-
Notifications
You must be signed in to change notification settings - Fork 0
/
backupjustincase.R
173 lines (148 loc) · 9.4 KB
/
backupjustincase.R
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
p <- data.frame(predicted.outcomes,testing[,1])
#Rsqd =(1-sum((p[,2]-p[,1])^2, na.rm = T)/sum((p[,2]-mean(p[,2]))^2, na.rm = T))
Rsqd=1-RMSE(p[,1],p[,2])/RMSE(p[,2],train.based.mean)
#mean.improvement=1-mean(abs(p[,2]-p[,1]), na.rm = T)/mean(abs(p[,2]-median(p[,2])), na.rm = T)
mean.improvement=1-MAE(p[,1],p[,2])/MAE(p[,2],train.based.med)
if(trans.y==2){
p<- data.frame(predicted.outcomes,y.untransformed[foldTrain[[FN]]])
}else{
p<- data.frame(predict(loess.model,predicted.outcomes),y.untransformed[foldTrain[[FN]]])
}
#RMSE=(sqrt(mean((p[,1]-p[,2])^2, na.rm = T)))
RMSEp=RMSE(p[,1],p[,2])
MMAAEE=MAE(p[,1],p[,2])
#MMAAEE=mean(abs(p[,2]-p[,1]), na.rm = T)
#print(c(Rsqd,RMSE,overRMSE,date(),allmodel,column.to.predict,datasource,missingdata,withextra,norming,adaptControl$search,seed.const,adaptControl$method,tuneLength,adaptControl$number,adaptControl$repeats,adaptControl$adaptive$min,trainedmodel$bestTune))
write.table(c(round(mean.improvement,digits = 3),round(Rsqd,digits = 3),
signif(overRMSE,digits = 3),signif(RMSEp,digits = 3),signif(MMAAEE,digits = 3),
date(),allmodel,column.to.predict,trans.y,datasource,missingdata,
withextra,norming,which.computer,task.subject,FN,high.fold,.Random.seed[1:2],seed.var,RMSE.mean,RMSE.mean.train,adaptControl$search,round(proc.time()[3]-when[3]),
adaptControl$method,tuneLength,adaptControl$number,adaptControl$repeats,
adaptControl$adaptive$min,trainedmodel$bestTune),
file = out.file, append =TRUE, quote = F, sep = ",",
eol = "\n", na = "NA", dec = ".", row.names = F,
col.names = F, qmethod = "double")
print(date())
p <- data.frame(predicted.outcomes,testing[,1])
#Rsqd =(1-sum((p[,2]-p[,1])^2, na.rm = T)/sum((p[,2]-mean(p[,2]))^2, na.rm = T))
Rsqd=1-RMSE(p[,1],p[,2])/RMSE(p[,2],train.based.mean)
#mean.improvement=1-mean(abs(p[,2]-p[,1]), na.rm = T)/mean(abs(p[,2]-median(p[,2])), na.rm = T)
mean.improvement=1-MAE(p[,1],p[,2])/MAE(p[,2],train.based.med)
if(trans.y==2){
p<- data.frame(predicted.outcomes,y.untransformed[foldTrain[[FN]]])
}else{
p<- data.frame(predict(loess.model,predicted.outcomes),y.untransformed[foldTrain[[FN]]])
}
#RMSE=(sqrt(mean((p[,1]-p[,2])^2, na.rm = T)))
RMSEp=RMSE(p[,1],p[,2])
MMAAEE=MAE(p[,1],p[,2])
#MMAAEE=mean(abs(p[,2]-p[,1]), na.rm = T)
overRMSE=-1
wut=print(trainedmodel,selectCol=TRUE)
try({overRMSE=as.numeric(wut[wut[,length(wut[1,])]=="*","RMSE"])})#length(wut[1,])-
replace.overRMSE=1
try({if(is.numeric(overRMSE)){replace.overRMSE=0}})
if(replace.overRMSE==1){overRMSE=-1}
if(length(overRMSE)<1){overRMSE=-1}
#print(c(Rsqd,RMSE,overRMSE,date(),allmodel,column.to.predict,datasource,missingdata,withextra,norming,adaptControl$search,seed.const,adaptControl$method,tuneLength,adaptControl$number,adaptControl$repeats,adaptControl$adaptive$min,trainedmodel$bestTune))
write.table(c(round(mean.improvement,digits = 3),round(Rsqd,digits = 3),signif(overRMSE,digits = 3),
signif(RMSEp,digits = 3),signif(MMAAEE,digits = 3),date(),allmodel,column.to.predict,
trans.y,datasource,missingdata,withextra,norming,which.computer,task.subject,FN,high.fold,.Random.seed[1:2],seed.var,RMSE.mean,RMSE.mean.train,simpleControl$search,
round(proc.time()[3]-when[3]),simpleControl$method,tuneLength2,
simpleControl$number,"no rep","no min",trainedmodel$bestTune),
file = out.file, append =TRUE, quote = F, sep = ",",
eol = "\n", na = "NA", dec = ".", row.names = F,
col.names = F, qmethod = "double")
print(date())
p <- data.frame(predicted.outcomes,testing[,1])
#Rsqd =(1-sum((p[,2]-p[,1])^2, na.rm = T)/sum((p[,2]-mean(p[,2]))^2, na.rm = T))
Rsqd=1-RMSE(p[,1],p[,2])/RMSE(p[,2],train.based.mean)
#mean.improvement=1-mean(abs(p[,2]-p[,1]), na.rm = T)/mean(abs(p[,2]-median(p[,2])), na.rm = T)
mean.improvement=1-MAE(p[,1],p[,2])/MAE(p[,2],train.based.med)
if(trans.y==2){
p<- data.frame(predicted.outcomes,y.untransformed[foldTrain[[FN]]])
}else{
p<- data.frame(predict(loess.model,predicted.outcomes),y.untransformed[foldTrain[[FN]]])
}
#RMSE=(sqrt(mean((p[,1]-p[,2])^2, na.rm = T)))
RMSEp=RMSE(p[,1],p[,2])
MMAAEE=MAE(p[,1],p[,2])
#MMAAEE=mean(abs(p[,2]-p[,1]), na.rm = T)
overRMSE=-1
wut=print(trainedmodel,selectCol=TRUE)
try({wut=print(trainedmodel,selectCol=TRUE)
overRMSE=as.numeric(wut[wut[,length(wut[1,])]=="*","RMSE"])})#length(wut[1,])-
replace.overRMSE=1
try({if(is.numeric(overRMSE)){replace.overRMSE=0}})
if(replace.overRMSE==1){overRMSE=-1}
if(length(overRMSE)<1){overRMSE=-1}
#print(c(Rsqd,RMSE,overRMSE,date(),allmodel,column.to.predict,datasource,missingdata,withextra,norming,adaptControl$search,seed.const,adaptControl$method,tuneLength,adaptControl$number,adaptControl$repeats,adaptControl$adaptive$min,trainedmodel$bestTune))
write.table(c(round(mean.improvement,digits = 3),round(Rsqd,digits = 3),signif(overRMSE,digits = 3),
signif(RMSEp,digits = 3),signif(MMAAEE,digits = 3),date(),allmodel,column.to.predict,
trans.y,datasource,missingdata,withextra,norming,which.computer,task.subject,FN,high.fold,.Random.seed[1:2],seed.var,RMSE.mean,RMSE.mean.train,simpleControl$search,
round(proc.time()[3]-when[3]),"nohyperparameters",tuneLength2,
simpleControl$number,"no rep","no min",trainedmodel$bestTune),
file = out.file, append =TRUE, quote = F, sep = ",",
eol = "\n", na = "NA", dec = ".", row.names = F,
col.names = F, qmethod = "double")
print(date())
p <- data.frame(predicted.outcomes$data[,2],testing[,1])
#Rsqd =(1-sum((p[,2]-p[,1])^2, na.rm = T)/sum((p[,2]-mean(p[,2]))^2, na.rm = T))
Rsqd=1-RMSE(p[,1],p[,2])/RMSE(p[,2],train.based.mean)
#mean.improvement=1-mean(abs(p[,2]-p[,1]), na.rm = T)/mean(abs(p[,2]-median(p[,2])), na.rm = T)
mean.improvement=1-MAE(p[,1],p[,2])/MAE(p[,2],train.based.med)
if(trans.y==2){
p<- data.frame(predicted.outcomes$data[,2],y.untransformed[foldTrain[[FN]]])
}else{
p<- data.frame(predict(loess.model,predicted.outcomes$data[,2]),y.untransformed[foldTrain[[FN]]])
}
#RMSE=(sqrt(mean((p[,1]-p[,2])^2, na.rm = T)))
RMSEp=RMSE(p[,1],p[,2])
MMAAEE=MAE(p[,1],p[,2])
#RMSE.mean=(sqrt(mean((p[,2]-mean(p[,2]))^2, na.rm = T)))
#RMSE.mean=signif(RMSE(p[,2],mean(p[,2], na.rm = T)), digits = 4)
#RMSE.mean.train=signif(RMSE(training[,1],mean(training[,1], na.rm = T)), digits = 4)
#MMAAEE=mean(abs(p[,2]-p[,1]), na.rm = T)
#print(c(Rsqd,RMSE,overRMSE,date(),allmodel,column.to.predict,datasource,missingdata,withextra,norming,adaptControl$search,seed.const,adaptControl$method,tuneLength,adaptControl$number,adaptControl$repeats,adaptControl$adaptive$min,trainedmodel$bestTune))
write.table(c(round(mean.improvement,digits = 3),round(Rsqd,digits = 3),
signif(overRMSE,digits = 3),signif(RMSEp,digits = 3),signif(MMAAEE,digits = 3),
date(),allmodel,column.to.predict,trans.y,datasource,missingdata,
withextra,norming,which.computer,task.subject,FN,high.fold,.Random.seed[1:2],seed.var,RMSE.mean,RMSE.mean.train,adaptControl$search,round(proc.time()[3]-when[3]),
adaptControl$method,tuneLength,adaptControl$number,adaptControl$repeats,
adaptControl$adaptive$min,mod$x),
file = out.file, append =TRUE, quote = F, sep = ",",
eol = "\n", na = "NA", dec = ".", row.names = F,
col.names = F, qmethod = "double")
print(date())
p <- data.frame(predicted.outcomes$data[,2],testing[,1])
#Rsqd =(1-sum((p[,2]-p[,1])^2, na.rm = T)/sum((p[,2]-mean(p[,2]))^2, na.rm = T))
Rsqd=1-RMSE(p[,1],p[,2])/RMSE(p[,2],train.based.mean)
#mean.improvement=1-mean(abs(p[,2]-p[,1]), na.rm = T)/mean(abs(p[,2]-median(p[,2])), na.rm = T)
mean.improvement=1-MAE(p[,1],p[,2])/MAE(p[,2],train.based.med)
if(trans.y==2){
p<- data.frame(predicted.outcomes$data[,2],y.untransformed[foldTrain[[FN]]])
}else{
p<- data.frame(predict(loess.model,predicted.outcomes$data[,2]),y.untransformed[foldTrain[[FN]]])
}
#RMSE=(sqrt(mean((p[,1]-p[,2])^2, na.rm = T)))
RMSEp=RMSE(p[,1],p[,2])
MMAAEE=MAE(p[,1],p[,2])
#RMSE.mean=(sqrt(mean((p[,2]-mean(p[,2]))^2, na.rm = T)))
#RMSE.mean=signif(RMSE(p[,2],mean(p[,2], na.rm = T)), digits = 4)
#RMSE.mean.train=signif(RMSE(training[,1],mean(training[,1], na.rm = T)), digits = 4)
#MMAAEE=mean(abs(p[,2]-p[,1]), na.rm = T)
NoAp<-"NoAp"
NoHyper<-"nohyperparam"
Rseed<-.Random.seed[1]
Cseed<-.Random.seed[2]
#print(c(Rsqd,RMSE,overRMSE,date(),allmodel,column.to.predict,datasource,missingdata,withextra,norming,adaptControl$search,seed.const,adaptControl$method,tuneLength,adaptControl$number,adaptControl$repeats,adaptControl$adaptive$min,trainedmodel$bestTune))
write.table(paste(round(mean.improvement,digits = 3),round(Rsqd,digits = 3),
signif(overRMSE,digits = 3),signif(RMSEp,digits = 3),signif(MMAAEE,digits = 3),
date(),allmodel,column.to.predict,trans.y,datasource,missingdata,
withextra,norming,which.computer,task.subject,FN,high.fold,Rseed,Cseed,seed.var,RMSE.mean,RMSE.mean.train,NoHyper,round(proc.time()[3]-when[3]),
adaptControl$method,tuneLength,adaptControl$number,adaptControl$repeats,
adaptControl$adaptive$min,mod$x, sep = ","),
file = out.file, append =TRUE, quote = F, sep = ",",
eol = "\n", na = "NA", dec = ".", row.names = F,
col.names = F, qmethod = "double")
print(date())