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train.asd.data.R
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train.asd.data.R
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library(data.table)
library(magrittr)
library(tidyr)
library(stringr)
library(plyr)
library(dplyr)
#library(caret)
library(DMwR)
library(RSNNS)
library(pROC)
library(parallelRandomForest)
library(party)
set.seed(10)
setwd("/mnt/d/Autism/")
setwd("/media/rrahman/Seagate Backup Plus Drive/Autism/")
all_data_atc_scaled_com= fread( "all_data_selected_atc.level2.softmax.csv", header =T, sep = "," )
all_data_atc_scaled_com$ses = all_data_atc_scaled_com$ses %>% as.character() %>% as.factor()
all_data_atc_scaled_com$missing_p = all_data_atc_scaled_com$missing_p %>% as.character() %>% as.factor()
all_data_atc_scaled_com$missing_m = NULL
#all_data_atc_scaled_com = all_data_atc_scaled_com[all_data_atc_scaled_com$missing_p == 0, ]
all_data_atc_scaled_com$missing_p = NULL
#all_data_atc_scaled_com = all_data_atc_scaled_com[, c(1:4,6:213)]
all_data_atc_scaled_com$asd_status = all_data_atc_scaled_com$asd_status %>% as.character() %>% as.factor()
rocs_all = data.frame()
performance_all = data.frame()
varimp_all = data.frame()
get_perf = function(pred_class, pred_prob , obs_class )
{
df = cbind(pred_class, as.character(obs_class)) %>% as.data.frame()
colnames(df) = c("predicted", "observed")
q = table(df)
q[1] = q[1] %>% as.numeric()
q[2] = q[2] %>% as.numeric()
q[3] = q[3] %>% as.numeric()
q[4] = q[4] %>% as.numeric()
sen = q[4]/(q[4] + q[3])
spe = q[1]/(q[1] + q[2])
pre = q[4]/(q[4] + q[2])
fpr = q[2]/(q[2] + q[1])
fdr = q[2]/(q[2] + q[4])
acc = (q[1] + q[4] )/(q[1] + q[2] + q[3] + q[4] )
f1_asd = (2 * q[4] ) / ((2*q[4]) + q[2] + q[3] )
f1_neg = (2 * q[1] ) / ((2*q[1]) + q[2] + q[3] )
r = roc(predictor = pred_prob[,1] , response = obs_class )
ac = auc(r)
rw = c(q[4], q[3], q[2], q[1], sen, spe, pre, fpr , fdr, acc, f1_neg, f1_asd, ac ) %>% as.data.frame %>% t %>% as.data.frame()
return(rw)
}
###revaluate performance of SMOTE vs no oversampling vs inverse class weights vs undersampling
all_data_atc_scaled_com =all_data_atc_scaled_com[sample(1:nrow(all_data_atc_scaled_com),nrow(all_data_atc_scaled_com)),]
all_data_atc_scaled_com.sp = split( all_data_atc_scaled_com, f= all_data_atc_scaled_com$asd_status )
size.un = round(.8*nrow(all_data_atc_scaled_com.sp$`0`))
size.asd = round(.8*nrow(all_data_atc_scaled_com.sp$`1`))
total.un = 1:nrow(all_data_atc_scaled_com.sp$`0`)
total.asd = 1:nrow(all_data_atc_scaled_com.sp$`1`)
train.un = sample(1:nrow(all_data_atc_scaled_com.sp$`0`), size.un )
train.asd = sample(1:nrow(all_data_atc_scaled_com.sp$`1`), size.asd )
test.un = total.un[which(! total.un %in% train.un ) ]
test.asd = total.asd[which(! total.asd %in% train.asd ) ]
training.dat.un = all_data_atc_scaled_com.sp$`0`[train.un, ]
training.dat.asd = all_data_atc_scaled_com.sp$`1`[train.asd, ]
training.dat = rbind(training.dat.un, training.dat.asd)
testing.dat.un = all_data_atc_scaled_com.sp$`0`[test.un, ]
testing.dat.asd = all_data_atc_scaled_com.sp$`1`[test.asd, ]
testing.dat = rbind(testing.dat.un, testing.dat.asd)
testing.class = testing.dat$asd_status %>% as.factor
testing.dat$asd_status = NULL
##fit no weight/smote/undersample
training.dat.no.fix = training.dat[sample(1:nrow(training.dat), nrow(training.dat)), ]
training.class = training.dat.no.fix$asd_status %>% as.factor
training.dat.no.fix$asd_status = NULL
fit_rf = randomForest(x = data.matrix(training.dat.no.fix) , training.class,
ntree = 100, nthreads = 4 , mtry = 20,
localImp = F, proximity = F)
pred.prob.rf = predict(fit_rf, data.matrix(testing.dat), type = "prob")
pred.prob.rf.class = round(pred.prob.rf[,2]) %>% as.data.frame()
rf.no.opti = get_perf(pred.prob.rf.class,pred.prob.rf, testing.class )
names(rf.no.opti) = c("TP", "FN","FP","TN" , "sen", "spe", "pre", "fpr",
"fdr", "acc", "f1neg", "f1asd", "ac")
rf.no.opti
##weighted classes
wy = 1/(sum(training.class)/length(training.class) )
fit_rf = randomForest(x = data.matrix(training.dat.no.fix) , training.class,
ntree = 100, nthreads = 4 , mtry = 20, classwt = c(1,wy),
localImp = F, proximity = F)
pred.prob.rf = predict(fit_rf, data.matrix(testing.dat), type = "prob")
pred.prob.rf.class = round(pred.prob.rf[,2]) %>% as.data.frame()
rf.no.opti = get_perf(pred.prob.rf.class,pred.prob.rf, testing.class )
names(rf.no.opti) = c("TP", "FN","FP","TN" , "sen", "spe", "pre", "fpr",
"fdr", "acc", "f1neg", "f1asd", "ac")
rf.no.opti
## equal class balance
training.non = training.dat[asd_status == 0 , ]
training.dat.2 = training.non[sample(1:nrow(training.non), sum(training.dat$asd_status)), ]
training.dat.2 = rbind(training.dat.2, training.dat[asd_status == 1, ])
training.dat.2 = training.dat.2[sample(1:nrow(training.dat.2), nrow(training.dat.2)), ]
training.class.2 = training.dat.2$asd_status %>% as.factor
training.dat.2$asd_status = NULL
training.dat.2$ses = training.dat.2$ses %>% as.factor
fit_rf = randomForest(x = data.matrix(training.dat.2) , training.class.2 ,
ntree = 100, nthreads = 4 , mtry = 20,
localImp = F, proximity = F)
pred.prob.rf = predict(fit_rf, data.matrix(testing.dat), type = "prob")
pred.prob.rf.class = round(pred.prob.rf[,2]) %>% as.data.frame()
rf.no.opti = get_perf(pred.prob.rf.class,pred.prob.rf, testing.class )
names(rf.no.opti) = c("TP", "FN","FP","TN" , "sen", "spe", "pre", "fpr",
"fdr", "acc", "f1neg", "f1asd", "ac")
rf.no.opti
##fit smote data
training.smote = training.dat
training.smote$asd_status = as.factor(training.smote$asd_status)
training.smote$ses = as.factor(training.smote$ses)
training.smote = SMOTE(asd_status ~ . , data = training.smote , perc.over = 500, k = 100 )
training.class = training.smote$asd_status %>% as.factor
training.smote$asd_status = NULL
fit_rf = randomForest( x = data.matrix(training.smote) , y = training.class ,
ntree = 100, nthreads = 4 , mtry = 20,
localImp = F, proximity = F)
pred.prob.rf = predict(fit_rf, data.matrix(testing.dat), type = "prob")
pred.prob.rf.class = round(pred.prob.rf[,2]) %>% as.data.frame()
rf.no.opti = get_perf(pred.prob.rf.class,pred.prob.rf, testing.class )
names(rf.no.opti) = c("TP", "FN","FP","TN" , "sen", "spe", "pre", "fpr",
"fdr", "acc", "f1neg", "f1asd", "ac")
rf.no.opti
for ( i in 1:10 )
{
print(paste("working on fold:", i , sep = " "))
all_data_atc_scaled_com =all_data_atc_scaled_com[sample(1:nrow(all_data_atc_scaled_com),nrow(all_data_atc_scaled_com)),]
all_data_atc_scaled_com.sp = split( all_data_atc_scaled_com, f= all_data_atc_scaled_com$asd_status )
size.un = round(.8*nrow(all_data_atc_scaled_com.sp$`0`))
size.asd = round(.8*nrow(all_data_atc_scaled_com.sp$`1`))
total.un = 1:nrow(all_data_atc_scaled_com.sp$`0`)
total.asd = 1:nrow(all_data_atc_scaled_com.sp$`1`)
train.un = sample(1:nrow(all_data_atc_scaled_com.sp$`0`), size.un )
train.asd = sample(1:nrow(all_data_atc_scaled_com.sp$`1`), size.asd )
test.un = total.un[which(! total.un %in% train.un ) ]
test.asd = total.asd[which(! total.asd %in% train.asd ) ]
training.dat.un = all_data_atc_scaled_com.sp$`0`[train.un, ]
training.dat.asd = all_data_atc_scaled_com.sp$`1`[train.asd, ]
training.dat = rbind(training.dat.un, training.dat.asd)
testing.dat.un = all_data_atc_scaled_com.sp$`0`[test.un, ]
testing.dat.asd = all_data_atc_scaled_com.sp$`1`[test.asd, ]
testing.dat = rbind(testing.dat.un, testing.dat.asd)
testing.class = testing.dat$asd_status
testing.dat$asd_status = NULL
#training.smote = SMOTE(asd_status ~ . , data = training.dat , perc.over = 500, k = 100 )
#training.smote = training.smote[training.smote$asd_status == 1,]
#training.dat = training.dat[training.dat$asd_status == 0 , ]
training.dat = training.dat[sample(1:nrow(training.dat), nrow(training.dat)), ]
training.class = factor(training.dat$asd_status)
crfctrl = cforest_control(mtry =20 , ntree = 1000, trace = T)
fit_crf = cforest(formula = asd_status ~ . , data = training.dat , controls = crfctrl )
training.dat$asd_status = NULL
fit_mlp = mlp(x = data.matrix(training.dat), as.numeric(as.character(training.class)), size=c(10,10,10,2) , maxit = 100,
linOut =F)
fit_rf = randomForest(x = data.matrix(training.dat) , training.class,
ntree = 100, nthreads = 4 , mtry = 20,
localImp = F, proximity = F)
training.dat$training.class = as.factor(training.class)
fit_logi = glm(training.class ~ . , data = training.dat, family = "binomial" )
library(randomForest)
pred.prob.mlp = predict(fit_mlp, data.matrix(testing.dat))
pred.prob.crf = predict(fit_crf, data.matrix(testing.dat), type = "prob")
pred.prob.rf = predict(fit_rf, data.matrix(testing.dat), type = "prob")
pred.prob.log = predict(fit_logi, testing.dat, type = "response")
pred.prob.log = pred.prob.log %>% as.data.frame()
pred.prob.mlp.class = round(pred.prob.mlp) %>% as.data.frame()
pred.prob.rf.class = round(pred.prob.rf[,2]) %>% as.data.frame()
pred.prob.log.class = round(pred.prob.log) %>% as.data.frame()
names(pred.prob.all) = c("mlp", "rf", "logit")
pred.prob.all$class = testing.class
pred.prob.all$fold = rep(i, nrow(pred.prob.all))
get_perf = function(pred_class, pred_prob , obs_class )
{
df = cbind(pred_class, as.character(obs_class)) %>% as.data.frame()
colnames(df) = c("predicted", "observed")
q = table(df)
q[1] = q[1] %>% as.numeric()
q[2] = q[2] %>% as.numeric()
q[3] = q[3] %>% as.numeric()
q[4] = q[4] %>% as.numeric()
sen = q[4]/(q[4] + q[3])
spe = q[1]/(q[1] + q[2])
pre = q[4]/(q[4] + q[2])
fpr = q[2]/(q[2] + q[1])
fdr = q[2]/(q[2] + q[4])
acc = (q[1] + q[4] )/(q[1] + q[2] + q[3] + q[4] )
f1_asd = (2 * q[4] ) / ((2*q[4]) + q[2] + q[3] )
f1_neg = (2 * q[1] ) / ((2*q[1]) + q[2] + q[3] )
r = roc(predictor = pred_prob[,1] , response = obs_class )
ac = auc(r)
rw = c(q[4], q[3], q[2], q[1], sen, spe, pre, fpr , fdr, acc, f1_neg, f1_asd, ac ) %>% as.data.frame %>% t %>% as.data.frame()
return(rw)
}
varimp = fit_rf$importance %>% as.data.frame()
varimp$feat = row.names(varimp)
varimp$fold= rep(i, nrow(varimp))
rw.log = get_perf(pred.prob.log.class , pred.prob.log , testing.class )
names(rw.log) = c("TP", "FN","FP","TN" , "sen", "spe", "pre", "fpr",
"fdr", "acc", "f1neg", "f1asd", "ac")
rw.mlp = get_perf(pred.prob.mlp.class , pred.prob.mlp , testing.class )
names(rw.mlp) = c("TP", "FN","FP","TN" , "sen", "spe", "pre", "fpr",
"fdr", "acc", "f1neg", "f1asd", "ac")
rw.rf = get_perf(pred.prob.rf.class , pred.prob.rf , testing.class )
names(rw.rf) = c("TP", "FN","FP","TN" , "sen", "spe", "pre", "fpr",
"fdr", "acc", "f1neg", "f1asd", "ac")
combined = rbind(rw.log, rw.mlp, rw.rf) %>% as.data.table()
combined$learner = c("logit", "mlp", "rf")
combined$fold = rep(i, nrow(combined))
rocs_all = rbind(pred.prob.all, rocs_all)
performance_all = rbind(combined, performance_all)
varimp_all = rbind(varimp , varimp_all)
}
library(ggplot2)
save.image(file = "asd.10x.trained.level3.remove.missing.p.party.Rdata")
reorder(as.character(varimp_all$feat) , -varimp_all$MeanDecreaseGini , FUN = median) %>%
head(20)
###average variable importance
ggplot(varimp_all, aes( x = reorder(feat , -MeanDecreaseGini , FUN = median),
y = MeanDecreaseGini ,
fill = factor(feat) )) +
geom_boxplot() + coord_cartesian(xlim = 1:20 ) +
theme_bw() +
theme(
axis.text.x = element_text(size = 20, hjust = 1, vjust = 0, angle = 90),
axis.text.y = element_text(size = 20) ,
legend.position = "none"
)
####roc curves
performance_all = performance_all[complete.cases(performance_all),]
performance_all.sp = split(performance_all, f= performance_all$learner)
performance_all.sp$logit$ac %>% mean
performance_all.sp$mlp$ac %>% mean
performance_all.sp$rf$ac %>% mean
performance_all.sp$logit$f1asd %>% mean
performance_all.sp$mlp$f1asd %>% mean
performance_all.sp$rf$f1asd %>% mean
r = roc(predictor = rocs_all$logit , response = rocs_all$class )
plot.roc(r, legacy.axes = T)
#
# sp.all_data_atc_scaled_com = split(all_data_atc_scaled_com, f = all_data_atc_scaled_com$asd_status)
# sample.asd = round((9/10) * nrow(sp.all_data_atc_scaled_com$`1`))
# sample.un = round((9/10) * nrow(sp.all_data_atc_scaled_com$`0`))
# asd = sp.all_data_atc_scaled_com$`1`
# un = sp.all_data_atc_scaled_com$`0`
# asd.train = sample(1:nrow(asd), sample.asd, replace = F)
# un.train = sample(1:nrow(un), sample.un, replace = F)
# asd.tot = 1:nrow(asd)
# un.tot = 1:nrow(un)
# asd.vali = asd.tot[! asd.tot %in% asd.train ]
# un.vali = un.tot[! un.tot %in% un.train ]
# training.asd = asd[asd.train, ]
# training.un = un[un.train, ]
# training = rbind(training.asd, training.un)
# training$asd_status = factor(training$asd_status)
#
# training.smote = SMOTE(asd_status ~ . , data = training , perc.over = 500, k = 100 )
# training.smote = training.smote[training.smote$asd_status == 1 , ]
# training.smote = rbind(training.smote, training[training$asd_status == 0 , ])
#
# training.smote = training.smote[sample(nrow(training.smote)),]
# #training.smote$missing_p = NULL
# asd.stat = training.smote$asd_status
# training.smote$asd_status = NULL
# library(parallelRandomForest)
# #fit = parallelRandomForest::randomForest( x = data.matrix(training.smote), nodesize = 10,
# # y = asd.stat, ntree = 500, nthreads = 20,
# # mtry = 40, importance = T, verbose = T ,localImp = F, proximity = F )
# library(RSNNS)
# fit = mlp(x = data.matrix(training.smote), as.numeric(as.character(asd.stat)), size=10, linOut =F)
# #library(e1071)
# #fit = svm(data.matrix(training.smote), y = asd.stat , scale = F, kernel = "linear", probability = T )
# test.asd = asd[asd.vali, ]
# test.un = un[un.vali, ]
# testing = rbind(test.asd, test.un)
# testing = testing[sample(1:nrow(testing), nrow(testing), replace = F), ]
# #testing$missing_p = NULL
# case.test = testing$asd_status %>% as.character() %>% as.numeric() %>% as.factor()
# testing$asd_status= NULL
# library(randomForest)
#
# pred = predict(fit, testing )
#
# pred.prob = predict(fit, testing , type = "prob" )
# pred.prob = pred.prob %>% as.data.frame()
# sapply(pred.prob, function(x){
#
# })
# library(pROC)
# r = roc(predictor = pred.prob$V1, response = case.test)
#
# plot.roc(r, legacy.axes = T)
# pred = pred %>% as.character() %>% as.numeric()
# pred = round(pred, digits = 0) %>% as.vector()
# df = cbind(pred,as.character(case.test)) %>% as.data.frame()
# colnames(df) = c("predicted", "observed")
# q = table(df)
# q
# q[1] = q[1] %>% as.numeric()
# q[2] = q[2] %>% as.numeric()
# q[3] = q[3] %>% as.numeric()
# q[4] = q[4] %>% as.numeric()
# sen = q[4]/(q[4] + q[3])
# spe = q[1]/(q[1] + q[2])
# pre = q[4]/(q[4] + q[2])
# fpr = q[2]/(q[2] +q[1])
# fdr = q[2]/(q[2]+q[4])
# acc = (q[1] + q[4] )/(q[1] + q[2] + q[3] + q[4] )
# q
# auc(r)
# print(paste("Sensitivity: ", sen , "
# specificity: " , spe , "
# precision: " , pre , "
# fdr: " , fdr, "
# fpr: ", fpr , sep = ""))
#
#
#