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
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'
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
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))
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)
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_
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
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)
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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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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
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[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
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[80] train-auc:0.713505 valid-auc:0.67099
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[83] train-auc:0.719265 valid-auc:0.670702
[84] train-auc:0.720825 valid-auc:0.672394
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[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
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[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
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[165] train-auc:0.786914 valid-auc:0.674304
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[196] train-auc:0.799658 valid-auc:0.672914
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[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
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[207] train-auc:0.803951 valid-auc:0.671823
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[211] train-auc:0.80587 valid-auc:0.6717
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[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
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[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)
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
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