-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy path.Rhistory
152 lines (148 loc) · 4.85 KB
/
.Rhistory
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
source("simulations/simulation_cpath_versions.R")
source("simulations/simulation_cpath_versions.R")
?cor
source("simulations/simulation_cpath_versions.R")
write.table(RES, file="COR_sim5.txt")
source("simulations/simulation_cpath_versions.R")
write.table(RES, file="COR_sim.txt")
source("simulations/simulation_cpath_versions.R")
write.table(RES, file="COR_sim3.txt")
source("simulations/simulation_cpath_versions.R")
source("simulations/simulation_cpath_versions.R")
source("simulations/simulation_cpath_versions_CORR.R")
write.table(RES, file="COR_sim4.txt")
is.element(1,c(2,3)
)
is.element(1,c(2,3))
is.element(c(1,2),c(2,3))
is.element(c(3,2),c(2,3))
source("simulations/simulation_cpath_min_COV.R")
q()
library(treeshap)
install.packages("treeshap")
source("simulations/simulation_main_datasets_treeSHAP.R")
traceback()
source("simulations/simulation_main_datasets_treeSHAP.R")
traceback()
q()
source("simulations/simulation_main_datasets_treeSHAP.R")
# main
library(ranger)
library(lime)
library(cpath)
library(shapr)
library(ranger)
library(lime)
library(cpath)
library(mlbench)
library(shapr)
library(treeshap)
model_unified
model
vimp
source("simulations/simulation_main_datasets_treeSHAP.R")
traceback()
source("simulations/simulation_main_datasets_treeSHAP.R")
is(model_unified)
is(model)
source("simulations/simulation_main_datasets_treeSHAP.R")
source("simulations/simulation_main_datasets_treeSHAP.R")
vimp = treeshap(model_unified, data)
data
vimp = treeshap(model_unified, data)
class(model_unified)
model_unified = unify(model, data)
#model = ranger(x=data,y=as.factor(target),
model = ranger(target~., data=as.data.frame(data),
num.trees=100,
classification=TRUE,
#probability=TRUE,
#mtry=4,
#replace=TRUE,#),
importance='impurity')
pred = predict(model, data)$predictions
#pred = apply(pred,1,function(x){which.max(x)-1})
#pred = t(pred)
#print("?")
model_unified = unify(model, data)
target
#model = ranger(x=data,y=as.factor(target),
model = ranger(target~., data=as.data.frame(data2),
num.trees=100,
classification=TRUE,
#probability=TRUE,
#mtry=4,
#replace=TRUE,#),
importance='impurity')
pred = predict(model, data)$predictions
#pred = apply(pred,1,function(x){which.max(x)-1})
#pred = t(pred)
#print("?")
if(all(pred==0)|all(pred==1)){
next
#}
#model = ranger(x=data,y=as.factor(target),
model = ranger(target~., data=as.data.frame(data2),
num.trees=100,
classification=TRUE,
#probability=TRUE,
#mtry=4,
#replace=TRUE,#),
importance='impurity')
pred = predict(model, data)$predictions
#pred = apply(pred,1,function(x){which.max(x)-1})
#pred = t(pred)
#print("?")
model_unified = unify(model, data)
vimp = treeshap(model_unified, data)
vimp
source("simulations/simulation_main_datasets_treeSHAP.R")
model_unified = unify(model, data)
target
source("simulations/simulation_main_datasets_treeSHAP.R")
data
data2
is(model)
model_unified = unify(model, data)
model_unified = unify(model, data)
model_unified = unify(model, data2)
model_unified = unify(model, data)
source("simulations/simulation_main_datasets_treeSHAP.R")
P = cpath::cpaths_mc(model, data, k=4, n_paths= 10000)
P = cpath::cpaths_mc(model, data, k=4, n_paths= 10000)
P = cpath::cpaths_mc(model, data, k=4, n_paths= 10000)
q()
source("simulations/simulation_main_datasets_gini.R")
source("simulations/simulation_main_datasets_gini.R")
source("simulations/simulation_main_datasets_gini.R")
source("simulations/simulation_main_datasets_gini.R")
source("simulations/simulation_main_datasets_gini.R")
write.table(RES, file="BREAST_corr_with_gini_CPATH_full.txt")
source("simulations/simulation_main_datasets_gini.R")
write.table(RES, file="IONOSPHERE_corr_with_gini_CPATH_full.txt")
source("simulations/simulation_main_datasets_gini.R")
write.table(RES, file="DIABETES_corr_with_gini_CPATH_full.txt")
source("simulations/simulation_main_datasets_gini.R")
write.table(RES, file="IRIS_corr_with_gini_CPATH_full.txt")
source("simulations/simulation_main_datasets_treeSHAP.R")
traceback()
dim(data)
source("simulations/simulation_main_datasets_treeSHAP.R")
ncol(data)
data
# Breast Cancer
data(BreastCancer)
na.ids = which(apply(BreastCancer,1,function(x){any(is.na(x))}))
BreastCancer = BreastCancer[-na.ids,]
data = BreastCancer[,2:10]
NN = colnames(data)
data = matrix(as.numeric(unlist(data)), dim(data)[1], dim(data)[2])
#data = apply(data,2,function(x){ (x - mean(x)) / sd(x)})
colnames(data) = NN
data = as.data.frame(data)
target = BreastCancer[,11]
target = factor(target)#, levels=c("setosa", "versicolor"))
data
source("simulations/simulation_main_datasets_treeSHAP.R")
pred
q()