:0.642672
[102]	train-auc:0.741682	valid-auc:0.642236
[103]	train-auc:0.74315	valid-auc:0.642264
[104]	train-auc:0.744273	valid-auc:0.64194
[105]	train-auc:0.745262	valid-auc:0.641375
[106]	train-auc:0.747075	valid-auc:0.641417
[107]	train-auc:0.747717	valid-auc:0.641973
[108]	train-auc:0.749126	valid-auc:0.641444
[109]	train-auc:0.750784	valid-auc:0.641855
[110]	train-auc:0.752136	valid-auc:0.642244
[111]	train-auc:0.753743	valid-auc:0.642648
[112]	train-auc:0.754394	valid-auc:0.64303
[113]	train-auc:0.756517	valid-auc:0.642438
[114]	train-auc:0.757125	valid-auc:0.642845
[115]	train-auc:0.757992	valid-auc:0.642282
[116]	train-auc:0.758772	valid-auc:0.642526
[117]	train-auc:0.759726	valid-auc:0.643247
[118]	train-auc:0.760467	valid-auc:0.643057
[119]	train-auc:0.762109	valid-auc:0.643938
[120]	train-auc:0.763231	valid-auc:0.643622
[121]	train-auc:0.763809	valid-auc:0.643695
[122]	train-auc:0.764351	valid-auc:0.6435
[123]	train-auc:0.764671	valid-auc:0.643595
[124]	train-auc:0.765686	valid-auc:0.642764
[125]	train-auc:0.765945	valid-auc:0.643099
[126]	train-auc:0.766591	valid-auc:0.643171
[127]	train-auc:0.767683	valid-auc:0.643199
[128]	train-auc:0.768604	valid-auc:0.643099
[129]	train-auc:0.769098	valid-auc:0.643797
[130]	train-auc:0.769463	valid-auc:0.644279
[131]	train-auc:0.770606	valid-auc:0.643092
[132]	train-auc:0.771508	valid-auc:0.642991
[133]	train-auc:0.771991	valid-auc:0.643263
[134]	train-auc:0.773423	valid-auc:0.643517
[135]	train-auc:0.77408	valid-auc:0.644681
[136]	train-auc:0.774802	valid-auc:0.644745
[137]	train-auc:0.775372	valid-auc:0.644834
[138]	train-auc:0.776054	valid-auc:0.645069
[139]	train-auc:0.776009	valid-auc:0.645539
[140]	train-auc:0.776221	valid-auc:0.645177
[141]	train-auc:0.778148	valid-auc:0.644807
[142]	train-auc:0.779727	valid-auc:0.645344
[143]	train-auc:0.780698	valid-auc:0.64502
[144]	train-auc:0.781371	valid-auc:0.645005
[145]	train-auc:0.782164	valid-auc:0.645098
[146]	train-auc:0.783133	valid-auc:0.644303
[147]	train-auc:0.783604	valid-auc:0.643674
[148]	train-auc:0.784378	valid-auc:0.643781
[149]	train-auc:0.785622	valid-auc:0.643464
[150]	train-auc:0.786319	valid-auc:0.64337
[151]	train-auc:0.78728	valid-auc:0.643786
[152]	train-auc:0.787864	valid-auc:0.643466
[153]	train-auc:0.788322	valid-auc:0.643601
[154]	train-auc:0.789958	valid-auc:0.643678
[155]	train-auc:0.791645	valid-auc:0.642986
[156]	train-auc:0.792625	valid-auc:0.642977
[157]	train-auc:0.793629	valid-auc:0.64321
[158]	train-auc:0.794009	valid-auc:0.643838
[159]	train-auc:0.79421	valid-auc:0.643799
[160]	train-auc:0.794658	valid-auc:0.643908
[161]	train-auc:0.795598	valid-auc:0.644001
[162]	train-auc:0.796459	valid-auc:0.644341
[163]	train-auc:0.797438	valid-auc:0.643406
[164]	train-auc:0.797532	valid-auc:0.643418
[165]	train-auc:0.79808	valid-auc:0.643112
[166]	train-auc:0.798745	valid-auc:0.64285
[167]	train-auc:0.799452	valid-auc:0.642474
[168]	train-auc:0.799767	valid-auc:0.642185
[169]	train-auc:0.800323	valid-auc:0.641992
[170]	train-auc:0.801209	valid-auc:0.642384
[171]	train-auc:0.801753	valid-auc:0.642261
[172]	train-auc:0.802079	valid-auc:0.64265
[173]	train-auc:0.802839	valid-auc:0.642558
[174]	train-auc:0.803313	valid-auc:0.642898
[175]	train-auc:0.803692	valid-auc:0.642653
[176]	train-auc:0.803968	valid-auc:0.641972
[177]	train-auc:0.804133	valid-auc:0.642493
[178]	train-auc:0.805044	valid-auc:0.642464
[179]	train-auc:0.806052	valid-auc:0.642652
[180]	train-auc:0.806033	valid-auc:0.642538
[181]	train-auc:0.806147	valid-auc:0.642438
[182]	train-auc:0.806452	valid-auc:0.642521
[183]	train-auc:0.807203	valid-auc:0.642529
[184]	train-auc:0.807461	valid-auc:0.642625
[185]	train-auc:0.807713	valid-auc:0.642596
[186]	train-auc:0.80802	valid-auc:0.642318
[187]	train-auc:0.808506	valid-auc:0.642539
[188]	train-auc:0.809058	valid-auc:0.641993
[189]	train-auc:0.809657	valid-auc:0.641709
Stopping. Best iteration:
[139]	train-auc:0.776009	valid-auc:0.645539

[mlcrate] Finished training fold 3 - took 10s - running score 0.67846675
[mlcrate] Running fold 4, 15737 train samples, 2622 validation samples
[0]	train-auc:0.578554	valid-auc:0.576108
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:0.58972	valid-auc:0.582128
[2]	train-auc:0.58972	valid-auc:0.582128
[3]	train-auc:0.58972	valid-auc:0.582128
[4]	train-auc:0.58972	valid-auc:0.582128
[5]	train-auc:0.592651	valid-auc:0.583252
[6]	train-auc:0.592651	valid-auc:0.583252
[7]	train-auc:0.592651	valid-auc:0.583236
[8]	train-auc:0.593445	valid-auc:0.585122
[9]	train-auc:0.593442	valid-auc:0.585134
[10]	train-auc:0.593525	valid-auc:0.584973
[11]	train-auc:0.593525	valid-auc:0.584973
[12]	train-auc:0.593809	valid-auc:0.585245
[13]	train-auc:0.593809	valid-auc:0.585245
[14]	train-auc:0.593809	valid-auc:0.585245
[15]	train-auc:0.593916	valid-auc:0.585306
[16]	train-auc:0.593923	valid-auc:0.585291
[17]	train-auc:0.593923	valid-auc:0.585291
[18]	train-auc:0.593923	valid-auc:0.585291
[19]	train-auc:0.594087	valid-auc:0.585419
[20]	train-auc:0.595186	valid-auc:0.585829
[21]	train-auc:0.595037	valid-auc:0.585194
[22]	train-auc:0.650366	valid-auc:0.635599
[23]	train-auc:0.650906	valid-auc:0.636147
[24]	train-auc:0.651902	valid-auc:0.63606
[25]	train-auc:0.652717	valid-auc:0.636335
[26]	train-auc:0.652794	valid-auc:0.636464
[27]	train-auc:0.660014	valid-auc:0.63732
[28]	train-auc:0.659662	valid-auc:0.637025
[29]	train-auc:0.662675	valid-auc:0.637933
[30]	train-auc:0.663708	valid-auc:0.638384
[31]	train-auc:0.665307	valid-auc:0.639946
[32]	train-auc:0.665999	valid-auc:0.638425
[33]	train-auc:0.666181	valid-auc:0.637669
[34]	train-auc:0.667073	valid-auc:0.63773
[35]	train-auc:0.667618	valid-auc:0.637777
[36]	train-auc:0.668099	valid-auc:0.638397
[37]	train-auc:0.66853	valid-auc:0.639054
[38]	train-auc:0.668623	valid-auc:0.638722
[39]	train-auc:0.670562	valid-auc:0.63797
[40]	train-auc:0.671361	valid-auc:0.636076
[41]	train-auc:0.671739	valid-auc:0.636994
[42]	train-auc:0.672405	valid-auc:0.636623
[43]	train-auc:0.674106	valid-auc:0.636222
[44]	train-auc:0.67504	valid-auc:0.636914
[45]	train-auc:0.676826	valid-auc:0.639284
[46]	train-auc:0.677703	valid-auc:0.638951
[47]	train-auc:0.678844	valid-auc:0.639776
[48]	train-auc:0.679858	valid-auc:0.64057
[49]	train-auc:0.680756	valid-auc:0.641718
[50]	train-auc:0.682523	valid-auc:0.640723
[51]	train-auc:0.68284	valid-auc:0.640051
[52]	train-auc:0.683092	valid-auc:0.640265
[53]	train-auc:0.683597	valid-auc:0.64046
[54]	train-auc:0.683961	valid-auc:0.640142
[55]	train-auc:0.685027	valid-auc:0.640735
[56]	train-auc:0.685766	valid-auc:0.641034
[57]	train-auc:0.689085	valid-auc:0.64267
[58]	train-auc:0.689345	valid-auc:0.642529
[59]	train-auc:0.689814	valid-auc:0.643329
[60]	train-auc:0.690199	valid-auc:0.643717
[61]	train-auc:0.691065	valid-auc:0.64414
[62]	train-auc:0.693272	valid-auc:0.643959
[63]	train-auc:0.693654	valid-auc:0.643736
[64]	train-auc:0.693863	valid-auc:0.641776
[65]	train-auc:0.694161	valid-auc:0.641561
[66]	train-auc:0.694491	valid-auc:0.641142
[67]	train-auc:0.694798	valid-auc:0.642643
[68]	train-auc:0.696644	valid-auc:0.642792
[69]	train-auc:0.699365	valid-auc:0.643529
[70]	train-auc:0.701946	valid-auc:0.647175
[71]	train-auc:0.702346	valid-auc:0.647766
[72]	train-auc:0.703149	valid-auc:0.647658
[73]	train-auc:0.70421	valid-auc:0.647961
[74]	train-auc:0.705236	valid-auc:0.648444
[75]	train-auc:0.706843	valid-auc:0.649148
[76]	train-auc:0.707773	valid-auc:0.649924
[77]	train-auc:0.709872	valid-auc:0.651066
[78]	train-auc:0.710234	valid-auc:0.650873
[79]	train-auc:0.711686	valid-auc:0.65026
[80]	train-auc:0.713741	valid-auc:0.650071
[81]	train-auc:0.71461	valid-auc:0.650986
[82]	train-auc:0.71474	valid-auc:0.651134
[83]	train-auc:0.715225	valid-auc:0.650869
[84]	train-auc:0.715948	valid-auc:0.650779
[85]	train-auc:0.717305	valid-auc:0.651597
[86]	train-auc:0.71901	valid-auc:0.651446
[87]	train-auc:0.719731	valid-auc:0.652089
[88]	train-auc:0.721268	valid-auc:0.65304
[89]	train-auc:0.721851	valid-auc:0.653513
[90]	train-auc:0.723567	valid-auc:0.655273
[91]	train-auc:0.725053	valid-auc:0.654749
[92]	train-auc:0.725906	valid-auc:0.654592
[93]	train-auc:0.726737	valid-auc:0.65598
[94]	train-auc:0.728206	valid-auc:0.656603
[95]	train-auc:0.728981	valid-auc:0.656164
[96]	train-auc:0.729709	valid-auc:0.656389
[97]	train-auc:0.731482	valid-auc:0.657968
[98]	train-auc:0.732759	valid-auc:0.65835
[99]	train-auc:0.733846	valid-auc:0.658252
[100]	train-auc:0.734591	valid-auc:0.658586
[101]	train-auc:0.735616	valid-auc:0.65892
[102]	train-auc:0.737257	valid-auc:0.658852
[103]	train-auc:0.738887	valid-auc:0.658394
[104]	train-auc:0.74054	valid-auc:0.657565
[105]	train-auc:0.741504	valid-auc:0.657256
[106]	train-auc:0.741982	valid-auc:0.656677
[107]	train-auc:0.743337	valid-auc:0.656797
[108]	train-auc:0.74485	valid-auc:0.657462
[109]	train-auc:0.746762	valid-auc:0.657559
[110]	train-auc:0.747322	valid-auc:0.657748
[111]	train-auc:0.748216	valid-auc:0.657757
[112]	train-auc:0.749619	valid-auc:0.657936
[113]	train-auc:0.750594	valid-auc:0.658457
[114]	train-auc:0.751253	valid-auc:0.65862
[115]	train-auc:0.752291	valid-auc:0.659483
[116]	train-auc:0.75278	valid-auc:0.659207
[117]	train-auc:0.754562	valid-auc:0.659543
[118]	train-auc:0.755641	valid-auc:0.659888
[119]	train-auc:0.756093	valid-auc:0.660111
[120]	train-auc:0.757043	valid-auc:0.660193
[121]	train-auc:0.757586	valid-auc:0.660445
[122]	train-auc:0.758622	valid-auc:0.660535
[123]	train-auc:0.759251	valid-auc:0.660482
[124]	train-auc:0.760335	valid-auc:0.660111
[125]	train-auc:0.761475	valid-auc:0.660256
[126]	train-auc:0.762494	valid-auc:0.659836
[127]	train-auc:0.762689	valid-auc:0.659626
[128]	train-auc:0.764308	valid-auc:0.659474
[129]	train-auc:0.76467	valid-auc:0.659514
[130]	train-auc:0.765236	valid-auc:0.659333
[131]	train-auc:0.765502	valid-auc:0.659304
[132]	train-auc:0.766512	valid-auc:0.659655
[133]	train-auc:0.767084	valid-auc:0.659605
[134]	train-auc:0.767599	valid-auc:0.659682
[135]	train-auc:0.767972	valid-auc:0.659794
[136]	train-auc:0.768604	valid-auc:0.660326
[137]	train-auc:0.769244	valid-auc:0.659928
[138]	train-auc:0.769601	valid-auc:0.65984
[139]	train-auc:0.770113	valid-auc:0.659724
[140]	train-auc:0.770973	valid-auc:0.659647
[141]	train-auc:0.771906	valid-auc:0.660488
[142]	train-auc:0.772775	valid-auc:0.66011
[143]	train-auc:0.773494	valid-auc:0.660881
[144]	train-auc:0.774191	valid-auc:0.661098
[145]	train-auc:0.774226	valid-auc:0.661196
[146]	train-auc:0.774811	valid-auc:0.661104
[147]	train-auc:0.775411	valid-auc:0.661427
[148]	train-auc:0.776035	valid-auc:0.661498
[149]	train-auc:0.776404	valid-auc:0.66124
[150]	train-auc:0.777272	valid-auc:0.661404
[151]	train-auc:0.777636	valid-auc:0.661312
[152]	train-auc:0.778436	valid-auc:0.660688
[153]	train-auc:0.779002	valid-auc:0.660582
[154]	train-auc:0.779995	valid-auc:0.660483
[155]	train-auc:0.781248	valid-auc:0.660368
[156]	train-auc:0.782357	valid-auc:0.661167
[157]	train-auc:0.783454	valid-auc:0.660763
[158]	train-auc:0.783981	valid-auc:0.660812
[159]	train-auc:0.784538	valid-auc:0.66088
[160]	train-auc:0.785132	valid-auc:0.66076
[161]	train-auc:0.786538	valid-auc:0.660657
[162]	train-auc:0.786773	valid-auc:0.660472
[163]	train-auc:0.78687	valid-auc:0.660118
[164]	train-auc:0.787236	valid-auc:0.660394
[165]	train-auc:0.787236	valid-auc:0.660394
[166]	train-auc:0.787669	valid-auc:0.660529
[167]	train-auc:0.788661	valid-auc:0.661368
[168]	train-auc:0.788965	valid-auc:0.661334
[169]	train-auc:0.79001	valid-auc:0.660708
[170]	train-auc:0.79001	valid-auc:0.660708
[171]	train-auc:0.790304	valid-auc:0.661091
[172]	train-auc:0.790334	valid-auc:0.661137
[173]	train-auc:0.790714	valid-auc:0.661127
[174]	train-auc:0.791223	valid-auc:0.661532
[175]	train-auc:0.791673	valid-auc:0.66128
[176]	train-auc:0.791715	valid-auc:0.661551
[177]	train-auc:0.792681	valid-auc:0.662092
[178]	train-auc:0.793839	valid-auc:0.661972
[179]	train-auc:0.794287	valid-auc:0.662096
[180]	train-auc:0.794809	valid-auc:0.662224
[181]	train-auc:0.795709	valid-auc:0.662953
[182]	train-auc:0.795816	valid-auc:0.663217
[183]	train-auc:0.796172	valid-auc:0.663046
[184]	train-auc:0.796487	valid-auc:0.663235
[185]	train-auc:0.796678	valid-auc:0.663205
[186]	train-auc:0.797142	valid-auc:0.662483
[187]	train-auc:0.797199	valid-auc:0.662199
[188]	train-auc:0.797473	valid-auc:0.661875
[189]	train-auc:0.797729	valid-auc:0.661935
[190]	train-auc:0.798149	valid-auc:0.661757
[191]	train-auc:0.798651	valid-auc:0.662139
[192]	train-auc:0.799317	valid-auc:0.661502
[193]	train-auc:0.799504	valid-auc:0.66155
[194]	train-auc:0.79983	valid-auc:0.661478
[195]	train-auc:0.800811	valid-auc:0.661374
[196]	train-auc:0.801222	valid-auc:0.661109
[197]	train-auc:0.80213	valid-auc:0.660651
[198]	train-auc:0.80245	valid-auc:0.661087
[199]	train-auc:0.802846	valid-auc:0.660991
[200]	train-auc:0.802863	valid-auc:0.661048
[201]	train-auc:0.803295	valid-auc:0.661045
[202]	train-auc:0.804328	valid-auc:0.660546
[203]	train-auc:0.804465	valid-auc:0.66032
[204]	train-auc:0.804911	valid-auc:0.66053
[205]	train-auc:0.805338	valid-auc:0.66096
[206]	train-auc:0.805992	valid-auc:0.660923
[207]	train-auc:0.807065	valid-auc:0.660212
[208]	train-auc:0.807594	valid-auc:0.660078
[209]	train-auc:0.808067	valid-auc:0.660372
[210]	train-auc:0.808526	valid-auc:0.660293
[211]	train-auc:0.80881	valid-auc:0.660392
[212]	train-auc:0.809097	valid-auc:0.660604
[213]	train-auc:0.809424	valid-auc:0.660687
[214]	train-auc:0.810009	valid-auc:0.660608
[215]	train-auc:0.810009	valid-auc:0.660608
[216]	train-auc:0.810366	valid-auc:0.660949
[217]	train-auc:0.810508	valid-auc:0.661036
[218]	train-auc:0.811139	valid-auc:0.661175
[219]	train-auc:0.811342	valid-auc:0.660986
[220]	train-auc:0.811797	valid-auc:0.661419
[221]	train-auc:0.812351	valid-auc:0.661394
[222]	train-auc:0.812781	valid-auc:0.661705
[223]	train-auc:0.813149	valid-auc:0.662228
[224]	train-auc:0.813401	valid-auc:0.662405
[225]	train-auc:0.813632	valid-auc:0.662195
[226]	train-auc:0.814393	valid-auc:0.662323
[227]	train-auc:0.815249	valid-auc:0.662256
[228]	train-auc:0.81585	valid-auc:0.662437
[229]	train-auc:0.816021	valid-auc:0.662038
[230]	train-auc:0.816823	valid-auc:0.661935
[231]	train-auc:0.81698	valid-auc:0.662377
[232]	train-auc:0.817678	valid-auc:0.661852
[233]	train-auc:0.818488	valid-auc:0.661864
[234]	train-auc:0.818939	valid-auc:0.661916
Stopping. Best iteration:
[184]	train-auc:0.796487	valid-auc:0.663235

[mlcrate] Finished training fold 4 - took 12s - running score 0.6754203999999999
[mlcrate] Running fold 5, 15737 train samples, 2622 validation samples
[0]	train-auc:0.5	valid-auc:0.5
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:0.573992	valid-auc:0.562536
[2]	train-auc:0.575218	valid-auc:0.566533
[3]	train-auc:0.578604	valid-auc:0.577277
[4]	train-auc:0.578604	valid-auc:0.577277
[5]	train-auc:0.578604	valid-auc:0.577277
[6]	train-auc:0.579056	valid-auc:0.576955
[7]	train-auc:0.579056	valid-auc:0.576955
[8]	train-auc:0.582164	valid-auc:0.581819
[9]	train-auc:0.589554	valid-auc:0.592599
[10]	train-auc:0.589809	valid-auc:0.592545
[11]	train-auc:0.589865	valid-auc:0.592485
[12]	train-auc:0.589883	valid-auc:0.592457
[13]	train-auc:0.589883	valid-auc:0.592457
[14]	train-auc:0.58988	valid-auc:0.592448
[15]	train-auc:0.590229	valid-auc:0.592632
[16]	train-auc:0.590229	valid-auc:0.592632
[17]	train-auc:0.590229	valid-auc:0.592632
[18]	train-auc:0.590229	valid-auc:0.592632
[19]	train-auc:0.590213	valid-auc:0.592632
[20]	train-auc:0.611001	valid-auc:0.608715
[21]	train-auc:0.617944	valid-auc:0.612498
[22]	train-auc:0.653867	valid-auc:0.648773
[23]	train-auc:0.655393	valid-auc:0.65029
[24]	train-auc:0.658499	valid-auc:0.651128
[25]	train-auc:0.660191	valid-auc:0.647199
[26]	train-auc:0.660557	valid-auc:0.646818
[27]	train-auc:0.660293	valid-auc:0.647818
[28]	train-auc:0.661015	valid-auc:0.646493
[29]	train-auc:0.66245	valid-auc:0.647064
[30]	train-auc:0.662697	valid-auc:0.646965
[31]	train-auc:0.662773	valid-auc:0.646985
[32]	train-auc:0.663685	valid-auc:0.647517
[33]	train-auc:0.66388	valid-auc:0.648026
[34]	train-auc:0.663883	valid-auc:0.647918
[35]	train-auc:0.663632	valid-auc:0.647984
[36]	train-auc:0.664254	valid-auc:0.648358
[37]	train-auc:0.664748	valid-auc:0.648556
[38]	train-auc:0.666942	valid-auc:0.646737
[39]	train-auc:0.667253	valid-auc:0.646202
[40]	train-auc:0.667389	valid-auc:0.646186
[41]	train-auc:0.667407	valid-auc:0.646168
[42]	train-auc:0.668948	valid-auc:0.646022
[43]	train-auc:0.669864	valid-auc:0.646842
[44]	train-auc:0.670168	valid-auc:0.646749
[45]	train-auc:0.670693	valid-auc:0.646223
[46]	train-auc:0.672517	valid-auc:0.647712
[47]	train-auc:0.672889	valid-auc:0.647664
[48]	train-auc:0.673612	valid-auc:0.647906
[49]	train-auc:0.674055	valid-auc:0.648188
[50]	train-auc:0.674349	valid-auc:0.649006
[51]	train-auc:0.674537	valid-auc:0.649043
[52]	train-auc:0.675882	valid-auc:0.648421
[53]	train-auc:0.676631	valid-auc:0.648194
[54]	train-auc:0.677733	valid-auc:0.648462
[55]	train-auc:0.678782	valid-auc:0.649172
[56]	train-auc:0.679036	valid-auc:0.64958
[57]	train-auc:0.684922	valid-auc:0.659261
[58]	train-auc:0.685249	valid-auc:0.659076
[59]	train-auc:0.685448	valid-auc:0.658995
[60]	train-auc:0.686017	valid-auc:0.660443
[61]	train-auc:0.686222	valid-auc:0.659809
[62]	train-auc:0.687084	valid-auc:0.660293
[63]	train-auc:0.689337	valid-auc:0.659377
[64]	train-auc:0.690008	valid-auc:0.659359
[65]	train-auc:0.689896	valid-auc:0.659135
[66]	train-auc:0.691478	valid-auc:0.661038
[67]	train-auc:0.694442	valid-auc:0.66042
[68]	train-auc:0.695312	valid-auc:0.661681
[69]	train-auc:0.695581	valid-auc:0.662129
[70]	train-auc:0.696703	valid-auc:0.663455
[71]	train-auc:0.698382	valid-auc:0.663708
[72]	train-auc:0.701632	valid-auc:0.664043
[73]	train-auc:0.702694	valid-auc:0.664469
[74]	train-auc:0.703351	valid-auc:0.665628
[75]	train-auc:0.706543	valid-auc:0.66684
[76]	train-auc:0.708204	valid-auc:0.666831
[77]	train-auc:0.709139	valid-auc:0.668131
[78]	train-auc:0.710918	valid-auc:0.668031
[79]	train-auc:0.711473	valid-auc:0.668733
[80]	train-auc:0.711838	valid-auc:0.668938
[81]	train-auc:0.711779	valid-auc:0.668551
[82]	train-auc:0.712933	valid-auc:0.668546
[83]	train-auc:0.713571	valid-auc:0.668088
[84]	train-auc:0.71518	valid-auc:0.668421
[85]	train-auc:0.716481	valid-auc:0.66815
[86]	train-auc:0.717897	valid-auc:0.667581
[87]	train-auc:0.719059	valid-auc:0.668428
[88]	train-auc:0.720359	valid-auc:0.669014
[89]	train-auc:0.721902	valid-auc:0.668579
[90]	train-auc:0.722828	valid-auc:0.66889
[91]	train-auc:0.725128	valid-auc:0.669237
[92]	train-auc:0.727184	valid-auc:0.668515
[93]	train-auc:0.729192	valid-auc:0.668591
[94]	train-auc:0.729068	valid-auc:0.668715
[95]	train-auc:0.730273	valid-auc:0.668558
[96]	train-auc:0.731968	valid-auc:0.668808
[97]	train-auc:0.733166	valid-auc:0.668515
[98]	train-auc:0.735354	valid-auc:0.670285
[99]	train-auc:0.736002	valid-auc:0.670285
[100]	train-auc:0.73734	valid-auc:0.670498
[101]	train-auc:0.73852	valid-auc:0.670645
[102]	train-auc:0.739801	valid-auc:0.670299
[103]	train-auc:0.740464	valid-auc:0.671126
[104]	train-auc:0.740464	valid-auc:0.670931
[105]	train-auc:0.741817	valid-auc:0.671388
[106]	train-auc:0.742827	valid-auc:0.671514
[107]	train-auc:0.745	valid-auc:0.670864
[108]	train-auc:0.746046	valid-auc:0.670678
[109]	train-auc:0.746741	valid-auc:0.670151
[110]	train-auc:0.74722	valid-auc:0.670625
[111]	train-auc:0.748149	valid-auc:0.670303
[112]	train-auc:0.749311	valid-auc:0.671489
[113]	train-auc:0.750769	valid-auc:0.671554
[114]	train-auc:0.751917	valid-auc:0.671527
[115]	train-auc:0.752676	valid-auc:0.671535
[116]	train-auc:0.753755	valid-auc:0.670596
[117]	train-auc:0.754714	valid-auc:0.671094
[118]	train-auc:0.756025	valid-auc:0.671242
[119]	train-auc:0.757086	valid-auc:0.671542
[120]	train-auc:0.757433	valid-auc:0.671701
[121]	train-auc:0.758303	valid-auc:0.671308
[122]	train-auc:0.759304	valid-auc:0.67058
[123]	train-auc:0.760033	valid-auc:0.670174
[124]	train-auc:0.760504	valid-auc:0.669926
[125]	train-auc:0.760762	valid-auc:0.669781
[126]	train-auc:0.761764	valid-auc:0.670263
[127]	train-auc:0.762536	valid-auc:0.670087
[128]	train-auc:0.763507	valid-auc:0.670391
[129]	train-auc:0.764945	valid-auc:0.670518
[130]	train-auc:0.76627	valid-auc:0.671069
[131]	train-auc:0.766761	valid-auc:0.671052
[132]	train-auc:0.767652	valid-auc:0.670979
[133]	train-auc:0.768054	valid-auc:0.671215
[134]	train-auc:0.768605	valid-auc:0.670627
[135]	train-auc:0.769722	valid-auc:0.669737
[136]	train-auc:0.770747	valid-auc:0.669544
[137]	train-auc:0.771162	valid-auc:0.669595
[138]	train-auc:0.771693	valid-auc:0.669682
[139]	train-auc:0.772281	valid-auc:0.668962
[140]	train-auc:0.772926	valid-auc:0.669239
[141]	train-auc:0.773144	valid-auc:0.669546
[142]	train-auc:0.773535	valid-auc:0.668944
[143]	train-auc:0.774373	valid-auc:0.668817
[144]	train-auc:0.774901	valid-auc:0.668849
[145]	train-auc:0.776236	valid-auc:0.669093
[146]	train-auc:0.777404	valid-auc:0.669065
[147]	train-auc:0.778287	valid-auc:0.669313
[148]	train-auc:0.778482	valid-auc:0.669287
[149]	train-auc:0.779305	valid-auc:0.669055
[150]	train-auc:0.779643	valid-auc:0.669092
[151]	train-auc:0.779643	valid-auc:0.669092
[152]	train-auc:0.780175	valid-auc:0.669051
[153]	train-auc:0.780699	valid-auc:0.669553
[154]	train-auc:0.781741	valid-auc:0.670767
[155]	train-auc:0.781849	valid-auc:0.670603
[156]	train-auc:0.782221	valid-auc:0.670364
[157]	train-auc:0.78253	valid-auc:0.670383
[158]	train-auc:0.782735	valid-auc:0.670201
[159]	train-auc:0.78277	valid-auc:0.670139
[160]	train-auc:0.783575	valid-auc:0.669595
[161]	train-auc:0.78456	valid-auc:0.669648
[162]	train-auc:0.784689	valid-auc:0.669685
[163]	train-auc:0.784831	valid-auc:0.669309
[164]	train-auc:0.785492	valid-auc:0.66978
[165]	train-auc:0.785831	valid-auc:0.670582
[166]	train-auc:0.786168	valid-auc:0.669847
[167]	train-auc:0.786459	valid-auc:0.669975
[168]	train-auc:0.786975	valid-auc:0.670334
[169]	train-auc:0.78758	valid-auc:0.669893
[170]	train-auc:0.787765	valid-auc:0.669568
Stopping. Best iteration:
[120]	train-auc:0.757433	valid-auc:0.671701

[mlcrate] Finished training fold 5 - took 9s - running score 0.6748004999999999
[mlcrate] Running fold 6, 15737 train samples, 2622 validation samples
[0]	train-auc:0.588844	valid-auc:0.576753
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:0.590291	valid-auc:0.578684
[2]	train-auc:0.59053	valid-auc:0.578684
[3]	train-auc:0.59053	valid-auc:0.578684
[4]	train-auc:0.59053	valid-auc:0.578684
[5]	train-auc:0.590513	valid-auc:0.578745
[6]	train-auc:0.590513	valid-auc:0.578745
[7]	train-auc:0.590513	valid-auc:0.578745
[8]	train-auc:0.591713	valid-auc:0.578938
[9]	train-auc:0.591713	valid-auc:0.578938
[10]	train-auc:0.592621	valid-auc:0.57733
[11]	train-auc:0.592947	valid-auc:0.57742
[12]	train-auc:0.592947	valid-auc:0.57742
[13]	train-auc:0.592936	valid-auc:0.577423
[14]	train-auc:0.59336	valid-auc:0.576731
[15]	train-auc:0.593337	valid-auc:0.576756
[16]	train-auc:0.593352	valid-auc:0.576798
[17]	train-auc:0.593352	valid-auc:0.576798
[18]	train-auc:0.593352	valid-auc:0.576798
[19]	train-auc:0.593352	valid-auc:0.576798
[20]	train-auc:0.593352	valid-auc:0.576798
[21]	train-auc:0.593385	valid-auc:0.576741
[22]	train-auc:0.599599	valid-auc:0.586543
[23]	train-auc:0.650572	valid-auc:0.645264
[24]	train-auc:0.651839	valid-auc:0.646639
[25]	train-auc:0.651718	valid-auc:0.64654
[26]	train-auc:0.651677	valid-auc:0.644157
[27]	train-auc:0.652953	valid-auc:0.645029
[28]	train-auc:0.653055	valid-auc:0.645507
[29]	train-auc:0.657204	valid-auc:0.642198
[30]	train-auc:0.65726	valid-auc:0.642894
[31]	train-auc:0.659812	valid-auc:0.645295
[32]	train-auc:0.660125	valid-auc:0.646043
[33]	train-auc:0.660683	valid-auc:0.646232
[34]	train-auc:0.661127	valid-auc:0.647015
[35]	train-auc:0.662546	valid-auc:0.647146
[36]	train-auc:0.662714	valid-auc:0.647794
[37]	train-auc:0.662703	valid-auc:0.648215
[38]	train-auc:0.662746	valid-auc:0.648295
[39]	train-auc:0.664757	valid-auc:0.650318
[40]	train-auc:0.665982	valid-auc:0.651223
[41]	train-auc:0.666909	valid-auc:0.651693
[42]	train-auc:0.666697	valid-auc:0.652355
[43]	train-auc:0.667377	valid-auc:0.652205
[44]	train-auc:0.669487	valid-auc:0.650859
[45]	train-auc:0.670016	valid-auc:0.650815
[46]	train-auc:0.670699	valid-auc:0.650541
[47]	train-auc:0.673607	valid-auc:0.654166
[48]	train-auc:0.674813	valid-auc:0.654593
[49]	train-auc:0.675855	valid-auc:0.653518
[50]	train-auc:0.67625	valid-auc:0.654052
[51]	train-auc:0.680162	valid-auc:0.65689
[52]	train-auc:0.680595	valid-auc:0.657688
[53]	train-auc:0.680781	valid-auc:0.658079
[54]	train-auc:0.681356	valid-auc:0.658692
[55]	train-auc:0.681787	valid-auc:0.659636
[56]	train-auc:0.682547	valid-auc:0.659574
[57]	train-auc:0.684104	valid-auc:0.658264
[58]	train-auc:0.684564	valid-auc:0.659582
[59]	train-auc:0.685702	valid-auc:0.66113
[60]	train-auc:0.686962	valid-auc:0.661541
[61]	train-auc:0.688415	valid-auc:0.662134
[62]	train-auc:0.689016	valid-auc:0.661812
[63]	train-auc:0.690023	valid-auc:0.662648
[64]	train-auc:0.690295	valid-auc:0.663292
[65]	train-auc:0.694537	valid-auc:0.66351
[66]	train-auc:0.697061	valid-auc:0.667468
[67]	train-auc:0.697653	valid-auc:0.666903
[68]	train-auc:0.698527	valid-auc:0.667214
[69]	train-auc:0.698916	valid-auc:0.666898
[70]	train-auc:0.699935	valid-auc:0.666829
[71]	train-auc:0.700832	valid-auc:0.667038
[72]	train-auc:0.701199	valid-auc:0.6675
[73]	train-auc:0.702365	valid-auc:0.666659
[74]	train-auc:0.703263	valid-auc:0.666565
[75]	train-auc:0.705657	valid-auc:0.666432
[76]	train-auc:0.708151	valid-auc:0.667774
[77]	train-auc:0.709786	valid-auc:0.6688
[78]	train-auc:0.710425	valid-auc:0.6684
[79]	train-auc:0.711636	valid-auc:0.668359
[80]	train-auc:0.712998	valid-auc:0.668687
[81]	train-auc:0.71319	valid-auc:0.668078
[82]	train-auc:0.7147	valid-auc:0.668055
[83]	train-auc:0.715222	valid-auc:0.668271
[84]	train-auc:0.71647	valid-auc:0.668654
[85]	train-auc:0.71705	valid-auc:0.668438
[86]	train-auc:0.720492	valid-auc:0.668934
[87]	train-auc:0.722495	valid-auc:0.668593
[88]	train-auc:0.723986	valid-auc:0.669398
[89]	train-auc:0.724872	valid-auc:0.669198
[90]	train-auc:0.725471	valid-auc:0.669055
[91]	train-auc:0.726861	valid-auc:0.669222
[92]	train-auc:0.728274	valid-auc:0.669219
[93]	train-auc:0.729392	valid-auc:0.668924
[94]	train-auc:0.729755	valid-auc:0.669694
[95]	train-auc:0.7301	valid-auc:0.670262
[96]	train-auc:0.731624	valid-auc:0.66989
[97]	train-auc:0.73226	valid-auc:0.669704
[98]	train-auc:0.73301	valid-auc:0.670523
[99]	train-auc:0.734441	valid-auc:0.671057
[100]	train-auc:0.735147	valid-auc:0.670824
[101]	train-auc:0.735505	valid-auc:0.670802
[102]	train-auc:0.735836	valid-auc:0.671033
[103]	train-auc:0.737812	valid-auc:0.672015
[104]	train-auc:0.739671	valid-auc:0.672506
[105]	train-auc:0.740482	valid-auc:0.672177
[106]	train-auc:0.742494	valid-auc:0.672376
[107]	train-auc:0.743183	valid-auc:0.673418
[108]	train-auc:0.744373	valid-auc:0.673316
[109]	train-auc:0.74536	valid-auc:0.672711
[110]	train-auc:0.746313	valid-auc:0.67169
[111]	train-auc:0.747061	valid-auc:0.671674
[112]	train-auc:0.748761	valid-auc:0.6712
[113]	train-auc:0.749531	valid-auc:0.671098
[114]	train-auc:0.751105	valid-auc:0.671871
[115]	train-auc:0.751763	valid-auc:0.672114
[116]	train-auc:0.75324	valid-auc:0.672161
[117]	train-auc:0.753725	valid-auc:0.672728
[118]	train-auc:0.755265	valid-auc:0.673285
[119]	train-auc:0.755936	valid-auc:0.673188
[120]	train-auc:0.75645	valid-auc:0.673046
[121]	train-auc:0.756616	valid-auc:0.673329
[122]	train-auc:0.757284	valid-auc:0.673304
[123]	train-auc:0.758072	valid-auc:0.674228
[124]	train-auc:0.759116	valid-auc:0.673843
[125]	train-auc:0.761376	valid-auc:0.673873
[126]	train-auc:0.761823	valid-auc:0.67369
[127]	train-auc:0.762774	valid-auc:0.672934
[128]	train-auc:0.763254	valid-auc:0.672765
[129]	train-auc:0.764045	valid-auc:0.67258
[130]	train-auc:0.764916	valid-auc:0.672735
[131]	train-auc:0.765288	valid-auc:0.672774
[132]	train-auc:0.766328	valid-auc:0.673051
[133]	train-auc:0.767251	valid-auc:0.673565
[134]	train-auc:0.767515	valid-auc:0.673701
[135]	train-auc:0.768305	valid-auc:0.673441
[136]	train-auc:0.768598	valid-auc:0.673348
[137]	train-auc:0.770205	valid-auc:0.67228
[138]	train-auc:0.770575	valid-auc:0.671955
[139]	train-auc:0.771698	valid-auc:0.671574
[140]	train-auc:0.772552	valid-auc:0.671494
[141]	train-auc:0.773177	valid-auc:0.671578
[142]	train-auc:0.774634	valid-auc:0.671776
[143]	train-auc:0.775467	valid-auc:0.671285
[144]	train-auc:0.776359	valid-auc:0.671394
[145]	train-auc:0.776608	valid-auc:0.671193
[146]	train-auc:0.77708	valid-auc:0.671643
[147]	train-auc:0.777585	valid-auc:0.67219
[148]	train-auc:0.777962	valid-auc:0.67232
[149]	train-auc:0.778213	valid-auc:0.67246
[150]	train-auc:0.7789	valid-auc:0.67232
[151]	train-auc:0.77925	valid-auc:0.672751
[152]	train-auc:0.779777	valid-auc:0.672924
[153]	train-auc:0.780014	valid-auc:0.67267
[154]	train-auc:0.780155	valid-auc:0.672635
[155]	train-auc:0.780991	valid-auc:0.672716
[156]	train-auc:0.781856	valid-auc:0.672727
[157]	train-auc:0.783054	valid-auc:0.673208
[158]	train-auc:0.783597	valid-auc:0.673254
[159]	train-auc:0.784726	valid-auc:0.673841
[160]	train-auc:0.784988	valid-auc:0.673973
[161]	train-auc:0.784988	valid-auc:0.673973
[162]	train-auc:0.785711	valid-auc:0.673705
[163]	train-auc:0.786167	valid-auc:0.673988
[164]	train-auc:0.786328	valid-auc:0.674137
[165]	train-auc:0.786914	valid-auc:0.674304
[166]	train-auc:0.78722	valid-auc:0.674064
[167]	train-auc:0.787445	valid-auc:0.674268
[168]	train-auc:0.788025	valid-auc:0.674367
[169]	train-auc:0.788059	valid-auc:0.674493
[170]	train-auc:0.788534	valid-auc:0.674351
[171]	train-auc:0.788625	valid-auc:0.674515
[172]	train-auc:0.78866	valid-auc:0.674373
[173]	train-auc:0.788955	valid-auc:0.674569
[174]	train-auc:0.789358	valid-auc:0.674695
[175]	train-auc:0.789688	valid-auc:0.674641
[176]	train-auc:0.790137	valid-auc:0.674359
[177]	train-auc:0.790774	valid-auc:0.674607
[178]	train-auc:0.791265	valid-auc:0.674195
[179]	train-auc:0.792048	valid-auc:0.674153
[180]	train-auc:0.792857	valid-auc:0.673861
[181]	train-auc:0.792957	valid-auc:0.673899
[182]	train-auc:0.79341	valid-auc:0.673635
[183]	train-auc:0.794358	valid-auc:0.673344
[184]	train-auc:0.794696	valid-auc:0.673103
[185]	train-auc:0.794849	valid-auc:0.672857
[186]	train-auc:0.795084	valid-auc:0.672736
[187]	train-auc:0.795395	valid-auc:0.672907
[188]	train-auc:0.796057	valid-auc:0.672983
[189]	train-auc:0.797045	valid-auc:0.672822
[190]	train-auc:0.797234	valid-auc:0.672595
[191]	train-auc:0.797848	valid-auc:0.672182
[192]	train-auc:0.798177	valid-auc:0.672254
[193]	train-auc:0.798736	valid-auc:0.672406
[194]	train-auc:0.798804	valid-auc:0.672627
[195]	train-auc:0.799377	valid-auc:0.672825
[196]	train-auc:0.799658	valid-auc:0.672914
[197]	train-auc:0.799658	valid-auc:0.672914
[198]	train-auc:0.8	valid-auc:0.672615
[199]	train-auc:0.800275	valid-auc:0.67251
[200]	train-auc:0.80064	valid-auc:0.672739
[201]	train-auc:0.800851	valid-auc:0.672212
[202]	train-auc:0.802049	valid-auc:0.672129
[203]	train-auc:0.802411	valid-auc:0.672129
[204]	train-auc:0.802504	valid-auc:0.6721
[205]	train-auc:0.802915	valid-auc:0.671583
[206]	train-auc:0.803501	valid-auc:0.671944
[207]	train-auc:0.803951	valid-auc:0.671823
[208]	train-auc:0.805028	valid-auc:0.671915
[209]	train-auc:0.805278	valid-auc:0.671923
[210]	train-auc:0.805278	valid-auc:0.671923
[211]	train-auc:0.80587	valid-auc:0.6717
[212]	train-auc:0.806136	valid-auc:0.6716
[213]	train-auc:0.80629	valid-auc:0.671172
[214]	train-auc:0.807284	valid-auc:0.670428
[215]	train-auc:0.807565	valid-auc:0.670326
[216]	train-auc:0.808305	valid-auc:0.670339
[217]	train-auc:0.808641	valid-auc:0.669823
[218]	train-auc:0.808849	valid-auc:0.669915
[219]	train-auc:0.809295	valid-auc:0.669754
[220]	train-auc:0.809323	valid-auc:0.669796
[221]	train-auc:0.809667	valid-auc:0.669819
[222]	train-auc:0.810262	valid-auc:0.669704
[223]	train-auc:0.810759	valid-auc:0.669639
[224]	train-auc:0.811151	valid-auc:0.66984
Stopping. Best iteration:
[174]	train-auc:0.789358	valid-auc:0.674695

[mlcrate] Finished training fold 6 - took 12s - running score 0.6747854285714284
[mlcrate] Finished training 7 XGBoost models, took 1m09s

In [11]:
xgb.plot_importance(model_xgb[0],max_num_features=12)


Out[11]:
<matplotlib.axes._subplots.AxesSubplot at 0x21c4d6aa2b0>

In [133]:
np.save(f'{PATH}\\AV_Stud_2\\train_67.npy', X_stack_train)
np.save(f'{PATH}\\AV_Stud_2\\test_67.npy', X_stack_test)
np.save(f'{PATH}\\AV_Stud_2\\target.npy', target)

9th July Night


In [7]:
X_stack_train = np.load(f'{PATH}\\AV_Stud_2\\train_67.npy')
X_stack_test = np.load(f'{PATH}\\AV_Stud_2\\test_67.npy')
target = np.load(f'{PATH}\\AV_Stud_2\\target.npy')

In [20]:
clf_ada = AdaBoostClassifier(n_estimators=100, learning_rate= 0.05)

In [21]:
clf_ada.fit(X_stack_train, target)


Out[21]:
AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
          learning_rate=0.05, n_estimators=100, random_state=None)

In [51]:
preds = clf_ada.predict_proba(X_stack_test)[:, 1]

In [79]:
submit = make_submission(p_test)
submit.to_csv(f'{PATH}\\AV_Stud_2\\stacked_sklearn_showdown.csv', index=False)
submit.head(2)


Out[79]:
enrollee_id target
0 16548 0.219328
1 12036 0.026984

In [53]:
stack_test = pd.DataFrame()
stack_train = pd.DataFrame()
log_cols=["Classifier", "Accuracy"]
log = pd.DataFrame(columns=log_cols)

In [54]:
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 [80]:
classifiers = [
#     GradientBoostingClassifier(max_depth=10,subsample=0.8,max_features='auto'),
#     MLPClassifier(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)
    model
    ]

# 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(stack_train, target, clf, stack_test, 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)


============================================================ CatBoostClassifier0
Training 7 stratified models on training set (18359, 10) with test set (15021, 10)
Running fold 0, 15735 train samples, 2624 validation samples
0:	learn: 0.6481851	total: 64.9ms	remaining: 1m 6s
1:	learn: 0.6094124	total: 121ms	remaining: 1m 1s
2:	learn: 0.5760035	total: 187ms	remaining: 1m 3s
3:	learn: 0.5472214	total: 241ms	remaining: 1m 1s
4:	learn: 0.5225398	total: 294ms	remaining: 59.7s
5:	learn: 0.5012622	total: 347ms	remaining: 58.7s
6:	learn: 0.4828539	total: 400ms	remaining: 57.9s
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8:	learn: 0.4533647	total: 508ms	remaining: 57.1s
9:	learn: 0.4415375	total: 562ms	remaining: 56.8s
10:	learn: 0.4314057	total: 615ms	remaining: 56.4s
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Finished training fold 0 - running score 0.8616615853658537 
Running fold 1, 15735 train samples, 2624 validation samples
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Finished training fold 1 - running score 0.8608993902439024 
Running fold 2, 15736 train samples, 2623 validation samples
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