In [1]:

    

# from __future__ import print_function, division
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
import pandas as pd
import operator
from scipy import interp
from itertools import cycle
from sklearn import svm
from sklearn.utils.validation import check_random_state
from sklearn.model_selection import StratifiedKFold, cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import roc_curve, auc
from statsmodels.graphics.mosaicplot import mosaic
print(__doc__)


    

    




Automatically created module for IPython interactive environment

In [2]:

    

from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import AdaBoostRegressor
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import BaggingRegressor
from sklearn.linear_model import LinearRegression
from sklearn.neighbors import KNeighborsRegressor
# from sklearn import svm


    

In [3]:

    

df_wnv_raw = pd.read_csv('bid_v007_ReScale.csv', encoding='utf-8') 
df_wnv_raw.head()


    

    
Out[3]:







  

    

      

      
target.7sec
      
ccyyy.mm.curr
      
ccyyy.mm.prev
      
bid.time
      
bid.sec
      
month
      
bid.curr.mth
      
increment.curr.mth
      
MV.3sec.curr
      
MV.5sec.curr
      
...
      
R01_Volume.Bidder.curr.mth
      
R01_success.ratio.curr.mth
      
R01_Volume.Plate.prev.mth
      
R01_Volume.Bidder.prev.mth
      
R01_success.ratio.prev.mth
      
R01_d.Volume.Plate
      
R01_d.Volume.Bidder
      
R01_d.success.ratio
      
R01_anomaly
      
R01_target.7sec
    
  
  

    

      
0
      
0
      
2016-01
      
2015-12
      
11:29:35
      
35
      
1
      
81800
      
0
      
0.0
      
0.0
      
...
      
0.0
      
0.678037
      
0.0
      
0.0
      
0.290318
      
0.675811
      
0.611385
      
0.867524
      
0.0
      
0.000000
    
    

      
1
      
0
      
2016-01
      
2015-12
      
11:29:36
      
36
      
1
      
81800
      
0
      
0.0
      
0.0
      
...
      
0.0
      
0.678037
      
0.0
      
0.0
      
0.290318
      
0.675811
      
0.611385
      
0.867524
      
0.0
      
0.000000
    
    

      
2
      
100
      
2016-01
      
2015-12
      
11:29:37
      
37
      
1
      
81800
      
0
      
0.0
      
0.0
      
...
      
0.0
      
0.678037
      
0.0
      
0.0
      
0.290318
      
0.675811
      
0.611385
      
0.867524
      
0.0
      
0.071429
    
    

      
3
      
100
      
2016-01
      
2015-12
      
11:29:38
      
38
      
1
      
81800
      
0
      
0.0
      
0.0
      
...
      
0.0
      
0.678037
      
0.0
      
0.0
      
0.290318
      
0.675811
      
0.611385
      
0.867524
      
0.0
      
0.071429
    
    

      
4
      
100
      
2016-01
      
2015-12
      
11:29:39
      
39
      
1
      
81800
      
0
      
0.0
      
0.0
      
...
      
0.0
      
0.678037
      
0.0
      
0.0
      
0.290318
      
0.675811
      
0.611385
      
0.867524
      
0.0
      
0.071429
    
  

5 rows × 59 columns

In [76]:

    

X = df_wnv_raw[[
'R01_bid.sec',
'R01_month',
'R01_bid.curr.mth',
'R01_increment.curr.mth',
'R01_MV.3sec.curr',
'R01_MV.5sec.curr',
'R01_MV.3sec.curr.d.Avg.Low.prev.mth',
'R01_MV.5sec.curr.d.Avg.Low.prev.mth',
'R01_bid.prev.mth',
'R01_increment.prev.mth',
'R01_MV.3sec.prev',
'R01_MV.5sec.prev',
'R01_MV.3sec.prev.d.Avg.Low.prev.mth',
'R01_MV.5sec.prev.d.Avg.Low.prev.mth',
'R01_d.Avg.Low.prev.mth',
'R01_increment.curr.mth.d.Avg.Low.prev.mth',
'R01_d.earliest.success.time.sec.prev.mth',
'R01_Volume.Plate.curr.mth',
'R01_Volume.Bidder.curr.mth',
'R01_success.ratio.curr.mth',
'R01_Volume.Plate.prev.mth',
'R01_Volume.Bidder.prev.mth',
'R01_success.ratio.prev.mth',
'R01_d.Volume.Plate',
'R01_d.Volume.Bidder',
'R01_d.success.ratio',
'R01_anomaly',
# 'R01_target.7sec'
        ]]

X_col = X.columns # get the column list
# X = StandardScaler().fit_transform(X.as_matrix())
X = X.as_matrix()
y = df_wnv_raw[['R01_target.7sec']].as_matrix().reshape(len(df_wnv_raw),)


    

In [77]:

    

X_col


    

    
Out[77]:





Index(['R01_bid.sec', 'R01_month', 'R01_bid.curr.mth',
       'R01_increment.curr.mth', 'R01_MV.3sec.curr', 'R01_MV.5sec.curr',
       'R01_MV.3sec.curr.d.Avg.Low.prev.mth',
       'R01_MV.5sec.curr.d.Avg.Low.prev.mth', 'R01_bid.prev.mth',
       'R01_increment.prev.mth', 'R01_MV.3sec.prev', 'R01_MV.5sec.prev',
       'R01_MV.3sec.prev.d.Avg.Low.prev.mth',
       'R01_MV.5sec.prev.d.Avg.Low.prev.mth', 'R01_d.Avg.Low.prev.mth',
       'R01_increment.curr.mth.d.Avg.Low.prev.mth',
       'R01_d.earliest.success.time.sec.prev.mth', 'R01_Volume.Plate.curr.mth',
       'R01_Volume.Bidder.curr.mth', 'R01_success.ratio.curr.mth',
       'R01_Volume.Plate.prev.mth', 'R01_Volume.Bidder.prev.mth',
       'R01_success.ratio.prev.mth', 'R01_d.Volume.Plate',
       'R01_d.Volume.Bidder', 'R01_d.success.ratio', 'R01_anomaly'],
      dtype='object')

In [78]:

    

rng = check_random_state(0)


    

In [79]:

    

# GB
# classifier_GB = GradientBoostingClassifier(n_estimators=500, # score:  (AUC 0.77018), learning_rate=0.005, max_features=5
# classifier_GB = GradientBoostingClassifier(n_estimators=500, # score:  (AUC 0.80), learning_rate=0.003, max_features=8
classifier_GB = GradientBoostingClassifier(n_estimators=500, # score: 0.94608 (AUC 0.81419), learning_rate=0.001, max_features=8 <<< Best
# classifier_GB = GradientBoostingClassifier(n_estimators=500, # score:  (AUC 0.82), learning_rate=0.0005, max_features=8
# classifier_GB = GradientBoostingClassifier(n_estimators=500, # score:  (AUC 0.82), learning_rate=0.0001, max_features=8
# classifier_GB = GradientBoostingClassifier(n_estimators=1000, # score:  (AUC 0.82), learning_rate=0.0001, max_features=8
# classifier_GB = GradientBoostingClassifier(n_estimators=1000, # score:  (AUC 0.82), learning_rate=0.0005, max_features=8
# classifier_GB = GradientBoostingClassifier(n_estimators=1000, # score: 0.78735 (AUC 0.76840), learning_rate=0.0005, max_features=8
# classifier_GB = GradientBoostingClassifier(n_estimators=200, # score: 0.94608 (AUC 0.78), learning_rate=0.0005, max_depth=4, min_samples_split=30, max_features=5
# classifier_GB = GradientBoostingClassifier(n_estimators=500, # score: 0.66030, default learning_rate=0.1
# classifier_GB = GradientBoostingClassifier(n_estimators=500, # score: 0.88464 (AUC 0.80), learning_rate=0.0035
# classifier_GB = GradientBoostingClassifier(n_estimators=500, # score: 0.85208 (AUC 0.72), learning_rate=0.0035, max_depth=5, min_samples_split=20, max_features=8
# classifier_GB = GradientBoostingClassifier(n_estimators=500, # score: 0.90691 (AUC 0.80), learning_rate=0.002
# classifier_GB = GradientBoostingClassifier(n_estimators=500, # score: 0.94608, learning_rate=0.0005, max_depth=5, min_samples_split=20
# classifier_GB = GradientBoostingClassifier(n_estimators=500, # score: 0.94608, learning_rate=0.0005, max_depth=5, min_samples_split=30
# classifier_GB = GradientBoostingClassifier(n_estimators=500, # score: 0.94608, learning_rate=0.0005, max_depth=5, min_samples_split=30, max_features=10
# classifier_GB = GradientBoostingClassifier(n_estimators=500, # score: 0.94608, learning_rate=0.0005, max_depth=5, min_samples_split=30, max_features=5
# classifier_GB = GradientBoostingClassifier(n_estimators=500, # score: 0.94608, learning_rate=0.0005, max_depth=4, min_samples_split=30, max_features=5
# classifier_GB = GradientBoostingClassifier(n_estimators=1000, # score: 0.79112 (AUC 0.70), learning_rate=0.0035, max_depth=5, min_samples_split=30, max_features=10
# classifier_GB = GradientBoostingClassifier(n_estimators=1000, # score: 0.94608, learning_rate=0.0005, max_depth=5, min_samples_split=30, max_features=5
# classifier_GB = GradientBoostingClassifier(n_estimators=1000, # score: 0.63623, default learning_rate=0.1
# classifier_GB = GradientBoostingClassifier(n_estimators=2000, # score: 0.77567, learning_rate=0.0035
#                                    loss='deviance',
#                                    subsample=1,
#                                    max_depth=5,
#                                    min_samples_split=20,
                                   learning_rate=0.001,
                                   max_features=8,
                                   random_state=rng)


    

In [144]:

    

# GB
# classifier_GB = GradientBoostingClassifier(n_estimators=500, # score:  (AUC 0.77018), learning_rate=0.005, max_features=5
# classifier_GB = GradientBoostingClassifier(n_estimators=500, # score:  (AUC 0.80), learning_rate=0.003, max_features=8
classifier_GB = GradientBoostingRegressor(n_estimators=1500, # score: 0.94608 (AUC 0.81419), learning_rate=0.001, max_features=8 <<< Best
# classifier_GB = GradientBoostingClassifier(n_estimators=500, # score:  (AUC 0.82), learning_rate=0.0005, max_features=8
# classifier_GB = GradientBoostingClassifier(n_estimators=500, # score:  (AUC 0.82), learning_rate=0.0001, max_features=8
# classifier_GB = GradientBoostingClassifier(n_estimators=1000, # score:  (AUC 0.82), learning_rate=0.0001, max_features=8
# classifier_GB = GradientBoostingClassifier(n_estimators=1000, # score:  (AUC 0.82), learning_rate=0.0005, max_features=8
# classifier_GB = GradientBoostingClassifier(n_estimators=1000, # score: 0.78735 (AUC 0.76840), learning_rate=0.0005, max_features=8
# classifier_GB = GradientBoostingClassifier(n_estimators=200, # score: 0.94608 (AUC 0.78), learning_rate=0.0005, max_depth=4, min_samples_split=30, max_features=5
# classifier_GB = GradientBoostingClassifier(n_estimators=500, # score: 0.66030, default learning_rate=0.1
# classifier_GB = GradientBoostingClassifier(n_estimators=500, # score: 0.88464 (AUC 0.80), learning_rate=0.0035
# classifier_GB = GradientBoostingClassifier(n_estimators=500, # score: 0.85208 (AUC 0.72), learning_rate=0.0035, max_depth=5, min_samples_split=20, max_features=8
# classifier_GB = GradientBoostingClassifier(n_estimators=500, # score: 0.90691 (AUC 0.80), learning_rate=0.002
# classifier_GB = GradientBoostingClassifier(n_estimators=500, # score: 0.94608, learning_rate=0.0005, max_depth=5, min_samples_split=20
# classifier_GB = GradientBoostingClassifier(n_estimators=500, # score: 0.94608, learning_rate=0.0005, max_depth=5, min_samples_split=30
# classifier_GB = GradientBoostingClassifier(n_estimators=500, # score: 0.94608, learning_rate=0.0005, max_depth=5, min_samples_split=30, max_features=10
# classifier_GB = GradientBoostingClassifier(n_estimators=500, # score: 0.94608, learning_rate=0.0005, max_depth=5, min_samples_split=30, max_features=5
# classifier_GB = GradientBoostingClassifier(n_estimators=500, # score: 0.94608, learning_rate=0.0005, max_depth=4, min_samples_split=30, max_features=5
# classifier_GB = GradientBoostingClassifier(n_estimators=1000, # score: 0.79112 (AUC 0.70), learning_rate=0.0035, max_depth=5, min_samples_split=30, max_features=10
# classifier_GB = GradientBoostingClassifier(n_estimators=1000, # score: 0.94608, learning_rate=0.0005, max_depth=5, min_samples_split=30, max_features=5
# classifier_GB = GradientBoostingClassifier(n_estimators=1000, # score: 0.63623, default learning_rate=0.1
# classifier_GB = GradientBoostingClassifier(n_estimators=2000, # score: 0.77567, learning_rate=0.0035
#                                    loss='deviance',
#                                    subsample=1,
#                                    max_depth=5,
#                                    min_samples_split=20,
                                   learning_rate=0.002,
#                                    max_features=10,
                                   random_state=rng)


    

In [139]:

    

# AB
# classifier_AB = AdaBoostClassifier(n_estimators=100, # score: (AUC 0.81618), learning_rate=0.004
# classifier_AB = AdaBoostClassifier(n_estimators=500, # score: 0.94608 (AUC 0.88), learning_rate=0.005
# classifier_AB = AdaBoostClassifier(n_estimators=1000, # score:  (AUC 0.87), learning_rate=0.01
# classifier_AB = AdaBoostClassifier(n_estimators=1000, # score:  (AUC 0.88), learning_rate=0.0075
# classifier_AB = AdaBoostClassifier(n_estimators=1000, # score:  (AUC 0.75), learning_rate=0.0001 <<< pre-matured
# classifier_AB = AdaBoostClassifier(n_estimators=1000, # score:  (AUC 0.88), learning_rate=0.005
# classifier_AB = AdaBoostClassifier(n_estimators=1000, # score:  (AUC 0.88), learning_rate=0.0025
# classifier_AB = AdaBoostClassifier(n_estimators=500, # score: 0.94608 (AUC 0.88), learning_rate=0.0035
# classifier_AB = AdaBoostClassifier(n_estimators=100, # score: 0.94608 (AUC 0.77), learning_rate=0.002
# classifier_AB = AdaBoostClassifier(n_estimators=500, # score: 0.94608 (AUC 0.85), learning_rate=0.002
# classifier_AB = AdaBoostClassifier(n_estimators=500, # score: 0.94608, learning_rate=0.0005
# classifier_AB = AdaBoostClassifier(n_estimators=1000, # score: 0.943130082 (AUC 0.88207), learning_rate=0.0035
classifier_AB = AdaBoostRegressor(n_estimators=1500, # score: 0.93948 (AUC 0.88339), learning_rate=0.004 <<< Best
# classifier_AB = AdaBoostClassifier(n_estimators=1500, # score: 0.92686 (AUC 0.88), learning_rate=0.0035 
# classifier_AB = AdaBoostClassifier(n_estimators=2000, # score: 0.63941, default learning_rate=0.1
# classifier_AB = AdaBoostClassifier(n_estimators=2000, # score: 0.90117 (AUC 0.87984), learning_rate=0.0035
# classifier_AB = AdaBoostClassifier(n_estimators=2000, # score: 0.89056 (AUC 0.88), learning_rate=0.004
# classifier_AB = AdaBoostClassifier(n_estimators=2000, # score: 0.83374 (AUC 0.83), learning_rate=0.01
                                   learning_rate=0.002,
                                   random_state=rng)


    

In [138]:

    

# RF
# classifier_RF = RandomForestClassifier(n_estimators=100, # score:  (AUC 0.66888), max_depth=8, min_samples_split=20,
# classifier_RF = RandomForestClassifier(n_estimators=500, # score: 0.90469, max_depth=5, min_samples_split=20,
# classifier_RF = RandomForestClassifier(n_estimators=500, # score: 0.90540, max_depth=5, min_samples_split=30,
# classifier_RF = RandomForestClassifier(n_estimators=500, # score: 0.93005, max_depth=4, min_samples_split=30,
# classifier_RF = RandomForestClassifier(n_estimators=500, # score: 0.93099, max_depth=4, min_samples_split=40,
# classifier_RF = RandomForestClassifier(n_estimators=500, # score: 0.93794 (AUC 0.82), max_depth=3, min_samples_split=20,
# classifier_RF = RandomForestClassifier(n_estimators=200, # score: 0.93771, max_depth=3, min_samples_split=20,
# classifier_RF = RandomForestClassifier(n_estimators=1000, # score: 0.90493, max_depth=5, min_samples_split=30,
classifier_RF = RandomForestRegressor(n_estimators=1500, # score: 0.94207 (AUC 0.81870), max_depth=3, min_samples_split=20, <<< Best
# classifier_RF = RandomForestClassifier(n_estimators=500, # score: 0.88900, max_depth=5, min_samples_split=30, max_features=10
# classifier_RF = RandomForestClassifier(n_estimators=1000, # score: 0.88864, max_depth=5, min_samples_split=30, max_features=10
# classifier_RF = RandomForestClassifier(n_estimators=500, # score: 0.77154
# classifier_RF = RandomForestClassifier(n_estimators=1000, # score: 0.76469
# classifier_RF = RandomForestClassifier(n_estimators=2000, # score: 0.76564
#                                     max_features=10,
#                                     max_depth=3,
#                                     min_samples_split=20,
                                    random_state=rng)


    

In [165]:

    

# ET
# classifier_ET = ExtraTreesClassifier(n_estimators=500, # score: 0.70973
# classifier_ET = ExtraTreesClassifier(n_estimators=500, # score: 0.93382 (AUC 0.81), max_depth=5, min_samples_split=30, max_features=10
classifier_ET = ExtraTreesRegressor(n_estimators=1500, # score: 0.94655 (AUC 0.84364), max_depth=3, min_samples_split=20, max_features=10 <<< Best
# classifier_ET = ExtraTreesClassifier(n_estimators=1000, # score: 0.93276, max_depth=5, min_samples_split=30, max_features=10
# classifier_ET = ExtraTreesClassifier(n_estimators=1000, # score: 0.94572, max_depth=4, min_samples_split=30, max_features=5
# classifier_ET = ExtraTreesClassifier(n_estimators=1000, # score: 0.94608 (AUC 0.82), max_depth=3, min_samples_split=30, max_features=5
# classifier_ET = ExtraTreesClassifier(n_estimators=1000, # score:  (AUC 0.84077), max_depth=3, min_samples_split=20, max_features=10
# classifier_ET = ExtraTreesClassifier(n_estimators=1000, # score: 0.93241, max_depth=5, min_samples_split=20, max_features=10
# classifier_ET = ExtraTreesClassifier(n_estimators=1000, # score: 0.71067
# classifier_ET = ExtraTreesClassifier(n_estimators=2000, # score: 0.71149
#                                     max_depth=3,
#                                     min_samples_split=20,
#                                     max_features=10,
                                    random_state=rng)


    

In [166]:

    

# BG
# classifier_BG = BaggingClassifier(n_estimators=500, # score: 0.77035, max_features=20
# classifier_BG = BaggingClassifier(n_estimators=500, # score: 0.78085 (AUC 0.57037), max_features=10
classifier_BG = BaggingRegressor(n_estimators=500, # score: 0.70725 (AUC 0.63729) <<< Best
# classifier_BG = BaggingClassifier(n_estimators=200, # score: 0.77707, max_features=10
# classifier_BG = BaggingClassifier(n_estimators=1000, # score: 0.78096 (AUC 0.56), max_features=10
# classifier_BG = BaggingClassifier(n_estimators=2000, # score:  (AUC 0.51506), max_features=10
# classifier_BG = BaggingClassifier(n_estimators=500, # score: 0.76553, max_features=5
# classifier_BG = BaggingClassifier(n_estimators=500, # score: 0.70181
# classifier_BG = BaggingClassifier(n_estimators=1000, # score: 0.69779
# classifier_BG = BaggingClassifier(n_estimators=2000, # score: 0.70004
#                                     max_features=10,
                                    random_state=rng)


    

In [167]:

    

classifier_LR = LinearRegression() # score: 0.90199 (AUC 0.80569)
# plot_roc(classifier_LR)


    

In [178]:

    

# classifier_SVCL = svm.SVC(kernel='linear', probability=True, random_state=rng) # score: 0.89976 (AUC 0.70524)
classifier_SVRL = svm.SVR() # score: 0.89976 (AUC 0.70524)
# plot_roc(classifier_SVCL)


    

In [169]:

    

# classifier_SVCR = svm.SVC(kernel='rbf', probability=True, random_state=rng) # score: 0.80188 (AUC 0.50050)
# classifier_SVRR = svm.SVR(kernel='rbf') # score: 0.80188 (AUC 0.50050)
classifier_SVRR = svm.SVR(kernel='rbf') # score: 0.80188 (AUC 0.50050)
# plot_roc(classifier_SVCR)


    

In [ ]:

    




    

In [187]:

    

# classifier = classifier_GB   # 0.07854768301
# classifier = classifier_AB   # 0.0846678136924
# classifier = classifier_RF   # 0.0791521661296
classifier = classifier_ET   # 0.0743745076978
# classifier = classifier_BG   # 0.0787926960258
# classifier = classifier_LR     # 0.0846378641917 (171079173108.0)
# classifier = classifier_SVRL # 0.0964908184664
# classifier = classifier_SVRR # 0.0964908184664


    

In [189]:

    

n_splits=19 # 19 seconds/records for each bidding month
n_fold = 19
# batch = 18 # range [1, 18]

# X_train_1 = X[0:(len(X)-batch*n_splits)]
# y_train_1 = y[0:(len(X)-batch*n_splits)]

# X_test_1 = X[(len(X)-batch*n_splits):((len(X)-batch*n_splits)+n_splits)]
# y_test_1 = y[(len(X)-batch*n_splits):((len(X)-batch*n_splits)+n_splits)]


    

In [190]:

    

y_pred = {}
y_test = {}

i = 0
for batch in range(1, n_fold):
    X_train_1 = X[0:(len(X)-batch*n_splits)]
    y_train_1 = y[0:(len(X)-batch*n_splits)]
    X_test_1  = X[(len(X)-batch*n_splits):((len(X)-batch*n_splits)+n_splits)]
    y_test_1  = y[(len(X)-batch*n_splits):((len(X)-batch*n_splits)+n_splits)]
    print(len(X_train_1))

    y_pred[i] = classifier.fit(X_train_1, y_train_1).predict(X_test_1)
#     y_pred[i] = classifier.fit(X_train_1, y_train_1).predict(X_test_1[0:10])
#     y_pred[i] = classifier.fit(X_train_1, y_train_1).predict(X_test_1[10:19])
    y_test[i] = y_test_1  
    plt.figure()
    plt.plot(y_train_1)
    plt.plot()
    plt.figure()
    plt.plot(y_test[i])
    plt.plot(y_pred[i])
    plt.plot()
    i += 1


    

    




342
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/home/user/env_py3/lib/python3.5/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  max_open_warning, RuntimeWarning)






    




133






    




/home/user/env_py3/lib/python3.5/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  max_open_warning, RuntimeWarning)






    




114






    




/home/user/env_py3/lib/python3.5/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  max_open_warning, RuntimeWarning)






    




95






    




/home/user/env_py3/lib/python3.5/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  max_open_warning, RuntimeWarning)






    




76






    




/home/user/env_py3/lib/python3.5/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  max_open_warning, RuntimeWarning)






    




57






    




/home/user/env_py3/lib/python3.5/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  max_open_warning, RuntimeWarning)






    




38






    




/home/user/env_py3/lib/python3.5/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  max_open_warning, RuntimeWarning)






    




19






    




/home/user/env_py3/lib/python3.5/site-packages/matplotlib/pyplot.py:524: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).
  max_open_warning, RuntimeWarning)
/home/user/env_py3/lib/python3.5/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans
  (prop.get_family(), self.defaultFamily[fontext]))






    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    













    

In [191]:

    

k = []
for i in range(0, len(y_test)):
    k.append(np.mean(np.sqrt(np.square(y_test[i] - y_pred[i]))))

k_mean = np.mean(k)

print(k_mean)
print()
print(k)


    

    




0.0932085769981

[0.097360902255640355, 0.050368421052630639, 0.044195488721805704, 0.045408521303257052, 0.14216040100250449, 0.080864661654136069, 0.063418546365914708, 0.033654135338345458, 0.11887719298245478, 0.094203007518795442, 0.06029824561403458, 0.067964912280699716, 0.099228070175438415, 0.12616541353383295, 0.077739348370927872, 0.12738596491228044, 0.032593984962407903, 0.31586716791980074]

In [ ]:

    




    

In [ ]:

    




    

In [ ]:

    




    

In [174]:

    

y_pred_df = pd.DataFrame.from_dict(y_pred)


    

In [175]:

    

y_pred_df.columns=['month 7','month 6','month 5','month 4','month 3','month 2','month 1']


    

In [176]:

    

y_pred_df.to_csv('bid_results_v001.csv', index=False)


    

In [177]:

    

y_pred_df


    

    
Out[177]:







  

    

      

      
month 7
      
month 6
      
month 5
      
month 4
      
month 3
      
month 2
      
month 1
    
  
  

    

      
0
      
0.143524
      
0.094619
      
0.105238
      
0.096524
      
0.061429
      
0.082810
      
0.096381
    
    

      
1
      
0.158667
      
0.128143
      
0.112143
      
0.138524
      
0.073619
      
0.097905
      
0.100667
    
    

      
2
      
0.234286
      
0.178810
      
0.207905
      
0.186095
      
0.102762
      
0.133429
      
0.142476
    
    

      
3
      
0.265524
      
0.186000
      
0.338857
      
0.179762
      
0.191429
      
0.164095
      
0.146905
    
    

      
4
      
0.300048
      
0.194619
      
0.364762
      
0.175143
      
0.225286
      
0.217000
      
0.166429
    
    

      
5
      
0.318810
      
0.208857
      
0.389571
      
0.188048
      
0.272143
      
0.251905
      
0.192381
    
    

      
6
      
0.363524
      
0.266286
      
0.405429
      
0.195476
      
0.317048
      
0.335095
      
0.202429
    
    

      
7
      
0.393429
      
0.275286
      
0.419571
      
0.222857
      
0.328571
      
0.405095
      
0.207048
    
    

      
8
      
0.406095
      
0.294952
      
0.424905
      
0.237619
      
0.346619
      
0.451619
      
0.211429
    
    

      
9
      
0.474048
      
0.294810
      
0.457000
      
0.317048
      
0.356333
      
0.478429
      
0.267810
    
    

      
10
      
0.496905
      
0.328476
      
0.482810
      
0.348048
      
0.361952
      
0.514381
      
0.276143
    
    

      
11
      
0.522381
      
0.340714
      
0.513714
      
0.359619
      
0.374190
      
0.554000
      
0.297000
    
    

      
12
      
0.569571
      
0.346286
      
0.564524
      
0.369190
      
0.379143
      
0.572000
      
0.359571
    
    

      
13
      
0.659619
      
0.366619
      
0.617619
      
0.398143
      
0.385095
      
0.659095
      
0.403333
    
    

      
14
      
0.715286
      
0.382333
      
0.636286
      
0.424667
      
0.396762
      
0.684095
      
0.450048
    
    

      
15
      
0.780714
      
0.396619
      
0.707905
      
0.452619
      
0.453333
      
0.740857
      
0.499619
    
    

      
16
      
0.844143
      
0.398905
      
0.775905
      
0.485238
      
0.475095
      
0.779238
      
0.527952
    
    

      
17
      
0.887000
      
0.401524
      
0.831667
      
0.511905
      
0.494333
      
0.798952
      
0.557381
    
    

      
18
      
0.904381
      
0.476905
      
0.892571
      
0.562762
      
0.565571
      
0.835000
      
0.583095
    
  

In [ ]:

    




    

In [ ]:

    




    

In [ ]:

    




    

In [ ]:

    




    

In [ ]:

    

cv = StratifiedKFold(n_splits=2, shuffle=False)


    

In [ ]:

    

# n_splits=6
# Run classifier with cross-validation and plot ROC curves
cv = StratifiedKFold(n_splits=3, shuffle=False)

# colors = cycle(['cyan', 'indigo', 'seagreen', 'yellow', 'blue', 'darkorange'])

y_pred = {}
y_test = {}

i = 0
for (train, test) in cv.split(X, y):
#     if i==0: print(X[train])
#         probas_ = classifier.fit(X[train], y[train]).predict_proba(X[test])
    y_pred[i] = classifier.fit(X[train], y[train]).predict(X[test])
    y_test[i] = y[test]    
    plt.figure()
    plt.plot(y[train])
    plt.plot()
    plt.figure()
    plt.plot(y_test[i])
    plt.plot(y_pred[i])
    plt.plot()
    i += 1


    

In [ ]:

    

k = []
for i in range(0, len(y_test)):
    k.append(np.mean(np.sqrt(np.square(y_test[i] - y_pred[i]))))

k_mean = np.mean(k)

print(k_mean)
print()
print(k)


    

In [ ]:

    




    

In [ ]:

    

plt.plot(y_test[0])
plt.plot(y_pred[0])
plt.plot()


    

In [ ]:

    




    

In [ ]:

    




    

In [ ]:

    




    

In [ ]:

    




    

In [ ]: