-
Notifications
You must be signed in to change notification settings - Fork 0
/
pickletotable.py
194 lines (176 loc) · 9.5 KB
/
pickletotable.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
import os
import sys
from datetime import datetime
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.append(parent_dir)
import utils
from tabulate import tabulate
import pickle as pic
from scipy import stats
import pandas as pd
from txt_to_pickle import txt_to_pickle
############### TEST RUNS ##################
file_ = 'run20240418180419.pic' #Ms
file_ = 'run20240502160503.pic'
#file_ ='run20240416210454.pic' #fBM.
file_ = 'run20240503200517.pic' # Independent set (pca3)
file_= 'run20240503220535.pic' # 200 no pca
file_= 'run20240503230502.pic' # pca 1
file_0 = 'run20240526170531.pic' # KAGGLE fBM Belomestny, Bender, Schoenmakers 2024
file_='run20240526200556.pic' # KAGGLE fBM 1.0, 1.2
file_2='run20240526220547.pic'# KAGGLE fBM 1.0, 1.2
file_='run20240527040556.pic' # Original M-C
file_2 = 'run20240526220508.pic'# Additional M-C
file_MC = 'run20240527110533.pic'# Extended M-C (A)
file_FBM='run20240527110508.pic'# Extended fbm (A)
file_ = 'run20240529120545.pic' # Test Run fair computational effort analysis
file_ = 'run20240529230505.pic' # Test Run fair computational effort analysis
file_ = 'run20240530150543.pic'# Test Run fair computational effort analysis
file_= 'run20240531080505.pic'# Test Run fair computational effort analysis
#file_ = 'run20240527040556.pic'
# file_='run20240511150517.pic'
# file_='run20240511180558.pic'
file_ = 'run20240601060629.pic' # Google Cloud Run (test)
file_='run20240602070653.pic' # Google Cloud Run (test)
file_='run20240602090618.pic'# Google Cloud Run (test)
file_='run20240602140619.pic'# Google Cloud Run (test)
file_='run20240604030642.pic'# Google Cloud Run (test [50,9,L])
file_='run20240606200655.pic'# Google Cloud Run (test [50,9,L])
# file_='run20240605010655.pic' # Test run high dimensions
file_='run20240606180647.pic'
file_='run20240607020621.pic'
file_='run20240608180610.pic' #fBM test run
file_ = 'run20240609070632.pic'# test Run fair computational effort analysis (90,5,L)
file_ = 'run20240608230625.pic'# test Run fair computational effort fbm
file_2= 'run20240609210633.pic' # Final Run fair computational effort fbm
file_= 'run20240610110654.pic' # Google cloud run final Final Run fair computational effort analysis (90,5,L)
file_= 'run20240610200648.pic'# Google cloud run final Final Run fair computational effort analysis (90,10,3)
file_='run20240611230633.pic'# test Run fair computational effort analysis (90,5,3) for N_T=49
file_='run20240612100609.pic'# Run fair computational effort analysis (90,5,3) for N_T=49
file_= 'run20240612140646.pic'# test Run fair computational effort analysis fBm for N_T=49
file_='run20240612220606.pic' # high dimensional test instance
file_ = 'run20240612220603.pic' # test instance fbm
file_='run20240613070643.pic' # final run fbm N_T =49
file_ = 'run20240614040605.pic'# Run fair computational effort fbm (final N_T=9). H=0.45
file_='run20240614080614.txt' # Run fair computational effort fbm (final N_T=9). All H
file_='run20240614070647.txt' # test Run fair computational effort fbm (N_T=49). H=0.45
file_='run20240614100613.txt'# final run fbm N_T =49 including H=0.45
file_='run20240615180639.txt'# test run final run fbm N_T =49
file_= 'run20240616170654.txt'# final run 16-6 for Bermudan Max Call N_T=49
file_= 'run20240617160643.txt'
file_='run20240618120619.txt'
file_='run20240618150618.txt'
file_='run20240619030632.txt'
file_='run20240619000608.txt' # fBM higher inner sim AB (too high)
file_='run20240619110659.txt' # fBM higher inner sim AB
file_='run20240619120658.txt'# fBM higher inner sim AB
file_='run20240702130705.txt' #100 run fbm
# file_ = 'run20240702120730.txt' #100 run bermudan max call
############### FINAL RUNS ##################
# file_='run20240701170744.txt'# final run fBM (higher inner sim AB), N_T=9. 28-6
#file_='run20240628030620.txt' #final run fBM (higher inner sim AB), N_T=9, hurst close to 0.5; 28-6
#file_='run20240628010620.txt'# final run fBM (higher inner sim AB), N_T=49. 28-6
file_ = 'run20240705180707.txt'# final run fBM (higher inner sim AB), N_T=100. 28-6
# file_ ='run20240628130622.txt'# final run Bermudan Max Call N_T=9. 22-6. Div=.15, r=0.0
# file_ ='run20240628130651.txt'# final run Bermudan Max Call N_T=9, d=10. Div=.15, r=0.0
# file_='run20240628150624.txt'# final run Bermudan Max Call N_T=49. 28-6. Div=.15, r=0.0
def read_file(file_):
try:
file_pic=file_.replace('txt', 'pic')
with open(file_pic, 'rb') as fh:
information=pic.load(fh)
return information
except Exception:
txt_to_pickle(file_)
base_name=file_.split('.')[0]
file_=f'{base_name}.pic'
with open(file_, 'rb') as fh:
information=pic.load(fh)
return information
information=read_file(file_)
# with open(file_2, 'rb') as fh:
# information_original =pic.load(fh)
# information_all = [*information, *information_original]
# import github
# from github import Auth
# from github import Github
# auth = Auth.Token('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')
# g = github.Github(auth=auth)
# repo = g.get_repo('PatrickvanEwijk/UpperboundApproachesDuality')
# repo.create_file(f'resultfiles/run{datetime.now().strftime("%Y%m%d%H%m%S")}.txt', f'run{datetime.now().strftime("%Y%m%d%H%m%S")}.txt', str(information_all), branch='main')
i_2= [i[2] for i in information]
FBM= min(i_2)>=0 and max(i_2)<=1 # Auto check if underlying data file is fBm/max call type: 3rd column= price Max Call and H fBm.
BBS_A_analysis=False
fuji=False
plot=False
GAP_comparison= True # If True, all upper bounds compared to lower bound with control variate with lowest standard error.
if BBS_A_analysis:
information_incl_95cb=[]
for i in information:
i[1]="%.1f" % i[1]+ '\hspace{0cm}'
i[2]="%.2f" % i[2]+ '\hspace{0cm}'
i.append(i[10]+stats.norm.ppf(0.95)*i[11] ) #95\% upper confidence bound.
information_incl_95cb.append(i)
information=information_incl_95cb
if FBM:
table_ = tabulate(utils.information_format_fbm_BBS(information), headers=utils.header_bbs_fbm, tablefmt="latex_raw", floatfmt=".5f")
else:
table_ = tabulate(utils.information_format_maxcall_BBS(information), headers=utils.header_bbs, tablefmt="latex_raw", floatfmt=".4f")
if plot and not FBM:
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
information=pd.DataFrame.from_records(information)
fig = plt.figure()
## Empirical
ax = fig.add_subplot(111, projection='3d')
DATA_sel= np.where(information.iloc[:,-1]< np.quantile( information.iloc[:,-1], 0.8))[0]
ax.scatter(information.iloc[DATA_sel, 1].str.replace('\\hspace{0cm}','').astype(float),information.iloc[DATA_sel, 2].str.replace('\\hspace{0cm}','').astype(float),information.iloc[DATA_sel,-1])
# ax.set_title('95\% Upper Quantile')
ax.set_xlabel(r'$\mu_A$')
ax.set_ylabel(r'$\sigma_A$')
ax.set_zlabel(r'95\% A.S. Upper Quantile')
ax.set_box_aspect(aspect=None, zoom=0.95)
ax.view_init(elev=17, azim=-53)
plt.show()
else:
if FBM:
info_original=pd.DataFrame.from_records(information)
if fuji is False:
info_transformed=pd.DataFrame.from_records(utils.information_format_fbm_no_fu(information), columns=utils.header_fbm_no_fu)
else:
info_transformed=pd.DataFrame.from_records(utils.information_format_fbm(information), columns=utils.header_fbm)
lower_bounds_cv=info_original.iloc[:,[2,13, 14]]
upper_bounds = info_original.iloc[:, 9]
# lower_bounds_cv.iloc[:, 2]= lower_bounds_cv.iloc[:, 1]+ stats.norm.ppf(0.05)*lower_bounds_cv.iloc[:,2]
loc_lowest_se = lower_bounds_cv.groupby(2)[14].idxmin()
lower_bounds_cv_sel = lower_bounds_cv.loc[loc_lowest_se, [2, 13]].rename(columns={13: 'best'})
lower_bounds_cv=pd.merge(left= lower_bounds_cv, right=lower_bounds_cv_sel, on=2)['best']
lower_bounds_cv.index= info_original.index
info_transformed['GAP']= upper_bounds- lower_bounds_cv
else:
info_original=pd.DataFrame.from_records(information)
info_transformed = pd.DataFrame.from_records(utils.information_format(information), columns=utils.header_)
lower_bounds_cv=info_original.iloc[:,[3,14, 15]]
upper_bounds = info_original.iloc[:, 10]
#lower_bounds_cv.iloc[:, 2]= lower_bounds_cv.iloc[:, 1]+ stats.norm.ppf(0.05)*lower_bounds_cv.iloc[:,2]
loc_lowest_se= lower_bounds_cv.groupby(3)[15].idxmin()
lower_bounds_cv_sel = lower_bounds_cv.loc[loc_lowest_se, [3, 14]].rename(columns={14: 'best'})
lower_bounds_cv=pd.merge(left= lower_bounds_cv, right=lower_bounds_cv_sel, on=3)['best']
lower_bounds_cv.index= info_original.index
info_transformed['GAP']= upper_bounds- lower_bounds_cv
table_ = tabulate(info_transformed.values, headers=info_transformed.columns, tablefmt="latex_raw", floatfmt=".4f")
print(table_)
fh = open(f'logresults.txt', 'a')
fh.write(f'{datetime.now()}\n ')
fh.writelines(table_)
line="".join( ['*' for i in range(75)])
fh.write(f'\n {line} \n')
fh.close()
# information = [[i[0]+' Cor.', *i[1:]] for i in information]
# file_2 ='run20240416100435.pic' #fBM.
# with open(file_2, 'rb') as fh:
# information2=pic.load(fh)
# information_merged = [*information2, *information]
# with open(f'run{datetime.now().strftime("%Y%m%d%H%m%S")}.pic', 'wb') as fh:
# pic.dump(information_merged, fh)