In [1]:
%matplotlib inline
from elasticsearch import Elasticsearch
from elasticsearch.helpers import scan
import numpy as np
import pandas as pd
import random
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import AdaBoostClassifier
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import roc_curve, auc
from sklearn import tree
from sklearn.metrics import roc_curve, auc
from pandas.tseries.offsets import *
from graphviz import Source
In [2]:
n_series = 20
start_date = '2017-08-01 00:00:00'
end_date = '2017-08-07 23:59:59'
# tuning parameters
cut = 0.75
ref = 48 * Hour()
sub = 1 * Hour()
sS='CERN-PROD'
srcSiteOWDServer = "128.142.223.247"
dS='pic'
destSiteOWDServer = "193.109.172.188"
In [3]:
es = Elasticsearch(['atlas-kibana.mwt2.org:9200'],timeout=60)
indices = "network_weather-2017.8.*"
start = pd.Timestamp(start_date)
end = pd.Timestamp(end_date)
my_query = {
'query': {
'bool':{
'must':[
{'range': {'timestamp': {'gte': start.strftime('%Y%m%dT%H%M00Z'), 'lt': end.strftime('%Y%m%dT%H%M00Z')}}},
{'term': {'src': srcSiteOWDServer}},
{'term': {'_type': 'packet_loss_rate'}}
]
}
}
}
scroll = list(scan(client=es, index=indices, query=my_query))
In [4]:
count = 0
allData={} # will be like this: {'dest_host':[[timestamp],[value]], ...}
for res in scroll:
# if count<2: print(res)
if not count%100000: print(count)
if count>1000000: break
dst = res['_source']['dest_host']
if dst not in allData: allData[dst]=[[],[]]
allData[dst][0].append(res['_source']['timestamp'] )
allData[dst][1].append(res['_source']['packet_loss'])
count=count+1
dfs=[]
for dest,data in allData.items():
ts=pd.to_datetime(data[0],unit='ms')
df=pd.DataFrame({dest:data[1]}, index=ts )
df.sort_index(inplace=True)
df.index = df.index.map(lambda t: t.replace(second=0))
df = df[~df.index.duplicated(keep='last')]
dfs.append(df)
#print(df.head(2))
0
100000
200000
300000
400000
500000
600000
700000
800000
900000
1000000
In [5]:
full_df = pd.concat(dfs, axis=1)
In [6]:
print(full_df.shape)
# full_df.head()
#print(full_df.columns )
(9809, 86)
In [7]:
full_df.iloc[:,0:n_series].plot(figsize=(20,7))
Out[7]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f3180479a58>
In [8]:
def check_for_anomaly(ref, sub):
y_ref = pd.Series([0] * ref.shape[0])
X_ref = ref
y_sub = pd.Series([1] * sub.shape[0])
X_sub = sub
# separate Reference and Subject into Train and Test
X_ref_train, X_ref_test, y_ref_train, y_ref_test = train_test_split(X_ref, y_ref, test_size=0.3, random_state=42)
X_sub_train, X_sub_test, y_sub_train, y_sub_test = train_test_split(X_sub, y_sub, test_size=0.3, random_state=42)
# combine training ref and sub samples
X_train = pd.concat([X_ref_train, X_sub_train])
y_train = pd.concat([y_ref_train, y_sub_train])
# combine testing ref and sub samples
X_test = pd.concat([X_ref_test, X_sub_test])
y_test = pd.concat([y_ref_test, y_sub_test])
# dtc=DecisionTreeClassifier()
clf = AdaBoostClassifier() #dtc
# clf = AdaBoostClassifier(DecisionTreeClassifier(max_depth=1),algorithm="SAMME",n_estimators=200)
#train an AdaBoost model to be able to tell the difference between the reference and subject data
# with pd.option_context('display.max_rows', 10000, 'display.max_columns', 10):
# print(X_train)
clf.fit(X_train, y_train)
#Predict using the combined test data
y_predict = clf.predict(X_test)
# scores = cross_val_score(clf, X, y)
# print(scores)
fpr, tpr, thresholds = roc_curve(y_test, y_predict) # calculate the false positive rate and true positive rate
auc_score = auc(fpr, tpr) #calculate the AUC score
print ("auc_score = ", auc_score, "\tfeature importances:", clf.feature_importances_)
if auc_score > cut:
plot_roc(fpr, tpr, auc_score)
# filename='tree_'+sub.index.min().strftime("%Y-%m-%d_%H")
# tree.export_graphviz(clf.estimators_[0] , out_file=filename +'_1.dot')
# tree.export_graphviz(clf.estimators_[1] , out_file=filename +'_2.dot')
return auc_score
In [9]:
def plot_roc(fpr,tpr, roc_auc):
plt.figure()
plt.plot(fpr, tpr, color='darkorange', label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], color='navy', linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.plot([0, 1], [0, 1], linestyle='--', color='r',label='Luck', alpha=.8)
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic')
plt.legend(loc="lower right")
plt.show()
In [10]:
# full_df = full_df.interpolate(method='time', axis=0) #these don't work for some reason...
# full_df.interpolate(method='nearest', axis=0, inplace=True)
full_df.fillna(0, inplace=True)
In [11]:
df = full_df#.iloc[:,0:n_series]
auc_df = pd.DataFrame(np.nan, index=df.index, columns=['auc_score'])
In [12]:
#find min and max timestamps
lstart = df.index.min()
lend = df.index.max()
#round start
lstart.seconds=0
lstart.minutes=0
# loop over them
ti = lstart + ref + sub
count = 0
while ti < lend + 1 * Minute():
print(count)
ref_start = ti-ref-sub
ref_end = ti-sub
ref_df = df[(df.index >= ref_start) & (df.index < ref_end)]
sub_df = df[(df.index >= ref_end) & (df.index < ti)]
auc_score = check_for_anomaly(ref_df, sub_df)
auc_df.loc[(auc_df.index >= ref_end) & (auc_df.index < ti), ['auc_score']] = auc_score
print(ti,"\trefes:" , ref_df.shape, "\tsubjects:", sub_df.shape, '\tauc:', auc_score)
ti = ti + sub
count = count + 1
#if count>2: break
0
auc_score = 0.498817966903 feature importances: [ 0. 0. 0.02 0. 0. 0. 0. 0.04 0. 0.02 0. 0.02
0.02 0.02 0. 0. 0. 0.06 0.04 0. 0. 0. 0. 0.02
0. 0. 0. 0.02 0. 0.02 0. 0. 0.06 0. 0. 0.
0.02 0. 0.02 0. 0.02 0. 0. 0.02 0. 0. 0. 0. 0.
0. 0.12 0.02 0.04 0.02 0. 0. 0.1 0.02 0. 0. 0.
0.02 0. 0.02 0.02 0. 0.02 0. 0. 0.02 0. 0. 0.
0.02 0. 0. 0. 0. 0. 0.02 0.04 0. 0.02 0.02 0.
0.02]
2017-08-03 01:00:00 refes: (2820, 86) subjects: (60, 86) auc: 0.498817966903
1
auc_score = 0.526595744681 feature importances: [ 0. 0. 0.02 0. 0. 0.02 0. 0.1 0. 0.02 0. 0.02
0. 0.02 0. 0. 0. 0.06 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0.02 0. 0. 0. 0. 0. 0. 0.02
0. 0. 0. 0. 0. 0. 0.02 0. 0. 0. 0. 0.02
0.02 0.12 0.06 0.04 0.02 0. 0. 0.06 0.04 0.02 0. 0. 0.
0. 0.02 0. 0. 0.02 0. 0. 0.02 0.02 0. 0. 0.02
0. 0. 0. 0. 0. 0.02 0.06 0.02 0.02 0. 0. 0.02]
2017-08-03 02:00:00 refes: (2820, 86) subjects: (60, 86) auc: 0.526595744681
2
auc_score = 0.552600472813 feature importances: [ 0. 0. 0.06 0. 0. 0.02 0. 0.06 0.02 0. 0. 0. 0.
0.02 0. 0. 0. 0.04 0.04 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0.02 0. 0. 0.02 0. 0. 0. 0.02 0.
0.02 0. 0.02 0. 0. 0.02 0. 0.02 0. 0. 0.02 0.
0.02 0.04 0.12 0.02 0. 0. 0.1 0.06 0.02 0. 0. 0. 0.
0.02 0. 0. 0.02 0. 0. 0.02 0. 0. 0. 0.02 0.
0.02 0. 0. 0.02 0.02 0.02 0. 0. 0.02 0. 0. ]
2017-08-03 03:00:00 refes: (2820, 86) subjects: (60, 86) auc: 0.552600472813
3
auc_score = 0.68853427896 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.02 0.02 0.02 0. 0.14
0. 0.02 0. 0. 0. 0.08 0.04 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.02 0. 0. 0. 0. 0.02 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0. 0. 0. 0. 0.02 0.
0.14 0.04 0.06 0.02 0. 0. 0.04 0.04 0. 0. 0. 0. 0.
0.02 0.02 0. 0.02 0.02 0. 0.02 0. 0. 0. 0.02 0. 0.
0. 0. 0.02 0. 0.02 0.02 0.02 0. 0. 0.02]
2017-08-03 04:00:00 refes: (2820, 86) subjects: (60, 86) auc: 0.68853427896
4
auc_score = 0.580969267139 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.02 0.02 0.02 0. 0.1 0.
0.02 0. 0. 0. 0.1 0.02 0. 0. 0. 0. 0.02 0. 0.
0. 0. 0. 0.1 0. 0. 0. 0. 0. 0. 0.02 0.
0.02 0. 0.02 0. 0. 0. 0. 0.02 0. 0. 0. 0.
0.18 0. 0.06 0.02 0. 0. 0.1 0.04 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.02 0. 0.
0. 0. 0. 0. 0.02 0.02 0. 0. 0. 0.02]
2017-08-03 05:00:00 refes: (2820, 86) subjects: (60, 86) auc: 0.580969267139
5
auc_score = 0.664302600473 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.06 0. 0.02 0. 0.06
0.02 0.06 0. 0. 0. 0. 0.04 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0.04 0. 0. 0. 0.02 0. 0. 0.02
0. 0.02 0. 0. 0. 0. 0.02 0. 0.04 0. 0. 0. 0.
0.06 0.02 0.06 0.02 0. 0. 0.14 0.02 0. 0. 0. 0. 0.
0.02 0.02 0. 0.02 0. 0. 0.02 0. 0. 0. 0.02 0. 0.
0. 0. 0.02 0.02 0.02 0.02 0.02 0. 0. 0.02]
2017-08-03 06:00:00 refes: (2820, 86) subjects: (60, 86) auc: 0.664302600473
6
auc_score = 0.604609929078 feature importances: [ 0. 0. 0.02 0. 0. 0. 0. 0.02 0.02 0. 0. 0.06
0.02 0.04 0. 0. 0. 0.06 0. 0.02 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.02 0. 0. 0. 0.02 0. 0. 0.02
0. 0.02 0. 0.02 0. 0. 0.02 0. 0. 0. 0. 0.02
0. 0.22 0. 0.02 0. 0. 0. 0.02 0.02 0. 0. 0.
0.02 0. 0.02 0.02 0. 0.02 0.02 0. 0.02 0. 0.02 0.
0.02 0. 0. 0. 0. 0.02 0.02 0.04 0.02 0.04 0. 0.
0.02]
2017-08-03 07:00:00 refes: (2820, 86) subjects: (60, 86) auc: 0.604609929078
7
auc_score = 0.663711583924 feature importances: [ 0. 0. 0. 0. 0. 0.02 0.02 0.04 0. 0.02 0. 0.1 0.
0. 0. 0. 0. 0.04 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.02 0. 0. 0. 0. 0. 0. 0.02 0. 0.2
0. 0. 0. 0. 0. 0. 0.04 0. 0. 0. 0. 0.12
0.02 0.06 0.04 0. 0. 0.02 0. 0. 0. 0. 0. 0.
0.04 0. 0. 0.04 0. 0. 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.04 0. 0.02 0.04 0. 0. ]
2017-08-03 08:00:00 refes: (2820, 86) subjects: (60, 86) auc: 0.663711583924
8
auc_score = 0.718676122931 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0. 0. 0.02 0. 0.04
0. 0.02 0. 0. 0. 0.14 0.04 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0.02 0. 0. 0. 0. 0.02 0. 0.02
0. 0.06 0. 0. 0. 0. 0. 0. 0.04 0. 0. 0. 0.
0.02 0. 0.06 0.02 0. 0. 0.02 0.04 0. 0. 0. 0. 0.
0.02 0. 0. 0.2 0. 0. 0.02 0.02 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.02 0.02 0.02 0.06 0. 0. ]
2017-08-03 09:00:00 refes: (2820, 86) subjects: (60, 86) auc: 0.718676122931
9
auc_score = 0.63475177305 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.1 0.02 0.04 0. 0.02
0. 0.06 0. 0. 0. 0.06 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0.04 0. 0.02 0. 0.02
0. 0.08 0. 0.02 0. 0. 0. 0. 0.06 0. 0.02 0. 0.
0.04 0. 0.02 0.02 0. 0. 0.04 0.08 0. 0. 0. 0.02
0. 0. 0. 0. 0.12 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0.02 0.02 0. 0. 0. 0.02]
2017-08-03 10:00:00 refes: (2820, 86) subjects: (60, 86) auc: 0.63475177305
10
auc_score = 0.608747044917 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.06 0. 0.04 0. 0.02
0. 0.02 0. 0. 0. 0.04 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.02 0. 0. 0. 0. 0.04 0. 0.02
0. 0.06 0.02 0.02 0. 0. 0. 0. 0.02 0. 0. 0. 0.
0.1 0.02 0.04 0.04 0. 0. 0.06 0.02 0. 0. 0. 0.04
0. 0. 0.02 0. 0.1 0. 0. 0.02 0. 0. 0. 0.02
0. 0. 0. 0. 0. 0.02 0.02 0.02 0.02 0. 0. 0.02]
2017-08-03 11:00:00 refes: (2820, 86) subjects: (60, 86) auc: 0.608747044917
11
auc_score = 0.716903073286 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.06 0. 0.02 0. 0.02
0. 0. 0. 0. 0. 0.02 0.04 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.02 0. 0. 0. 0. 0.02 0. 0.02
0. 0.04 0.02 0.02 0. 0. 0. 0. 0. 0. 0. 0.02
0. 0.16 0.02 0.02 0.04 0. 0. 0.02 0.04 0.02 0. 0. 0.
0. 0.02 0. 0. 0.12 0. 0. 0.02 0. 0. 0. 0. 0.
0.02 0. 0. 0.08 0.02 0.02 0.02 0.02 0. 0. 0. ]
2017-08-03 12:00:00 refes: (2820, 86) subjects: (60, 86) auc: 0.716903073286
12
auc_score = 0.602245862884 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.04 0. 0. 0. 0.02
0. 0.02 0. 0. 0. 0.08 0.04 0.02 0. 0. 0. 0. 0.
0. 0. 0.04 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0.16 0. 0. 0. 0. 0. 0. 0.04 0. 0. 0.02 0.
0.16 0.02 0.04 0.06 0. 0. 0.02 0.02 0. 0. 0. 0. 0.
0. 0. 0. 0.08 0. 0. 0. 0. 0. 0. 0.02 0. 0.
0. 0. 0.02 0. 0.02 0.02 0. 0.02 0. 0. ]
2017-08-03 13:00:00 refes: (2820, 86) subjects: (60, 86) auc: 0.602245862884
13
auc_score = 0.637706855792 feature importances: [ 0. 0. 0. 0. 0. 0.04 0. 0.06 0. 0.04 0. 0.02
0. 0. 0. 0. 0.02 0.02 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.04 0. 0. 0. 0.02 0. 0.04 0. 0. 0.02
0. 0.06 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0.12 0. 0.06 0.02 0. 0. 0.02 0.04 0. 0. 0. 0.02
0. 0.02 0. 0. 0.08 0. 0. 0.02 0. 0. 0. 0.02
0. 0.02 0.02 0. 0.02 0. 0.08 0.02 0.02 0. 0. 0. ]
2017-08-03 14:00:00 refes: (2820, 86) subjects: (60, 86) auc: 0.637706855792
14
auc_score = 0.664893617021 feature importances: [ 0. 0. 0. 0. 0. 0.08 0. 0.04 0.02 0.02 0. 0.02
0.02 0.02 0. 0. 0. 0.02 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.04 0. 0. 0. 0. 0. 0.04 0.02 0. 0.02
0. 0.1 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0.22 0. 0.02 0. 0. 0. 0.02 0.04 0. 0. 0. 0. 0.
0. 0. 0. 0.08 0. 0. 0.02 0. 0. 0. 0.02 0. 0.
0. 0. 0.02 0.02 0.02 0. 0.02 0.02 0. 0. ]
2017-08-03 15:00:00 refes: (2820, 86) subjects: (60, 86) auc: 0.664893617021
15
auc_score = 0.635933806147 feature importances: [ 0. 0. 0.02 0. 0. 0.1 0. 0.02 0.02 0.02 0. 0. 0.
0. 0. 0. 0. 0.04 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0.02 0. 0.04 0.02 0. 0. 0.
0.04 0. 0.02 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.2
0.04 0.02 0.02 0. 0. 0.02 0.04 0. 0. 0. 0.02 0. 0.
0. 0. 0.08 0. 0. 0.02 0. 0. 0. 0. 0. 0.02
0.02 0. 0. 0.06 0.02 0.02 0. 0. 0. 0. ]
2017-08-03 16:00:00 refes: (2820, 86) subjects: (60, 86) auc: 0.635933806147
16
auc_score = 0.635342789598 feature importances: [ 0. 0. 0.02 0. 0. 0.06 0. 0.06 0. 0.04 0. 0.02
0. 0.02 0. 0.02 0. 0.02 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.08 0. 0. 0. 0.02 0. 0. 0. 0.02 0.02
0. 0.02 0. 0.02 0. 0. 0. 0. 0.02 0. 0. 0. 0.
0.02 0. 0.04 0.08 0. 0. 0.02 0.08 0. 0. 0. 0. 0.
0.02 0. 0.02 0.06 0. 0. 0.02 0. 0. 0. 0.02 0.
0.02 0.02 0. 0.02 0.04 0.02 0.02 0. 0. 0. 0. ]
2017-08-03 17:00:00 refes: (2820, 86) subjects: (60, 86) auc: 0.635342789598
17
auc_score = 0.606973995272 feature importances: [ 0. 0. 0.02 0. 0. 0.04 0. 0.02 0.02 0.04 0. 0.02
0.02 0.04 0. 0. 0. 0.04 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.1 0. 0. 0. 0. 0. 0. 0. 0. 0.02
0. 0.02 0. 0.02 0. 0. 0. 0. 0.02 0. 0. 0. 0.
0.04 0.02 0.04 0.02 0. 0. 0.02 0.06 0. 0. 0. 0. 0.
0.02 0.02 0.02 0.04 0. 0. 0. 0.02 0. 0. 0.02 0. 0.
0.02 0. 0.02 0.06 0.02 0.04 0.02 0.02 0. 0. ]
2017-08-03 18:00:00 refes: (2820, 86) subjects: (60, 86) auc: 0.606973995272
18
auc_score = 0.633569739953 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.1 0.02 0.06 0. 0.02
0. 0.02 0. 0. 0. 0. 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0.02 0. 0. 0. 0.02 0.02
0. 0.02 0. 0. 0. 0. 0. 0. 0.02 0. 0. 0. 0.
0.02 0. 0.02 0.04 0.02 0. 0.04 0.04 0. 0. 0. 0.02
0. 0.02 0. 0.04 0.16 0. 0. 0. 0. 0. 0. 0.02
0. 0. 0.02 0. 0.02 0.02 0.08 0.02 0. 0. 0. 0.02]
2017-08-03 19:00:00 refes: (2820, 86) subjects: (60, 86) auc: 0.633569739953
19
auc_score = 0.553191489362 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.02 0.02 0.04 0. 0.02
0. 0.02 0. 0. 0. 0.02 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0.02 0. 0. 0. 0. 0.04 0.02 0. 0. 0.
0.04 0. 0.02 0. 0. 0.02 0. 0.02 0. 0. 0.02 0.
0.02 0.06 0.04 0.02 0. 0. 0.1 0.06 0. 0. 0. 0.02
0. 0.02 0. 0.02 0.06 0. 0. 0.02 0. 0. 0. 0.02
0. 0.02 0. 0. 0.02 0.02 0.02 0. 0.02 0.02 0. 0.02]
2017-08-03 20:00:00 refes: (2820, 86) subjects: (60, 86) auc: 0.553191489362
20
auc_score = 0.553191489362 feature importances: [ 0. 0. 0.04 0. 0. 0.02 0. 0.1 0. 0. 0. 0.02
0.02 0.02 0. 0. 0. 0.04 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0.02 0. 0.04 0.02 0.02 0. 0.
0.02 0. 0.02 0. 0. 0. 0.02 0.02 0. 0. 0.02 0.
0.06 0.02 0.06 0.04 0. 0. 0.04 0.06 0. 0. 0. 0.02
0. 0.02 0. 0. 0.08 0. 0. 0.02 0. 0. 0. 0.02
0. 0. 0. 0. 0. 0. 0.02 0.04 0. 0. 0. 0. ]
2017-08-03 21:00:00 refes: (2820, 86) subjects: (60, 86) auc: 0.553191489362
21
auc_score = 0.580378250591 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.12 0.02 0.02 0. 0.02
0.02 0.02 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0. 0. 0.02 0.02 0. 0.02
0. 0.04 0. 0. 0. 0. 0.02 0. 0.06 0. 0. 0. 0.
0.1 0.02 0.02 0. 0. 0. 0.04 0.06 0. 0. 0. 0.02
0. 0.02 0. 0.02 0.08 0. 0. 0.04 0. 0. 0. 0. 0.
0. 0. 0. 0.02 0. 0.04 0.02 0.02 0.02 0. 0.02]
2017-08-03 22:00:00 refes: (2820, 86) subjects: (60, 86) auc: 0.580378250591
22
auc_score = 0.525413711584 feature importances: [ 0. 0. 0. 0. 0. 0.04 0. 0.02 0.02 0.02 0.02 0.02
0.02 0.02 0. 0. 0. 0.06 0. 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0. 0. 0. 0.02 0.02 0. 0.
0.02 0. 0. 0. 0. 0.02 0. 0.02 0. 0.02 0. 0.
0.08 0.06 0.02 0.04 0. 0. 0.02 0.02 0. 0. 0. 0. 0.
0. 0. 0.02 0.12 0. 0. 0.02 0. 0. 0. 0.02 0. 0.
0.02 0. 0.02 0.06 0.02 0.02 0.02 0.02 0. 0.02]
2017-08-03 23:00:00 refes: (2820, 86) subjects: (60, 86) auc: 0.525413711584
23
auc_score = 0.498226950355 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.1 0.02 0.02 0. 0.02
0.02 0.02 0. 0. 0. 0.02 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0.02 0. 0. 0.02 0.02 0. 0. 0.02
0. 0.02 0. 0.02 0. 0. 0. 0. 0.04 0. 0.02 0. 0.
0.02 0.02 0.02 0.02 0. 0. 0.02 0.02 0. 0. 0. 0. 0.
0. 0. 0. 0.26 0.02 0. 0.02 0. 0. 0. 0.02 0. 0.
0. 0. 0.02 0.02 0.02 0.02 0. 0.02 0. 0. ]
2017-08-04 00:00:00 refes: (2820, 86) subjects: (24, 86) auc: 0.498226950355
24
auc_score = 0.526585882665 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.04 0. 0.02 0. 0.02
0.02 0.02 0. 0. 0. 0.06 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0.02 0.04 0.02 0. 0.02
0. 0.02 0. 0.02 0. 0. 0.02 0. 0.02 0. 0. 0. 0.
0.1 0. 0.02 0. 0. 0. 0.04 0.04 0. 0. 0. 0.04
0. 0. 0. 0. 0.1 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0.02 0.02 0.1 0.02 0.02 0.04 0. 0.02]
2017-08-04 01:00:00 refes: (2795, 86) subjects: (60, 86) auc: 0.526585882665
25
auc_score = 0.580949543107 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.04 0. 0.02 0. 0.02
0. 0. 0. 0. 0. 0. 0.02 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0. 0. 0.02 0.02 0. 0.02
0. 0.02 0. 0. 0.02 0. 0.02 0. 0.02 0. 0. 0.
0.02 0.12 0. 0.06 0.02 0. 0. 0.14 0.08 0. 0. 0.
0.04 0. 0. 0. 0. 0.06 0. 0. 0. 0. 0. 0.
0.02 0. 0.02 0. 0. 0.02 0.02 0.02 0.02 0.02 0.02 0.
0.02]
2017-08-04 02:00:00 refes: (2795, 86) subjects: (60, 86) auc: 0.580949543107
26
auc_score = 0.497616209774 feature importances: [ 0. 0. 0. 0. 0. 0. 0. 0.08 0.02 0. 0. 0.02
0. 0.02 0. 0. 0. 0.06 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0. 0. 0.04 0.02 0. 0.02
0. 0.02 0. 0. 0.02 0. 0. 0. 0. 0. 0. 0.02
0. 0.08 0.02 0.02 0.02 0. 0. 0.1 0.04 0. 0. 0.
0.02 0. 0.06 0. 0. 0.14 0. 0. 0. 0. 0.02 0.
0.02 0. 0. 0. 0. 0.02 0.02 0.02 0. 0. 0.02 0. 0. ]
2017-08-04 03:00:00 refes: (2795, 86) subjects: (60, 86) auc: 0.497616209774
27
auc_score = 0.525989935108 feature importances: [ 0. 0. 0. 0. 0. 0.04 0. 0. 0. 0.04 0. 0.02
0.02 0.02 0. 0. 0. 0.08 0. 0. 0. 0. 0. 0. 0.
0. 0.06 0.02 0. 0. 0. 0.02 0. 0. 0.02 0. 0.02
0. 0.02 0. 0. 0. 0. 0.02 0.02 0. 0. 0. 0.02
0. 0.1 0.02 0.04 0.04 0. 0. 0.02 0.04 0. 0. 0. 0.
0. 0.02 0. 0. 0.06 0. 0. 0.02 0. 0. 0. 0.02
0. 0. 0.02 0. 0.02 0.02 0.02 0.04 0.02 0.02 0. 0.02]
2017-08-04 04:00:00 refes: (2795, 86) subjects: (60, 86) auc: 0.525989935108
28
auc_score = 0.49821215733 feature importances: [ 0. 0. 0. 0. 0. 0.04 0. 0.04 0. 0.04 0. 0.02
0.02 0.02 0. 0.02 0. 0.04 0. 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0. 0. 0.02 0.02 0. 0. 0.
0.02 0. 0. 0. 0. 0. 0. 0.02 0. 0. 0.04 0.02
0.1 0.02 0.04 0.02 0. 0. 0.02 0.04 0. 0. 0. 0. 0.
0.02 0.02 0.02 0.06 0. 0. 0.06 0. 0. 0. 0. 0.
0.02 0. 0. 0.02 0.02 0.06 0.02 0.04 0. 0. 0. ]
2017-08-04 05:00:00 refes: (2795, 86) subjects: (60, 86) auc: 0.49821215733
29
auc_score = 0.552575817772 feature importances: [ 0. 0. 0.02 0. 0. 0. 0. 0.12 0.02 0.02 0. 0.02
0. 0.02 0. 0. 0. 0.1 0. 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0. 0. 0.02 0. 0. 0.02
0. 0.06 0. 0. 0. 0. 0. 0. 0.06 0. 0. 0.02
0. 0.02 0.04 0.04 0.1 0. 0. 0.02 0.02 0. 0. 0.
0.02 0. 0. 0. 0. 0.06 0. 0. 0. 0. 0.02 0.
0.02 0. 0.02 0. 0. 0. 0.02 0.02 0.02 0. 0.02 0.
0.02]
2017-08-04 06:00:00 refes: (2795, 86) subjects: (60, 86) auc: 0.552575817772
30
auc_score = 0.550787975103 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.04 0.02 0. 0. 0.02
0. 0.02 0. 0. 0. 0.04 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0.02 0. 0. 0. 0.02 0.04 0. 0.02
0. 0.08 0. 0. 0.02 0. 0. 0. 0.02 0. 0. 0. 0.
0.02 0.02 0.04 0.02 0. 0. 0.02 0.06 0. 0. 0. 0.02
0. 0.06 0. 0. 0.08 0. 0. 0.04 0. 0. 0. 0.02
0. 0. 0. 0. 0.02 0.02 0.08 0.02 0.02 0.02 0. 0. ]
2017-08-04 07:00:00 refes: (2795, 86) subjects: (60, 86) auc: 0.550787975103
31
auc_score = 0.525393987551 feature importances: [ 0. 0. 0. 0. 0. 0. 0. 0.04 0. 0.04 0. 0.02
0. 0.02 0. 0. 0. 0.02 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0. 0.02 0.02 0.02 0. 0.02
0. 0.06 0. 0.04 0. 0. 0. 0. 0.02 0. 0. 0.02
0. 0.12 0.02 0.02 0. 0. 0. 0.04 0.06 0. 0. 0. 0.
0. 0.04 0. 0. 0.16 0. 0. 0. 0. 0. 0. 0.02
0. 0.02 0. 0. 0.02 0.02 0.02 0.02 0. 0.02 0. 0. ]
2017-08-04 08:00:00 refes: (2795, 86) subjects: (60, 86) auc: 0.525393987551
32
auc_score = 0.553767712886 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.08 0.02 0.02 0. 0.02
0. 0.02 0. 0. 0. 0.06 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0.02 0.02 0. 0.02
0. 0.08 0. 0. 0. 0. 0.02 0. 0. 0. 0. 0.02
0. 0.14 0.02 0.02 0.02 0. 0. 0.06 0.02 0. 0. 0.
0.02 0. 0.02 0. 0.02 0.08 0. 0. 0.02 0. 0. 0.
0.02 0. 0. 0. 0. 0.02 0.02 0.04 0.02 0. 0.02 0. 0. ]
2017-08-04 09:00:00 refes: (2795, 86) subjects: (60, 86) auc: 0.553767712886
33
auc_score = 0.550787975103 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.06 0.02 0.02 0. 0.02
0.02 0.02 0. 0. 0. 0.04 0. 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0. 0. 0.02 0. 0. 0.02
0. 0.08 0.02 0. 0. 0. 0.02 0. 0.02 0. 0. 0.02
0. 0.08 0.04 0.04 0.02 0. 0. 0.02 0.04 0. 0. 0. 0.
0. 0.02 0. 0.02 0.12 0. 0.04 0. 0. 0. 0. 0.02
0. 0.02 0. 0. 0. 0. 0.02 0.02 0.02 0.02 0. 0. ]
2017-08-04 10:00:00 refes: (2795, 86) subjects: (60, 86) auc: 0.550787975103
34
auc_score = 0.497020262217 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.06 0.02 0.02 0. 0.02
0. 0.02 0. 0. 0. 0.02 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0. 0.02 0.02 0. 0. 0.02
0. 0.02 0. 0. 0. 0. 0.02 0. 0.02 0. 0. 0.02
0. 0.14 0.04 0.06 0.04 0. 0. 0.1 0.04 0. 0. 0.
0.02 0. 0. 0. 0. 0.08 0. 0. 0.02 0. 0. 0.
0.02 0. 0. 0. 0. 0. 0. 0.02 0.02 0.02 0.02 0. 0. ]
2017-08-04 11:00:00 refes: (2795, 86) subjects: (60, 86) auc: 0.497020262217
35
auc_score = 0.582737385777 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.02 0.02 0. 0. 0.02
0. 0.02 0. 0. 0. 0.06 0.06 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0. 0. 0.02 0.02 0. 0.02
0.04 0.06 0. 0. 0. 0. 0.02 0. 0. 0. 0. 0. 0.
0.14 0.02 0.08 0.02 0. 0. 0.06 0.02 0. 0. 0. 0.02
0. 0. 0. 0. 0.06 0. 0. 0. 0. 0. 0. 0.02
0. 0. 0. 0. 0.06 0.02 0.02 0.02 0. 0.02 0. 0. ]
2017-08-04 12:00:00 refes: (2795, 86) subjects: (60, 86) auc: 0.582737385777
36
auc_score = 0.635909151106 feature importances: [ 0. 0. 0.02 0. 0. 0.02 0. 0.02 0. 0.02 0.02 0.02
0. 0.02 0. 0. 0.02 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0.06 0. 0. 0. 0. 0. 0.02 0.02 0. 0.02
0. 0.06 0. 0. 0. 0. 0.02 0. 0.02 0. 0. 0.02
0. 0.04 0.02 0.04 0.06 0. 0. 0.06 0.06 0. 0. 0.
0.02 0. 0. 0. 0.02 0.1 0. 0. 0.04 0. 0. 0.
0.02 0. 0. 0.02 0. 0.02 0.04 0.02 0. 0. 0.02 0. 0. ]
2017-08-04 13:00:00 refes: (2795, 86) subjects: (60, 86) auc: 0.635909151106
37
auc_score = 0.775393987551 feature importances: [ 0.02 0. 0. 0. 0. 0.02 0. 0.02 0. 0.02 0.02 0.02
0. 0. 0. 0. 0.04 0.02 0.04 0. 0. 0. 0. 0. 0.
0. 0. 0.04 0. 0. 0. 0. 0. 0.02 0.04 0. 0.02
0. 0.04 0. 0. 0. 0.02 0. 0.02 0.02 0. 0. 0. 0.
0.1 0. 0.08 0.02 0. 0. 0.02 0.12 0. 0. 0. 0.02
0. 0. 0. 0. 0.08 0. 0. 0. 0. 0. 0. 0.02
0. 0. 0. 0. 0. 0.04 0.02 0.02 0. 0.02 0. 0. ]
2017-08-04 14:00:00 refes: (2795, 86) subjects: (60, 86) auc: 0.775393987551
38
auc_score = 0.885909151106 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.1 0. 0. 0.02 0. 0.
0. 0. 0. 0.02 0.04 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.08 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0.02 0. 0. 0. 0.22 0.02 0. 0. 0. 0. 0. 0.
0.16 0. 0.12 0. 0. 0. 0. 0.02 0. 0. 0. 0. 0.
0. 0. 0. 0.06 0. 0. 0. 0. 0. 0. 0.02 0. 0.
0. 0. 0. 0.02 0. 0.02 0. 0.04 0. 0. ]
2017-08-04 15:00:00 refes: (2795, 86) subjects: (60, 86) auc: 0.885909151106
39
auc_score = 0.553171765329 feature importances: [ 0. 0. 0. 0. 0. 0.06 0. 0.04 0. 0.02 0. 0.02
0.02 0. 0. 0. 0.02 0.02 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.06 0.02 0. 0. 0. 0. 0.02 0. 0. 0.
0.02 0.02 0. 0. 0. 0.06 0. 0. 0. 0. 0. 0. 0.
0.06 0.02 0.14 0.02 0. 0. 0. 0.08 0. 0. 0. 0. 0.
0. 0. 0. 0.14 0. 0. 0. 0. 0. 0. 0.02 0. 0.
0. 0. 0. 0.02 0. 0.02 0. 0.06 0. 0. ]
2017-08-04 16:00:00 refes: (2795, 86) subjects: (60, 86) auc: 0.553171765329
40
auc_score = 0.552575817772 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.04 0.02 0.02 0.02 0.02
0. 0.02 0. 0. 0. 0.04 0. 0. 0. 0. 0. 0. 0.
0. 0. 0.04 0. 0. 0. 0. 0. 0.02 0.02 0. 0.02
0. 0.02 0. 0.04 0. 0.2 0.02 0. 0. 0. 0. 0. 0.
0.06 0.02 0.1 0.02 0. 0. 0.02 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0.1 0. 0. 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.02 0.02 0. 0. 0.02 0. 0. ]
2017-08-04 17:00:00 refes: (2795, 86) subjects: (60, 86) auc: 0.552575817772
41
auc_score = 0.663686928884 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.02 0. 0.02 0. 0.02
0. 0.02 0. 0. 0. 0.04 0.02 0. 0. 0. 0. 0. 0.
0. 0.04 0.02 0. 0. 0. 0. 0. 0.02 0. 0.02 0.02
0. 0.02 0. 0.04 0. 0.16 0. 0. 0. 0. 0. 0. 0.
0.08 0. 0.04 0. 0. 0. 0.02 0.1 0. 0. 0. 0.02
0. 0. 0.02 0. 0.08 0. 0. 0.02 0.02 0. 0. 0. 0.
0. 0. 0. 0.02 0.02 0.02 0. 0.04 0. 0. 0. ]
2017-08-04 18:00:00 refes: (2795, 86) subjects: (60, 86) auc: 0.663686928884
42
auc_score = 0.499404052443 feature importances: [ 0. 0. 0. 0. 0. 0.04 0. 0.04 0.02 0.02 0. 0.02
0. 0.02 0. 0. 0.02 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0. 0. 0.02 0.02 0. 0. 0.
0.02 0. 0.02 0. 0.08 0.02 0. 0.02 0. 0. 0.02 0.
0.12 0.04 0.04 0.04 0. 0. 0.02 0.02 0.02 0. 0. 0. 0.
0. 0. 0. 0.1 0. 0. 0.02 0. 0.02 0. 0. 0. 0.
0. 0. 0.02 0.02 0.02 0. 0.04 0.02 0. 0. ]
2017-08-04 19:00:00 refes: (2795, 86) subjects: (60, 86) auc: 0.499404052443
43
auc_score = 0.524202092438 feature importances: [ 0. 0. 0.02 0. 0. 0.02 0. 0.02 0. 0.02 0. 0.02
0. 0.02 0. 0. 0. 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0. 0.06 0.02 0. 0. 0. 0. 0.02 0.02 0. 0.02
0. 0.04 0. 0. 0. 0.08 0.02 0. 0.02 0. 0. 0.02
0. 0.08 0.04 0.02 0.02 0. 0. 0.02 0.04 0. 0. 0.
0.02 0. 0. 0. 0. 0.04 0. 0. 0.02 0. 0. 0.
0.04 0. 0. 0. 0. 0. 0.04 0.06 0. 0.04 0.02 0.04
0. ]
2017-08-04 20:00:00 refes: (2795, 86) subjects: (60, 86) auc: 0.524202092438
44
auc_score = 0.552575817772 feature importances: [ 0. 0. 0.02 0. 0. 0.02 0. 0.04 0. 0. 0. 0.02
0. 0.02 0. 0. 0. 0.04 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0. 0.02 0.02 0.02 0. 0. 0.
0.02 0. 0.02 0. 0.06 0. 0. 0.02 0. 0.02 0. 0.
0.08 0. 0.04 0.06 0. 0. 0.02 0.02 0. 0. 0. 0.02
0. 0. 0. 0. 0.12 0. 0. 0.02 0. 0. 0. 0.02
0. 0.04 0. 0. 0.02 0.02 0.02 0.04 0. 0.02 0.04 0. ]
2017-08-04 21:00:00 refes: (2795, 86) subjects: (60, 86) auc: 0.552575817772
45
auc_score = 0.580949543107 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.06 0.04 0. 0. 0.02
0. 0.02 0. 0. 0.02 0.04 0.04 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0. 0. 0. 0.02 0. 0. 0.
0.02 0. 0.02 0. 0.22 0.02 0. 0. 0. 0. 0. 0.
0.08 0.02 0.08 0. 0. 0. 0.06 0.04 0. 0. 0. 0. 0.
0. 0. 0. 0.02 0.02 0. 0.02 0. 0. 0. 0.04 0. 0.
0. 0. 0.02 0. 0. 0. 0.02 0. 0. 0. ]
2017-08-04 22:00:00 refes: (2795, 86) subjects: (60, 86) auc: 0.580949543107
46
auc_score = 0.552575817772 feature importances: [ 0. 0. 0. 0. 0. 0.06 0. 0.04 0.02 0.02 0. 0.02
0. 0.02 0. 0. 0. 0.04 0. 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0. 0. 0.02 0.02 0. 0.02
0. 0.04 0. 0. 0. 0.1 0.02 0. 0.02 0. 0. 0. 0.
0.04 0.02 0.06 0.02 0. 0. 0.02 0.02 0. 0. 0. 0. 0.
0.02 0. 0. 0.06 0.02 0. 0.02 0. 0. 0. 0.02 0.
0.02 0.04 0. 0. 0.04 0.02 0.02 0.02 0.02 0.02 0. ]
2017-08-04 23:00:00 refes: (2795, 86) subjects: (60, 86) auc: 0.552575817772
47
auc_score = 0.498808104887 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.08 0.02 0.02 0. 0.02
0. 0.02 0. 0. 0. 0.04 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0. 0. 0.02 0.02 0. 0. 0.
0.02 0. 0. 0. 0.02 0.02 0. 0.02 0. 0. 0. 0.
0.02 0.04 0.04 0.02 0. 0. 0.02 0.06 0. 0. 0.02 0. 0.
0.04 0. 0. 0.16 0.06 0. 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0.02 0. 0.02 0. 0.02 0.04 0. ]
2017-08-05 00:00:00 refes: (2795, 86) subjects: (41, 86) auc: 0.498808104887
48
auc_score = 0.523650419287 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.02 0.02 0.02 0. 0.02
0. 0.02 0. 0. 0. 0.14 0.04 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0.02 0. 0.02 0. 0. 0.02
0. 0.02 0. 0.02 0. 0.12 0. 0. 0.02 0. 0. 0. 0.
0.08 0.02 0.04 0. 0. 0. 0.04 0.04 0. 0. 0. 0.02
0. 0. 0. 0. 0.04 0.02 0. 0.02 0. 0. 0.02 0. 0.
0.02 0.02 0. 0. 0.02 0.02 0. 0. 0.02 0. 0. ]
2017-08-05 01:00:00 refes: (2825, 86) subjects: (60, 86) auc: 0.523650419287
49
auc_score = 0.495872641509 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.02 0.02 0.02 0. 0.02
0. 0.02 0. 0. 0. 0.06 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0. 0. 0.02 0.02 0. 0.02
0.02 0.02 0. 0. 0. 0.12 0.02 0. 0.04 0. 0. 0. 0.
0.1 0.08 0.02 0.02 0. 0. 0.02 0.02 0. 0. 0.02 0.02
0. 0. 0. 0. 0.08 0. 0. 0. 0. 0. 0. 0.02
0. 0. 0. 0. 0.02 0. 0.02 0.02 0.02 0.02 0. 0. ]
2017-08-05 02:00:00 refes: (2825, 86) subjects: (60, 86) auc: 0.495872641509
50
auc_score = 0.527188155136 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.02 0. 0.02 0. 0.02
0. 0.02 0. 0.02 0. 0.04 0.04 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0.02 0. 0.02 0. 0. 0. 0.
0.02 0. 0.02 0. 0.04 0. 0. 0.02 0. 0. 0. 0. 0.1
0.04 0.04 0.04 0. 0. 0.02 0.06 0. 0. 0. 0.02 0.
0.02 0. 0. 0.1 0. 0. 0.04 0. 0. 0. 0.02 0.
0.02 0. 0. 0.04 0. 0.02 0.02 0.02 0.02 0. 0. ]
2017-08-05 03:00:00 refes: (2825, 86) subjects: (60, 86) auc: 0.527188155136
51
auc_score = 0.498820754717 feature importances: [ 0. 0. 0.02 0. 0. 0.02 0. 0.02 0.02 0.02 0. 0.02
0. 0.02 0. 0.02 0. 0.06 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0. 0. 0.02 0. 0. 0. 0.
0.02 0. 0.02 0. 0.02 0.02 0. 0.02 0. 0. 0. 0.
0.18 0.04 0.04 0.02 0. 0. 0.04 0.02 0. 0. 0. 0.02
0. 0.02 0. 0. 0.06 0. 0. 0. 0. 0. 0. 0.02
0. 0. 0. 0. 0.04 0.02 0.02 0.04 0.02 0.02 0. 0. ]
2017-08-05 04:00:00 refes: (2825, 86) subjects: (60, 86) auc: 0.498820754717
52
auc_score = 0.523060796646 feature importances: [ 0. 0. 0. 0. 0. 0.04 0. 0.04 0. 0.02 0.02 0.02
0. 0.02 0. 0. 0.02 0.04 0.02 0. 0. 0. 0. 0.02
0. 0. 0. 0.02 0. 0. 0. 0.02 0. 0. 0.02 0. 0.
0. 0.02 0. 0.04 0. 0.06 0.02 0. 0.02 0. 0. 0. 0.
0.08 0.06 0.02 0.02 0. 0. 0. 0.06 0. 0. 0. 0.02
0. 0.02 0. 0. 0.04 0. 0. 0.02 0. 0. 0. 0. 0.
0.02 0. 0. 0. 0.04 0.02 0.06 0.02 0.02 0. 0. ]
2017-08-05 05:00:00 refes: (2825, 86) subjects: (60, 86) auc: 0.523060796646
53
auc_score = 0.498231132075 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.06 0.02 0.02 0. 0.02
0. 0.02 0. 0. 0. 0.04 0.04 0. 0. 0. 0.02 0. 0.
0. 0. 0.02 0. 0. 0. 0.02 0.02 0.02 0.04 0. 0.02
0. 0.04 0. 0.02 0. 0.04 0.02 0. 0. 0. 0. 0. 0.
0.1 0.02 0.02 0.02 0. 0. 0.02 0.02 0. 0. 0. 0.02
0. 0.02 0. 0. 0.06 0. 0. 0.02 0. 0. 0. 0.02
0. 0.04 0. 0. 0.02 0. 0.02 0. 0.02 0.02 0.02 0. ]
2017-08-05 06:00:00 refes: (2825, 86) subjects: (60, 86) auc: 0.498231132075
54
auc_score = 0.55319706499 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.08 0. 0. 0. 0.02
0. 0.02 0. 0. 0. 0.06 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0.02 0. 0. 0. 0. 0. 0.02 0. 0.02
0. 0.04 0. 0.02 0. 0.04 0.02 0. 0.02 0. 0. 0. 0.
0.08 0.02 0.02 0.04 0. 0. 0.02 0.06 0. 0. 0. 0.02
0. 0.02 0. 0. 0.08 0. 0. 0.02 0.02 0. 0. 0.02
0. 0. 0. 0. 0. 0. 0.04 0.06 0.02 0.02 0. 0. ]
2017-08-05 07:00:00 refes: (2825, 86) subjects: (60, 86) auc: 0.55319706499
55
auc_score = 0.522471174004 feature importances: [ 0. 0. 0. 0. 0. 0.08 0. 0.04 0. 0.02 0.04 0. 0.
0. 0. 0. 0.02 0.08 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0. 0.02 0.02 0. 0. 0.
0.04 0. 0. 0.02 0. 0.04 0. 0.04 0. 0. 0. 0. 0.1
0.02 0.04 0.02 0. 0. 0.04 0.04 0. 0. 0. 0. 0.
0.02 0. 0. 0.08 0. 0. 0.02 0. 0. 0. 0.02 0. 0.
0. 0. 0. 0. 0.04 0.02 0.02 0.02 0. 0. ]
2017-08-05 08:00:00 refes: (2825, 86) subjects: (60, 86) auc: 0.522471174004
56
auc_score = 0.5 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.04 0.02 0. 0. 0. 0.
0.02 0. 0.02 0. 0.06 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0.02 0.02 0. 0.02 0.
0.02 0. 0.02 0. 0.02 0. 0. 0. 0. 0. 0. 0.
0.12 0.04 0.02 0.06 0. 0. 0.02 0.04 0. 0. 0. 0. 0.
0.02 0. 0.02 0.14 0. 0. 0.02 0. 0. 0. 0.02 0. 0.
0. 0. 0. 0.02 0.04 0.02 0.02 0.06 0. 0. ]
2017-08-05 09:00:00 refes: (2825, 86) subjects: (60, 86) auc: 0.5
57
auc_score = 0.499410377358 feature importances: [ 0. 0. 0.02 0. 0. 0.04 0. 0.02 0. 0.02 0. 0. 0.
0.02 0. 0. 0.02 0.02 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.04 0. 0. 0. 0. 0. 0. 0.02 0. 0. 0.
0.02 0. 0.02 0. 0.12 0. 0. 0.02 0. 0. 0. 0.
0.22 0.02 0.04 0.02 0. 0. 0.02 0.04 0. 0. 0. 0. 0.
0. 0. 0. 0.06 0. 0. 0. 0. 0. 0. 0.02 0.
0.02 0. 0. 0.02 0.02 0.04 0.02 0.02 0.02 0. 0. ]
2017-08-05 10:00:00 refes: (2825, 86) subjects: (60, 86) auc: 0.499410377358
58
auc_score = 0.498231132075 feature importances: [ 0. 0. 0. 0. 0. 0.04 0. 0.04 0. 0.02 0.02 0. 0.
0.02 0. 0. 0. 0.04 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0. 0.02 0.02 0. 0. 0.02
0.04 0. 0. 0. 0.04 0.02 0. 0.02 0. 0. 0. 0. 0.2
0.06 0.02 0.04 0. 0. 0.02 0. 0. 0. 0. 0.02 0.
0.02 0. 0. 0.08 0. 0. 0. 0. 0. 0. 0.02 0.
0.02 0.02 0. 0.02 0.02 0.02 0.02 0. 0. 0. 0. ]
2017-08-05 11:00:00 refes: (2825, 86) subjects: (60, 86) auc: 0.498231132075
59
auc_score = 0.497641509434 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.02 0.02 0.02 0.02 0. 0.
0.02 0. 0. 0.04 0.06 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0.04 0.02 0. 0. 0.02 0.
0.02 0. 0.02 0. 0.04 0.02 0. 0.02 0. 0.02 0. 0.
0.06 0.02 0.02 0. 0. 0. 0.1 0.04 0. 0. 0. 0. 0.
0.02 0. 0.02 0.06 0. 0. 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0.02 0.04 0.02 0.02 0.04 0.02 0. ]
2017-08-05 12:00:00 refes: (2825, 86) subjects: (60, 86) auc: 0.497641509434
60
auc_score = 0.554965932914 feature importances: [ 0. 0. 0. 0. 0. 0.02 0.02 0.02 0. 0.02 0.02 0. 0.
0.02 0. 0. 0. 0.04 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0.04 0. 0. 0. 0. 0.02 0. 0. 0. 0.04 0.
0.02 0. 0.02 0. 0.08 0.02 0. 0.02 0. 0. 0. 0.
0.06 0.06 0.02 0.06 0. 0. 0.06 0.04 0. 0. 0. 0. 0.
0. 0. 0. 0.06 0. 0. 0. 0. 0. 0. 0. 0.
0.02 0.02 0. 0.04 0.06 0.02 0.02 0. 0.02 0. 0. ]
2017-08-05 13:00:00 refes: (2825, 86) subjects: (60, 86) auc: 0.554965932914
61
auc_score = 0.525419287212 feature importances: [ 0. 0. 0.02 0. 0. 0.02 0.02 0.02 0. 0.02 0.02 0. 0.
0.02 0. 0. 0.02 0.04 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0.02 0.04 0.02 0. 0.02 0.
0.02 0. 0.02 0. 0.04 0.02 0. 0. 0. 0. 0. 0.
0.12 0. 0.04 0.02 0. 0. 0.02 0.02 0. 0. 0. 0.02
0. 0.06 0. 0. 0.1 0. 0. 0. 0. 0. 0. 0. 0.
0.02 0.02 0. 0.02 0.02 0.02 0.02 0.02 0.02 0. 0. ]
2017-08-05 14:00:00 refes: (2825, 86) subjects: (60, 86) auc: 0.525419287212
62
auc_score = 0.553786687631 feature importances: [ 0. 0. 0. 0. 0. 0.02 0.02 0.06 0.02 0.02 0.02 0.02
0. 0.02 0. 0. 0.04 0.02 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0. 0. 0.02 0.02 0. 0.02
0. 0.02 0. 0. 0.02 0.06 0. 0. 0.02 0. 0. 0. 0.
0.08 0.06 0.02 0.02 0. 0. 0.02 0.04 0. 0. 0. 0.02
0. 0. 0. 0.02 0.12 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.02 0.02 0.02 0.02 0.02 0. 0. 0. ]
2017-08-05 15:00:00 refes: (2825, 86) subjects: (60, 86) auc: 0.553786687631
63
auc_score = 0.523650419287 feature importances: [ 0. 0. 0. 0. 0. 0.04 0.02 0.14 0. 0.02 0.02 0.02
0. 0.02 0. 0. 0. 0.04 0.04 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0.02 0. 0. 0. 0.02 0.02 0.02 0. 0.
0.02 0.02 0. 0. 0. 0.04 0.02 0. 0. 0. 0. 0. 0.
0.12 0.02 0.02 0.02 0. 0. 0. 0.06 0. 0. 0. 0. 0.
0. 0. 0. 0.12 0. 0. 0.02 0. 0. 0. 0.02 0.
0.02 0. 0. 0. 0. 0. 0. 0.02 0. 0. 0. ]
2017-08-05 16:00:00 refes: (2825, 86) subjects: (60, 86) auc: 0.523650419287
64
auc_score = 0.526008909853 feature importances: [ 0. 0. 0. 0. 0. 0. 0. 0.08 0. 0.02 0. 0. 0.
0.02 0. 0. 0.02 0.02 0.04 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0.02 0. 0.02 0. 0. 0.
0.02 0. 0.02 0. 0.06 0.02 0. 0. 0. 0. 0. 0.
0.14 0. 0.04 0. 0. 0. 0.02 0.04 0. 0. 0. 0.02
0. 0.02 0. 0.02 0.08 0. 0. 0.02 0. 0. 0. 0.04
0. 0.02 0. 0. 0.02 0.02 0.04 0.04 0.02 0.02 0. 0. ]
2017-08-05 17:00:00 refes: (2825, 86) subjects: (60, 86) auc: 0.526008909853
65
auc_score = 0.526598532495 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.08 0. 0.04 0.02 0.02
0. 0.02 0. 0. 0. 0.04 0.02 0. 0. 0. 0.02 0. 0.
0. 0. 0.02 0. 0. 0. 0.02 0. 0.02 0. 0. 0.04
0. 0.02 0. 0. 0. 0.08 0.02 0. 0. 0. 0. 0. 0.
0.04 0.08 0.02 0.06 0. 0. 0.02 0.02 0. 0. 0. 0.02
0. 0. 0. 0. 0.1 0. 0. 0.02 0. 0. 0. 0.
0.02 0. 0. 0. 0.02 0.02 0.02 0.02 0. 0.02 0. 0. ]
2017-08-05 18:00:00 refes: (2825, 86) subjects: (60, 86) auc: 0.526598532495
66
auc_score = 0.523650419287 feature importances: [ 0. 0. 0. 0. 0. 0.02 0.02 0.06 0. 0.04 0.02 0. 0.
0. 0. 0. 0. 0.02 0. 0. 0. 0. 0. 0. 0. 0.
0.02 0.02 0. 0.02 0. 0. 0. 0.02 0.02 0. 0.02 0.02
0.1 0. 0. 0. 0.1 0.02 0. 0.02 0. 0. 0. 0.
0.04 0.02 0.04 0.04 0. 0. 0.12 0.06 0. 0. 0. 0. 0.
0. 0. 0. 0.04 0. 0. 0.02 0. 0. 0. 0. 0.
0.02 0. 0. 0. 0. 0.02 0.02 0. 0. 0. 0. ]
2017-08-05 19:00:00 refes: (2825, 86) subjects: (60, 86) auc: 0.523650419287
67
auc_score = 0.525419287212 feature importances: [ 0. 0. 0. 0. 0. 0.04 0. 0.06 0.02 0. 0. 0. 0.
0. 0. 0. 0. 0.04 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0.02 0. 0. 0. 0.02 0.02 0. 0.02 0.02
0.06 0. 0. 0. 0.04 0. 0. 0.02 0. 0. 0. 0.
0.08 0.06 0.04 0.08 0. 0. 0.02 0.02 0. 0. 0. 0.02
0. 0. 0.02 0. 0.12 0. 0. 0. 0. 0. 0. 0.02
0. 0. 0. 0. 0. 0.02 0.02 0.02 0.02 0.02 0. 0. ]
2017-08-05 20:00:00 refes: (2825, 86) subjects: (60, 86) auc: 0.525419287212
68
auc_score = 0.551428197065 feature importances: [ 0. 0. 0. 0. 0. 0.02 0.08 0.06 0. 0.02 0. 0.
0.02 0. 0. 0. 0. 0.02 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.08 0. 0. 0. 0. 0. 0.02 0.02 0. 0.02
0.02 0.02 0. 0. 0. 0.06 0. 0. 0.02 0. 0. 0. 0.
0.06 0.02 0.02 0.06 0. 0. 0. 0.1 0. 0. 0. 0.02
0. 0. 0. 0. 0.1 0. 0. 0.02 0. 0. 0. 0.02
0. 0. 0.02 0. 0. 0.02 0.02 0. 0.02 0. 0. 0. ]
2017-08-05 21:00:00 refes: (2825, 86) subjects: (60, 86) auc: 0.551428197065
69
auc_score = 0.524240041929 feature importances: [ 0. 0. 0. 0. 0. 0.02 0.02 0.04 0. 0.04 0. 0. 0.
0. 0. 0. 0. 0.06 0. 0. 0. 0. 0. 0. 0. 0.
0.02 0.02 0.02 0. 0. 0. 0. 0.02 0.02 0. 0.02 0.
0.02 0. 0.02 0. 0.14 0. 0. 0. 0. 0. 0. 0.
0.16 0. 0.02 0.06 0. 0. 0.02 0.04 0. 0. 0. 0.02
0. 0.02 0. 0. 0.08 0. 0. 0. 0. 0. 0. 0.02
0. 0. 0. 0. 0. 0.02 0.02 0. 0.02 0.02 0. 0. ]
2017-08-05 22:00:00 refes: (2825, 86) subjects: (60, 86) auc: 0.524240041929
70
auc_score = 0.525419287212 feature importances: [ 0. 0. 0. 0. 0. 0.04 0.02 0.18 0. 0.04 0.02 0. 0.
0. 0. 0. 0.02 0.04 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0.02 0. 0. 0. 0.06 0.02 0. 0.02 0. 0. 0. 0.
0.16 0.06 0.04 0.02 0. 0. 0.02 0. 0. 0. 0. 0. 0.
0.04 0. 0. 0.02 0. 0. 0.02 0. 0. 0. 0.02 0. 0.
0. 0. 0. 0. 0.04 0. 0.02 0. 0.02 0. ]
2017-08-05 23:00:00 refes: (2825, 86) subjects: (60, 86) auc: 0.525419287212
71
auc_score = 0.498231132075 feature importances: [ 0. 0. 0.02 0. 0. 0.02 0. 0.04 0.02 0.14 0.02 0. 0.
0.02 0. 0. 0. 0.02 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0. 0.02 0.02 0. 0.02 0.
0.02 0. 0.02 0. 0.02 0.02 0. 0.02 0. 0. 0. 0.
0.08 0.16 0.02 0.02 0. 0. 0.02 0.02 0. 0. 0. 0. 0.
0.02 0. 0. 0.04 0. 0. 0.02 0. 0. 0. 0.02 0. 0.
0.02 0. 0. 0.02 0.02 0.02 0. 0. 0. 0. ]
2017-08-06 00:00:00 refes: (2825, 86) subjects: (22, 86) auc: 0.498231132075
72
auc_score = 0.497638724911 feature importances: [ 0. 0. 0. 0. 0. 0.04 0.02 0.04 0.02 0. 0. 0. 0.
0.02 0. 0. 0. 0. 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0. 0.02 0. 0. 0.02 0.02
0.02 0. 0.02 0. 0.02 0. 0. 0.04 0. 0. 0. 0.
0.06 0.04 0.04 0.02 0. 0. 0.14 0.02 0. 0. 0. 0.02
0. 0. 0. 0. 0.1 0. 0. 0. 0. 0. 0. 0. 0.
0.02 0.02 0. 0.02 0.04 0.02 0.04 0.02 0.02 0.02 0. ]
2017-08-06 01:00:00 refes: (2823, 86) subjects: (60, 86) auc: 0.497638724911
73
auc_score = 0.580381739473 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.02 0.02 0.02 0.02 0. 0.
0. 0. 0.02 0.02 0.08 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0.02 0.02 0.02 0. 0.02 0.
0.02 0. 0.04 0.06 0.08 0.02 0.02 0.02 0. 0. 0. 0.
0.08 0.02 0.04 0.02 0. 0. 0.08 0. 0. 0. 0. 0. 0.
0.02 0. 0. 0.02 0. 0. 0. 0. 0. 0. 0.02 0. 0.
0. 0. 0. 0.02 0.02 0.04 0.02 0. 0. 0. ]
2017-08-06 02:00:00 refes: (2823, 86) subjects: (60, 86) auc: 0.580381739473
74
auc_score = 0.497638724911 feature importances: [ 0. 0. 0. 0. 0. 0.02 0.02 0.08 0. 0.08 0.02 0. 0.
0. 0. 0. 0. 0.06 0.04 0. 0. 0. 0. 0. 0. 0.
0.02 0.02 0. 0. 0. 0. 0. 0.04 0.04 0. 0. 0.
0.02 0. 0.02 0. 0.1 0. 0. 0.02 0. 0. 0. 0.
0.02 0.04 0.02 0.04 0. 0. 0.04 0.06 0. 0. 0. 0. 0.
0. 0. 0. 0.06 0. 0. 0.02 0. 0. 0. 0.02 0.
0.02 0. 0. 0. 0.02 0.02 0.02 0. 0. 0. 0. ]
2017-08-06 03:00:00 refes: (2823, 86) subjects: (60, 86) auc: 0.497638724911
75
auc_score = 0.549062049062 feature importances: [ 0. 0. 0. 0. 0. 0.06 0.02 0.18 0.02 0.02 0. 0.
0.02 0. 0. 0. 0. 0.02 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0. 0. 0.02 0. 0. 0.02
0.02 0.02 0. 0. 0. 0.04 0.04 0. 0.02 0. 0.02 0.
0.02 0.02 0. 0.04 0.04 0. 0. 0.12 0.04 0. 0. 0.
0.02 0. 0.02 0. 0. 0.02 0. 0. 0.02 0. 0. 0.
0.02 0. 0. 0. 0. 0. 0.02 0.02 0. 0. 0. 0. 0. ]
2017-08-06 04:00:00 refes: (2823, 86) subjects: (60, 86) auc: 0.549062049062
76
auc_score = 0.526597140234 feature importances: [ 0. 0.02 0.02 0.02 0. 0.02 0.1 0.08 0.02 0.02 0. 0. 0.
0. 0. 0. 0.02 0.04 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0.02 0. 0. 0. 0.04 0.02 0. 0. 0. 0. 0. 0.
0.08 0.02 0.04 0.04 0. 0. 0.04 0.02 0.02 0. 0. 0.02
0. 0. 0. 0. 0.14 0. 0. 0.02 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0.02 0.02 0.02 0. 0. 0. 0. ]
2017-08-06 05:00:00 refes: (2823, 86) subjects: (60, 86) auc: 0.526597140234
77
auc_score = 0.524235865145 feature importances: [ 0. 0. 0.02 0. 0. 0. 0.02 0.04 0.02 0.02 0. 0. 0.
0. 0. 0. 0. 0.04 0.04 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0. 0.02 0.04 0. 0.02 0.
0.02 0. 0.04 0. 0.04 0. 0. 0.02 0. 0. 0. 0.
0.14 0.02 0.08 0.02 0. 0. 0.04 0.02 0. 0. 0. 0.02
0. 0. 0. 0. 0.08 0. 0. 0.04 0. 0. 0. 0.02
0. 0. 0. 0. 0.02 0.02 0.02 0.02 0.02 0. 0. 0. ]
2017-08-06 06:00:00 refes: (2823, 86) subjects: (60, 86) auc: 0.524235865145
78
auc_score = 0.498819362456 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.04 0. 0.02 0.02 0. 0.
0. 0. 0. 0. 0.02 0.04 0. 0. 0.02 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0.04 0.02 0.04 0. 0.02 0.
0.02 0. 0.02 0. 0.04 0. 0. 0.02 0. 0. 0. 0.
0.14 0.08 0.06 0.04 0. 0. 0.02 0.04 0. 0. 0. 0.02
0. 0.02 0. 0. 0.04 0. 0. 0.02 0. 0. 0. 0.02
0. 0. 0. 0. 0. 0.02 0. 0.02 0.02 0.02 0. 0. ]
2017-08-06 07:00:00 refes: (2823, 86) subjects: (60, 86) auc: 0.498819362456
79
auc_score = 0.497638724911 feature importances: [ 0. 0. 0. 0. 0. 0.04 0.02 0.1 0. 0. 0.02 0. 0.
0. 0. 0. 0. 0.04 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0.08 0. 0. 0. 0. 0. 0.02 0. 0. 0.04 0.
0.04 0. 0.04 0. 0.02 0. 0. 0.02 0. 0. 0. 0.
0.06 0.04 0.06 0.02 0. 0. 0.02 0.04 0. 0. 0. 0. 0.
0. 0. 0. 0.04 0. 0. 0.02 0. 0. 0. 0.02 0.
0.02 0. 0. 0.02 0.02 0.06 0.02 0.02 0.02 0. 0. ]
2017-08-06 08:00:00 refes: (2823, 86) subjects: (60, 86) auc: 0.497638724911
80
auc_score = 0.498819362456 feature importances: [ 0. 0. 0.02 0. 0. 0.06 0.02 0.02 0. 0.02 0. 0.02
0. 0. 0. 0. 0.02 0.04 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0. 0. 0.02 0.02 0. 0.02
0. 0.04 0. 0.04 0.02 0.02 0.02 0. 0.02 0. 0.02 0. 0.
0.06 0. 0.06 0. 0. 0. 0.06 0.02 0. 0. 0. 0.02
0. 0. 0. 0. 0.1 0. 0. 0.02 0. 0. 0. 0.02
0. 0. 0. 0. 0. 0.02 0.02 0.06 0.02 0.02 0. 0. ]
2017-08-06 09:00:00 refes: (2823, 86) subjects: (60, 86) auc: 0.498819362456
81
auc_score = 0.552603961695 feature importances: [ 0. 0. 0. 0. 0. 0.06 0. 0.06 0.02 0.02 0. 0. 0.
0. 0. 0. 0.02 0.04 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0. 0.02 0.04 0. 0.02 0.
0.04 0.02 0. 0. 0.06 0. 0. 0.02 0. 0. 0. 0.
0.14 0.02 0.08 0.02 0. 0. 0.06 0.06 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0.02 0. 0. 0. 0.02 0. 0.
0.02 0. 0. 0.02 0. 0.02 0.02 0. 0.04 0. ]
2017-08-06 10:00:00 refes: (2823, 86) subjects: (60, 86) auc: 0.552603961695
82
auc_score = 0.526006821461 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.04 0. 0.02 0. 0. 0.
0. 0. 0. 0.02 0. 0.04 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0. 0.02 0.06 0. 0.02 0.
0.02 0. 0.02 0. 0.1 0. 0. 0.02 0. 0. 0. 0. 0.1
0. 0.04 0.08 0. 0. 0.04 0.06 0. 0. 0.02 0.02 0.
0.06 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.02 0. 0.
0.02 0. 0.02 0.02 0.02 0.02 0.02 0.02 0. 0. ]
2017-08-06 11:00:00 refes: (2823, 86) subjects: (60, 86) auc: 0.526006821461
83
auc_score = 0.497638724911 feature importances: [ 0. 0. 0. 0. 0. 0.06 0.02 0.02 0.02 0. 0.02 0. 0.
0. 0. 0. 0. 0.06 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0. 0.02 0.04 0. 0. 0.02
0.06 0. 0.04 0. 0.02 0.02 0. 0.02 0. 0. 0. 0. 0.1
0.02 0.06 0.04 0. 0. 0.02 0. 0. 0. 0. 0.02 0. 0.
0. 0. 0.08 0. 0. 0.02 0. 0. 0. 0. 0. 0.
0.02 0. 0.02 0.02 0.02 0.04 0.02 0. 0. 0.02]
2017-08-06 12:00:00 refes: (2823, 86) subjects: (60, 86) auc: 0.497638724911
84
auc_score = 0.525416502689 feature importances: [ 0. 0. 0. 0. 0. 0.04 0. 0.04 0.02 0.02 0.02 0.02
0.02 0. 0. 0. 0. 0.04 0. 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0. 0.02 0. 0.02 0. 0.02
0. 0.02 0. 0. 0. 0.04 0.02 0. 0.02 0. 0. 0. 0.
0.12 0.02 0.1 0. 0. 0. 0.02 0.04 0. 0. 0. 0.02
0. 0. 0. 0. 0.1 0. 0. 0. 0.02 0. 0. 0.02
0. 0.02 0. 0. 0. 0.02 0.02 0.02 0.02 0. 0. 0.04]
2017-08-06 13:00:00 refes: (2823, 86) subjects: (60, 86) auc: 0.525416502689
85
auc_score = 0.526597140234 feature importances: [ 0. 0. 0. 0. 0. 0.02 0.02 0.06 0. 0.02 0. 0. 0.
0. 0. 0. 0. 0.06 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0. 0. 0. 0. 0.02 0.
0.02 0. 0.02 0.02 0.04 0. 0. 0.02 0. 0. 0. 0. 0.2
0.02 0.08 0.02 0. 0. 0.02 0.04 0. 0. 0. 0.02 0. 0.
0. 0. 0.16 0. 0. 0. 0. 0. 0. 0.02 0. 0. 0.
0. 0. 0.02 0.02 0. 0.02 0.02 0. 0. ]
2017-08-06 14:00:00 refes: (2823, 86) subjects: (60, 86) auc: 0.526597140234
86
auc_score = 0.578610783156 feature importances: [ 0. 0. 0. 0. 0. 0.06 0. 0.14 0. 0.02 0. 0. 0.
0. 0. 0. 0. 0.02 0. 0. 0. 0.02 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0. 0.02 0. 0. 0.02 0.
0.06 0. 0. 0. 0.04 0. 0. 0. 0. 0. 0. 0.
0.04 0.06 0.12 0.02 0. 0. 0. 0.02 0. 0. 0. 0.02
0. 0. 0. 0. 0.14 0. 0. 0. 0. 0. 0. 0.02
0. 0. 0. 0. 0.02 0. 0.06 0.02 0. 0.02 0.02 0. ]
2017-08-06 15:00:00 refes: (2823, 86) subjects: (60, 86) auc: 0.578610783156
87
auc_score = 0.579791420701 feature importances: [ 0. 0. 0.02 0. 0. 0.04 0. 0. 0.04 0.02 0. 0. 0.
0. 0. 0. 0.02 0.06 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0. 0. 0.02 0. 0. 0.
0.02 0. 0. 0. 0.02 0. 0. 0.02 0. 0. 0. 0.
0.22 0.02 0.08 0.02 0. 0. 0.06 0.02 0. 0. 0. 0.02
0. 0. 0. 0. 0.08 0. 0. 0.02 0. 0. 0. 0.02
0. 0. 0. 0. 0.04 0.02 0. 0.06 0. 0. 0. 0. ]
2017-08-06 16:00:00 refes: (2823, 86) subjects: (60, 86) auc: 0.579791420701
88
auc_score = 0.526006821461 feature importances: [ 0. 0. 0. 0. 0. 0. 0.02 0.06 0.02 0.02 0. 0.
0.04 0. 0. 0.04 0.02 0.04 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0. 0. 0.02 0.02 0. 0. 0.
0.02 0. 0.02 0. 0. 0. 0. 0.02 0. 0. 0. 0.
0.12 0.02 0.06 0.1 0. 0. 0.04 0.02 0. 0. 0. 0. 0.
0. 0. 0. 0.08 0. 0. 0.02 0. 0. 0. 0. 0.
0.02 0. 0. 0.02 0. 0.06 0.02 0.02 0. 0. 0. ]
2017-08-06 17:00:00 refes: (2823, 86) subjects: (60, 86) auc: 0.526006821461
89
auc_score = 0.495867768595 feature importances: [ 0. 0. 0. 0. 0. 0.06 0.02 0. 0.02 0.04 0.02 0.
0.02 0. 0.02 0. 0.02 0.02 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0.02 0. 0. 0. 0. 0.02 0.02 0. 0.02
0. 0.04 0.02 0. 0. 0.04 0.02 0. 0.02 0. 0. 0. 0.
0.16 0.02 0.08 0.04 0. 0. 0.02 0. 0. 0. 0. 0.02
0. 0. 0. 0. 0.04 0. 0. 0.02 0. 0. 0. 0.02
0. 0. 0. 0. 0. 0.02 0. 0.02 0.02 0.02 0. 0. ]
2017-08-06 18:00:00 refes: (2823, 86) subjects: (60, 86) auc: 0.495867768595
90
auc_score = 0.527187459006 feature importances: [ 0. 0. 0. 0. 0. 0.04 0. 0.04 0.02 0. 0.02 0. 0.
0. 0.02 0.02 0.02 0. 0.04 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0.02 0. 0. 0. 0.08 0. 0.02 0.
0.02 0. 0.02 0. 0.1 0.02 0. 0.02 0. 0. 0. 0. 0.1
0.02 0.06 0.04 0.04 0. 0.02 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.04 0. 0. 0. 0.02 0. 0. 0. 0. 0.
0.02 0. 0. 0. 0.04 0.02 0.02 0. 0. 0. ]
2017-08-06 19:00:00 refes: (2823, 86) subjects: (60, 86) auc: 0.527187459006
91
auc_score = 0.526597140234 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.04 0.02 0.02 0. 0. 0.
0. 0. 0. 0. 0.06 0.04 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0.02 0. 0. 0.02 0.
0.02 0. 0.02 0. 0.1 0.04 0. 0.02 0. 0. 0. 0.
0.12 0.04 0.06 0.02 0. 0. 0.08 0.04 0. 0. 0. 0. 0.
0.02 0. 0. 0.06 0. 0. 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0.02 0. 0.02 0.02 0.02 0. 0. ]
2017-08-06 20:00:00 refes: (2823, 86) subjects: (60, 86) auc: 0.526597140234
92
auc_score = 0.497048406139 feature importances: [ 0. 0. 0.02 0. 0. 0.02 0. 0.02 0. 0.02 0. 0. 0.
0. 0. 0. 0. 0.04 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0. 0.02 0.02 0. 0.02 0.
0.04 0. 0.02 0. 0.12 0.02 0. 0.02 0. 0. 0. 0. 0.1
0. 0.06 0.02 0. 0. 0.04 0.02 0. 0. 0. 0.02 0. 0.
0. 0. 0.14 0. 0. 0.02 0. 0. 0. 0.02 0. 0.
0.02 0. 0. 0.02 0.02 0.06 0. 0. 0. 0. ]
2017-08-06 21:00:00 refes: (2823, 86) subjects: (60, 86) auc: 0.497048406139
93
auc_score = 0.524235865145 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.04 0. 0.02 0. 0.
0.04 0.02 0. 0. 0.02 0.02 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.04 0. 0. 0. 0. 0.02 0.02 0.04 0. 0.02
0. 0.02 0. 0.02 0. 0.08 0.02 0. 0. 0. 0. 0. 0.
0.16 0.04 0.04 0.02 0. 0. 0.06 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.06 0. 0. 0. 0.02 0. 0. 0.02 0. 0.
0. 0. 0.02 0. 0.04 0.02 0. 0.02 0. 0. ]
2017-08-06 22:00:00 refes: (2823, 86) subjects: (60, 86) auc: 0.524235865145
94
auc_score = 0.499409681228 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.04 0. 0.02 0. 0. 0.
0. 0. 0. 0. 0.04 0.02 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0.02 0.02 0. 0.02 0.
0.04 0. 0.02 0. 0.06 0. 0. 0.02 0. 0. 0. 0.
0.18 0.02 0.02 0.04 0. 0. 0.06 0.04 0. 0. 0. 0.02
0. 0. 0. 0. 0.16 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.02 0.04 0.02 0. 0.02 0. 0. ]
2017-08-06 23:00:00 refes: (2823, 86) subjects: (59, 86) auc: 0.499409681228
95
auc_score = 0.5 feature importances: [ 0. 0. 0.02 0. 0. 0.04 0. 0.04 0.04 0.02 0.04 0. 0.
0. 0. 0. 0.02 0.04 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0. 0. 0.04 0. 0.04 0.02
0.02 0. 0.04 0. 0.04 0. 0.02 0.02 0. 0. 0. 0.
0.04 0.04 0.04 0.04 0. 0. 0.04 0.04 0. 0. 0. 0. 0.
0.02 0. 0. 0.04 0. 0. 0. 0. 0. 0. 0. 0. 0.
0.02 0. 0. 0.04 0.04 0.04 0. 0. 0.02 0. ]
2017-08-07 00:00:00 refes: (2822, 86) subjects: (18, 86) auc: 0.5
96
auc_score = 0.55496031746 feature importances: [ 0. 0. 0. 0. 0. 0. 0. 0.04 0. 0. 0. 0.
0.02 0.02 0. 0. 0. 0.04 0. 0. 0. 0. 0. 0. 0.
0. 0. 0.1 0. 0. 0. 0. 0. 0. 0.04 0. 0.04
0. 0.02 0. 0.02 0.02 0.04 0.02 0. 0.02 0. 0. 0. 0.
0.14 0.02 0.06 0.02 0. 0. 0.04 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.14 0. 0. 0.04 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.02 0.04 0.02 0. 0.02 0. 0. ]
2017-08-07 01:00:00 refes: (2799, 86) subjects: (60, 86) auc: 0.55496031746
97
auc_score = 0.550198412698 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.04 0. 0.02 0.02 0. 0.
0.02 0. 0. 0.02 0.06 0.04 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0. 0.02 0.02 0. 0.06 0.
0.02 0.02 0. 0. 0.02 0. 0. 0.02 0. 0. 0. 0. 0.1
0.08 0.04 0.06 0. 0. 0.06 0.04 0. 0. 0. 0.02 0. 0.
0. 0. 0.04 0. 0. 0. 0. 0. 0. 0. 0. 0.
0.02 0. 0.02 0.02 0. 0.02 0.02 0.02 0. 0. ]
2017-08-07 02:00:00 refes: (2799, 86) subjects: (60, 86) auc: 0.550198412698
98
auc_score = 0.498214285714 feature importances: [ 0. 0. 0. 0. 0.02 0.04 0. 0.04 0.02 0. 0.02 0. 0.
0.02 0. 0. 0.02 0.08 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0.02 0.02 0.02 0. 0. 0.06 0.
0.06 0.02 0.02 0. 0.04 0.02 0. 0. 0. 0. 0. 0.
0.14 0.02 0.04 0.02 0. 0. 0.02 0.02 0. 0. 0. 0. 0.
0. 0. 0. 0.08 0. 0. 0. 0. 0. 0. 0.02 0. 0.
0. 0. 0. 0.02 0.02 0.02 0. 0.02 0. 0. ]
2017-08-07 03:00:00 refes: (2799, 86) subjects: (60, 86) auc: 0.498214285714
99
auc_score = 0.499404761905 feature importances: [ 0. 0. 0.02 0. 0. 0.04 0.02 0.1 0. 0.02 0.02 0. 0.
0. 0. 0. 0. 0.04 0.02 0.02 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0.02 0.04 0.02 0.02 0. 0. 0.
0.02 0. 0.02 0.04 0.04 0.02 0. 0.02 0. 0. 0. 0. 0.1
0.02 0.04 0.02 0. 0. 0.02 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.04 0. 0. 0.02 0. 0. 0. 0.02 0. 0.
0.02 0. 0.02 0.02 0.02 0.02 0. 0.02 0. 0. ]
2017-08-07 04:00:00 refes: (2799, 86) subjects: (60, 86) auc: 0.499404761905
100
auc_score = 0.49880952381 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.04 0. 0.02 0. 0. 0.
0.04 0. 0. 0. 0.02 0.04 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0.08 0. 0. 0. 0.02 0.02 0.04 0. 0.02 0.
0.02 0. 0. 0. 0.02 0. 0. 0.02 0. 0. 0. 0.
0.04 0.02 0.04 0.16 0. 0. 0.04 0.02 0. 0. 0. 0.02
0. 0. 0. 0. 0.06 0. 0. 0.02 0. 0. 0. 0.02
0. 0. 0.04 0. 0.02 0. 0.02 0.02 0. 0. 0.02 0. ]
2017-08-07 05:00:00 refes: (2799, 86) subjects: (60, 86) auc: 0.49880952381
101
auc_score = 0.524801587302 feature importances: [ 0. 0. 0.02 0. 0. 0.02 0. 0.04 0. 0.02 0. 0. 0.
0. 0. 0. 0. 0.04 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0.06 0. 0. 0. 0. 0. 0.02 0.02 0. 0. 0.
0.02 0. 0.02 0.04 0.06 0. 0. 0.02 0. 0. 0. 0.
0.06 0.02 0.04 0.12 0. 0. 0.02 0.04 0. 0. 0. 0.02
0. 0.02 0. 0. 0.1 0. 0. 0.04 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0.02 0.02 0.04 0. 0. 0. 0. ]
2017-08-07 06:00:00 refes: (2799, 86) subjects: (60, 86) auc: 0.524801587302
102
auc_score = 0.522420634921 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.02 0. 0. 0. 0.
0.02 0. 0. 0.04 0. 0.02 0.04 0.02 0. 0. 0. 0. 0.
0. 0. 0.04 0. 0. 0. 0. 0. 0. 0.02 0. 0. 0.
0.04 0.02 0.02 0. 0.02 0. 0. 0.02 0. 0. 0. 0.
0.12 0. 0.14 0.1 0. 0. 0.02 0.04 0. 0. 0. 0. 0.
0. 0. 0. 0.1 0. 0. 0. 0.02 0. 0. 0. 0. 0.
0.02 0. 0.02 0. 0.02 0.02 0. 0.02 0. 0. ]
2017-08-07 07:00:00 refes: (2799, 86) subjects: (60, 86) auc: 0.522420634921
103
auc_score = 0.499404761905 feature importances: [ 0. 0. 0.02 0. 0. 0.02 0.02 0.04 0.02 0. 0. 0. 0.
0. 0. 0. 0.02 0.02 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0.02 0.02 0.02 0.02 0. 0. 0.
0.02 0. 0.02 0. 0.1 0.02 0. 0.02 0. 0. 0. 0.
0.14 0. 0.04 0.06 0. 0. 0.04 0.04 0. 0. 0. 0.02
0. 0. 0. 0. 0.08 0. 0. 0. 0.02 0. 0. 0. 0.
0. 0.02 0. 0. 0.02 0.04 0.02 0. 0. 0. 0. ]
2017-08-07 08:00:00 refes: (2799, 86) subjects: (60, 86) auc: 0.499404761905
104
auc_score = 0.527182539683 feature importances: [ 0. 0. 0. 0. 0. 0.04 0. 0.02 0.02 0.02 0. 0. 0.
0.02 0. 0. 0.02 0.02 0.02 0.02 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0. 0. 0.02 0. 0.02 0.06
0.02 0. 0.02 0. 0.02 0. 0. 0. 0. 0. 0. 0.
0.08 0.06 0.06 0.08 0. 0. 0.02 0.02 0. 0. 0. 0.02
0. 0. 0. 0. 0.12 0. 0. 0.02 0. 0. 0. 0. 0.
0. 0. 0. 0.02 0. 0.06 0.02 0. 0.02 0.02 0. ]
2017-08-07 09:00:00 refes: (2799, 86) subjects: (60, 86) auc: 0.527182539683
105
auc_score = 0.49880952381 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.02 0.02 0.02 0. 0. 0.
0.02 0. 0. 0. 0.04 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0. 0. 0.04 0. 0.04 0.
0.02 0. 0.02 0. 0.08 0. 0.02 0.02 0. 0. 0. 0. 0.1
0.02 0.08 0. 0. 0. 0.02 0.04 0. 0. 0. 0. 0.
0.02 0. 0. 0.14 0. 0. 0.02 0.04 0. 0. 0. 0. 0.
0. 0. 0.02 0.02 0.02 0.02 0. 0.02 0. 0. ]
2017-08-07 10:00:00 refes: (2799, 86) subjects: (60, 86) auc: 0.49880952381
106
auc_score = 0.552579365079 feature importances: [ 0. 0. 0. 0. 0. 0.02 0.02 0.04 0.02 0.02 0. 0. 0.
0. 0. 0.02 0. 0.04 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0. 0. 0.02 0. 0. 0.
0.02 0. 0.02 0. 0.06 0. 0. 0.02 0. 0. 0. 0.
0.14 0.08 0.04 0.06 0. 0. 0.02 0.02 0. 0. 0. 0.02
0. 0. 0. 0. 0.04 0. 0. 0.08 0.02 0. 0. 0. 0.
0. 0.02 0. 0.04 0.02 0.02 0.02 0. 0.02 0. 0. ]
2017-08-07 11:00:00 refes: (2799, 86) subjects: (60, 86) auc: 0.552579365079
107
auc_score = 0.579761904762 feature importances: [ 0. 0. 0. 0. 0. 0. 0. 0.02 0.02 0. 0. 0. 0.
0.02 0. 0. 0. 0.04 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0. 0. 0.02 0. 0.04 0.02
0.02 0. 0.02 0. 0.08 0. 0. 0.02 0. 0. 0. 0.
0.12 0.1 0.06 0.04 0. 0. 0.06 0.02 0. 0. 0. 0.02
0. 0. 0. 0. 0.04 0. 0. 0.04 0. 0. 0. 0.04
0. 0. 0. 0. 0.02 0.02 0. 0.02 0. 0.04 0. 0. ]
2017-08-07 12:00:00 refes: (2799, 86) subjects: (60, 86) auc: 0.579761904762
108
auc_score = 0.690873015873 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.02 0.02 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0.02 0. 0. 0. 0. 0. 0.02 0.04 0. 0.02 0. 0.02
0. 0. 0. 0. 0. 0.08 0. 0. 0. 0. 0. 0.02
0. 0.22 0.04 0.04 0.06 0. 0. 0.1 0.02 0. 0. 0. 0.
0. 0.02 0. 0. 0.08 0. 0. 0.02 0. 0. 0. 0.02
0. 0. 0. 0. 0. 0.02 0.04 0. 0.02 0.02 0. 0. ]
2017-08-07 13:00:00 refes: (2799, 86) subjects: (60, 86) auc: 0.690873015873
109
auc_score = 0.802579365079 feature importances: [ 0. 0. 0. 0. 0. 0.02 0.02 0.08 0. 0.02 0. 0. 0.
0. 0. 0. 0. 0.06 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.06 0.
0.02 0. 0. 0. 0.12 0. 0.02 0. 0. 0. 0. 0.
0.08 0.26 0.06 0.02 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.1 0. 0. 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.02 0. 0.02 0. 0. 0. 0. ]
2017-08-07 14:00:00 refes: (2799, 86) subjects: (60, 86) auc: 0.802579365079
110
auc_score = 0.71746031746 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.04 0. 0. 0. 0. 0.
0. 0. 0. 0. 0.06 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0.04 0. 0. 0. 0. 0. 0. 0.02 0. 0.02 0.
0.02 0. 0.02 0. 0.1 0. 0.02 0. 0. 0. 0. 0.
0.14 0.22 0.06 0.02 0. 0. 0.02 0. 0. 0. 0. 0.02
0. 0.02 0. 0. 0.06 0. 0. 0. 0. 0. 0. 0.02
0. 0. 0. 0. 0. 0. 0.02 0. 0. 0.02 0. 0. ]
2017-08-07 15:00:00 refes: (2799, 86) subjects: (60, 86) auc: 0.71746031746
111
auc_score = 0.693253968254 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.06 0. 0.04 0. 0. 0.
0. 0. 0. 0. 0.06 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0. 0. 0.02 0. 0.02 0.
0.04 0. 0.04 0. 0.1 0. 0.02 0. 0. 0. 0. 0.
0.08 0.22 0.02 0.02 0. 0. 0.02 0.02 0. 0. 0. 0. 0.
0. 0. 0. 0.08 0. 0. 0.02 0. 0. 0. 0.02 0. 0.
0. 0. 0. 0.02 0. 0.02 0. 0. 0. 0. ]
2017-08-07 16:00:00 refes: (2799, 86) subjects: (60, 86) auc: 0.693253968254
112
auc_score = 0.581547619048 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.04 0.02 0. 0. 0.02
0. 0. 0. 0. 0. 0.06 0.02 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0. 0. 0. 0.02 0. 0.02
0. 0.08 0. 0.02 0. 0.06 0. 0.02 0. 0. 0. 0. 0.
0.12 0.08 0.06 0.02 0. 0. 0.04 0. 0. 0. 0. 0.02
0. 0. 0. 0. 0.08 0. 0. 0.04 0. 0. 0. 0.02
0. 0. 0. 0. 0. 0.02 0.04 0.04 0. 0. 0. 0. ]
2017-08-07 17:00:00 refes: (2799, 86) subjects: (60, 86) auc: 0.581547619048
113
auc_score = 0.578571428571 feature importances: [ 0. 0. 0.02 0. 0. 0.02 0. 0.1 0. 0.02 0. 0.02
0. 0. 0. 0. 0. 0.06 0. 0. 0. 0. 0. 0. 0.
0. 0. 0.02 0. 0. 0. 0. 0. 0. 0.02 0. 0.02
0. 0.08 0. 0.02 0. 0.06 0. 0.02 0.02 0. 0. 0. 0.
0.1 0.14 0.04 0.02 0. 0. 0.06 0. 0. 0. 0. 0.02
0. 0.02 0. 0. 0.04 0. 0. 0. 0.02 0. 0. 0.02
0. 0. 0. 0. 0. 0. 0. 0.02 0. 0. 0. 0. ]
2017-08-07 18:00:00 refes: (2799, 86) subjects: (60, 86) auc: 0.578571428571
114
auc_score = 0.526587301587 feature importances: [ 0. 0. 0. 0. 0. 0.04 0. 0.06 0. 0.02 0. 0. 0.
0. 0. 0. 0. 0.06 0.02 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0. 0. 0. 0. 0. 0.02 0. 0.02 0.
0.06 0. 0. 0.02 0.04 0.02 0.02 0.02 0. 0. 0. 0.
0.08 0.04 0.02 0.04 0. 0. 0.02 0.04 0. 0. 0. 0. 0.
0. 0. 0. 0.14 0. 0. 0.06 0. 0. 0. 0.02 0. 0.
0.02 0. 0. 0.02 0.02 0.02 0. 0.02 0. 0. ]
2017-08-07 19:00:00 refes: (2799, 86) subjects: (60, 86) auc: 0.526587301587
115
auc_score = 0.49880952381 feature importances: [ 0. 0. 0. 0. 0. 0. 0. 0.04 0. 0.04 0. 0. 0.
0. 0. 0. 0. 0.02 0. 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0.04 0. 0. 0. 0. 0. 0.02 0. 0.02 0.
0.02 0. 0.02 0. 0.06 0.02 0. 0. 0. 0. 0.02 0.
0.12 0.06 0.08 0.02 0. 0. 0.02 0.02 0. 0. 0. 0. 0.
0. 0. 0. 0.1 0. 0. 0.06 0. 0. 0. 0.06 0. 0.
0.02 0. 0. 0.02 0.04 0.02 0. 0.02 0. 0. ]
2017-08-07 20:00:00 refes: (2799, 86) subjects: (60, 86) auc: 0.49880952381
116
auc_score = 0.775992063492 feature importances: [ 0. 0. 0. 0. 0. 0.06 0. 0.06 0.02 0. 0. 0.
0.02 0. 0. 0. 0. 0.1 0.02 0. 0. 0.04 0. 0. 0.
0. 0. 0. 0. 0.04 0. 0. 0. 0. 0.02 0. 0.02
0. 0.02 0. 0. 0. 0.1 0. 0. 0. 0. 0. 0. 0.
0.2 0.02 0.02 0.02 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.02 0. 0. 0.08 0. 0. 0. 0.02 0. 0.
0. 0. 0. 0. 0.02 0.06 0. 0.02 0. 0. ]
2017-08-07 21:00:00 refes: (2799, 86) subjects: (60, 86) auc: 0.775992063492
117
auc_score = 0.858134920635 feature importances: [ 0. 0. 0. 0. 0. 0.02 0. 0.06 0.02 0.04 0. 0. 0.
0. 0. 0. 0. 0.04 0.04 0. 0. 0. 0. 0. 0. 0.
0. 0.02 0. 0.12 0. 0.02 0.02 0.02 0.02 0. 0.02 0.
0.02 0. 0. 0. 0.02 0. 0.02 0. 0. 0. 0. 0.
0.06 0.04 0.1 0.02 0. 0. 0.04 0. 0. 0. 0. 0.02
0. 0.02 0. 0. 0.02 0. 0. 0.04 0.02 0. 0. 0. 0.
0. 0. 0. 0. 0.02 0.02 0.02 0.02 0.02 0. 0. ]
2017-08-07 22:00:00 refes: (2799, 86) subjects: (60, 86) auc: 0.858134920635
In [13]:
df.plot(figsize=(20,7))
Out[13]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f318032ca58>
In [14]:
fig, ax = plt.subplots(figsize=(20,7))
auc_df['Detected'] = 0
auc_df.loc[auc_df.auc_score>cut, ['Detected']]=1
ax.plot( auc_df.auc_score,'g')
ax.fill( auc_df.Detected, 'b', alpha=0.3)
ax.legend(loc='upper left')
plt.show()
In [ ]:
Content source: ivukotic/ML_platform_tests
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