-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy path04modifVariables.py
74 lines (53 loc) · 2.72 KB
/
04modifVariables.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
limport pandas as pd
import biogeme.database as db
import biogeme.biogeme as bio
pandas = pd.read_table("swissmetro.dat")
database = db.Database("swissmetro",pandas)
# The Pandas data structure is available as database.data. Use all the
# Pandas functions to invesigate the database
#print(database.data.describe())
from headers import *
# Removing some observations can be done directly using pandas.
#remove = (((database.data.PURPOSE != 1) & (database.data.PURPOSE != 3)) | (database.data.CHOICE == 0))
#database.data.drop(database.data[remove].index,inplace=True)
# Here we use the "biogeme" way for backward compatibility
exclude = (( PURPOSE != 1 ) * ( PURPOSE != 3 ) + ( CHOICE == 0 )) > 0
database.remove(exclude)
ASC_CAR = Beta('ASC_CAR',0,None,None,0)
ASC_TRAIN = Beta('ASC_TRAIN',0,None,None,0)
ASC_SM = Beta('ASC_SM',0,None,None,1)
B_TIME = Beta('B_TIME',0,None,None,0)
B_COST = Beta('B_COST',0,None,None,0)
SM_COST = SM_CO * ( GA == 0 )
TRAIN_COST = TRAIN_CO * ( GA == 0 )
TRAIN_TT_SCALED = DefineVariable('TRAIN_TT_SCALED',\
TRAIN_TT / 100.0,database)
TRAIN_COST_SCALED = DefineVariable('TRAIN_COST_SCALED',\
TRAIN_COST / 100,database)
SM_TT_SCALED = DefineVariable('SM_TT_SCALED', SM_TT / 100.0,database)
SM_COST_SCALED = DefineVariable('SM_COST_SCALED', SM_COST / 100,database)
CAR_TT_SCALED = DefineVariable('CAR_TT_SCALED', CAR_TT / 100,database)
CAR_CO_SCALED = DefineVariable('CAR_CO_SCALED', CAR_CO / 100,database)
# Biogeme cannot compute the log of 0. Therefore, whenever the cost
# is 0, the log of 1 computed instead.
LOG_CAR_COST = DefineVariable('LOG_CAR_COST',(CAR_CO_SCALED != 0) * log( CAR_CO_SCALED + 1 * (CAR_CO_SCALED == 0)),database)
LOG_TRAIN_COST = DefineVariable('LOG_TRAIN_COST',(TRAIN_COST_SCALED != 0) * log( TRAIN_COST_SCALED + 1 * (TRAIN_COST_SCALED == 0) ),database)
LOG_SM_COST = DefineVariable('LOG_SM_COST', (SM_COST_SCALED != 0) * log( SM_COST_SCALED + 1 * (SM_COST_SCALED == 0)),database)
V1 = ASC_TRAIN + B_TIME * TRAIN_TT_SCALED + B_COST * LOG_TRAIN_COST
V2 = ASC_SM + B_TIME * SM_TT_SCALED + B_COST * LOG_SM_COST
V3 = ASC_CAR + B_TIME * CAR_TT_SCALED + B_COST * LOG_CAR_COST
# Associate utility functions with the numbering of alternatives
V = {1: V1,
2: V2,
3: V3}
# Associate the availability conditions with the alternatives
CAR_AV_SP = DefineVariable('CAR_AV_SP',CAR_AV * ( SP != 0 ),database)
TRAIN_AV_SP = DefineVariable('TRAIN_AV_SP',TRAIN_AV * ( SP != 0 ),database)
av = {1: TRAIN_AV_SP,
2: SM_AV,
3: CAR_AV_SP}
logprob = bioLogLogit(V,av,CHOICE)
biogeme = bio.BIOGEME(database,logprob)
biogeme.modelName = "04modifVariables"
results = biogeme.estimate()
print("Results=",results)