R/all_test.R
In adriansidor/antminer:
test <- 3
loop_size<-10
cars <- read.csv(url("https://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data"), header = FALSE)
names(cars) <- c("buying", "maint", "doors", "persons", "lug_boot", "safety", "evaluation")
#10-fold-cross-validation
#sprawdzian krzyzowy (10 krotna walidacja)
cars_folds<-list()
cars_results<-list()
cars_model_time<-list()
cars_predict_time<-list()
cars_result_c50 <-list()
cars_conf_c50<-list()
cars_result_svm <-list()
cars_conf_svm<-list()
cars_result_bayes <-list()
cars_conf_bayes<-list()
cars_result_rpart <-list()
cars_conf_rpart<-list()
cars_result_antminer <-list()
cars_conf_antminer<-list()
cars_result_antminer2 <-list()
cars_conf_antminer2<-list()
cars_result_antminer3 <-list()
cars_conf_antminer3<-list()
cars_result_antminer4 <-list()
cars_conf_antminer4<-list()
for(t in 1:test) {
folds <- createFolds(cars$evaluation, k = loop_size, returnTrain = TRUE)
cars_folds[[t]]<-folds
#data frame with all results
results <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
results[1, 1]<-c("C5.0")
results[2, 1]<-c("svm")
results[3, 1]<-c("naive bayes")
results[4, 1]<-c("cart")
results[5, 1]<-c("antminer")
results[6, 1]<-c("antminer2")
results[7, 1]<-c("antminer3")
results[8, 1]<-c("antminer4")
model_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
model_time[1, 1]<-c("C5.0")
model_time[2, 1]<-c("svm")
model_time[3, 1]<-c("naive bayes")
model_time[4, 1]<-c("cart")
model_time[5, 1]<-c("antminer")
model_time[6, 1]<-c("antminer2")
model_time[7, 1]<-c("antminer3")
model_time[8, 1]<-c("antminer4")
predict_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
predict_time[1, 1]<-c("C5.0")
predict_time[2, 1]<-c("svm")
predict_time[3, 1]<-c("naive bayes")
predict_time[4, 1]<-c("cart")
predict_time[5, 1]<-c("antminer")
predict_time[6, 1]<-c("antminer2")
predict_time[7, 1]<-c("antminer3")
predict_time[8, 1]<-c("antminer4")
for(i in 1:loop_size) {
trainset<-cars[ folds[[i]] ,]
testset<-cars[ -folds[[i]] ,]
starttime<-Sys.time()
model_c50 <- C5.0(evaluation ~ ., data=trainset)
model_time[1, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_c50 <- predict(object=model_c50, newdata=testset, type="class")
predict_time[1, i+1]<-Sys.time()-starttime
tab_c50<-conf_matrix_table(result_c50, testset$evaluation)
conf_c50 <- confusionMatrix(tab_c50)
results[1, i+1]<-c(conf_c50$overall['Accuracy'])
cars_result_c50[t][[i]]<-result_c50
cars_conf_c50[t][[i]]<-conf_c50
starttime<-Sys.time()
model_svm <- svm(evaluation ~ ., data=trainset, type="C-classification")
model_time[2, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_svm <- predict(object=model_svm, newdata=testset)
predict_time[2, i+1]<-Sys.time()-starttime
tab_svm<-conf_matrix_table(result_svm, testset$evaluation)
conf_svm <- confusionMatrix(tab_svm)
results[2, i+1]<-c(conf_svm$overall['Accuracy'])
cars_result_svm[t][[i]]<-result_svm
cars_conf_svm[t][[i]]<-conf_svm
starttime<-Sys.time()
model_bayes <- naiveBayes(x = subset(trainset, select=-evaluation), y = trainset$evaluation)
model_time[3, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_bayes <- predict(object = model_bayes, newdata = testset, type = "class")
predict_time[3, i+1]<-Sys.time()-starttime
tab_bayes<-conf_matrix_table(result_bayes, testset$evaluation)
conf_bayes <- confusionMatrix(tab_bayes)
results[3, i+1]<-c(conf_bayes$overall['Accuracy'])
cars_result_bayes[t][[i]]<-result_bayes
cars_conf_bayes[t][[i]]<-conf_bayes
starttime<-Sys.time()
model_rpart <- rpart(evaluation ~ ., data=trainset)
model_time[4, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_rpart <- predict(object=model_rpart, newdata=testset, type="class")
predict_time[4, i+1]<-Sys.time()-starttime
tab6<-conf_matrix_table(result_rpart, testset$evaluation)
conf_rpart <- confusionMatrix(tab6)
results[4, i+1]<-c(conf_rpart$overall['Accuracy'])
cars_result_rpart[t][[i]]<-result_rpart
cars_conf_rpart[t][[i]]<-conf_rpart
starttime<-Sys.time()
model_antminer <- antminer(trainset, "evaluation", 10, 1000, 10, 10)
model_time[5, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer <- predict.antminer(model_antminer, subset(testset, select=-evaluation))
predict_time[5, i+1]<-Sys.time()-starttime
tab_antminer<-conf_matrix_table(result_antminer$class, testset$evaluation)
conf_antminer <- confusionMatrix(tab_antminer)
results[5, i+1]<-c(conf_antminer$overall['Accuracy'])
cars_result_antminer[t][[i]]<-result_antminer
cars_conf_antminer[t][[i]]<-conf_antminer
starttime<-Sys.time()
model_antminer2 <- antminer2(trainset, "evaluation", 10, 1000, 10, 10)
model_time[6, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer2 <- predict.antminer2(model_antminer2, subset(testset, select=-evaluation))
predict_time[6, i+1]<-Sys.time()-starttime
tab_antminer2<-conf_matrix_table(result_antminer2$class, testset$evaluation)
conf_antminer2 <- confusionMatrix(tab_antminer2)
results[6, i+1]<-c(conf_antminer2$overall['Accuracy'])
cars_result_antminer2[t][[i]]<-result_antminer2
cars_conf_antminer2[t][[i]]<-conf_antminer2
starttime<-Sys.time()
model_antminer3 <- antminer3(trainset, "evaluation", 10, 1000, 10, 10)
model_time[7, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer3 <- predict.antminer3(model_antminer3, subset(testset, select=-evaluation))
predict_time[7, i+1]<-Sys.time()-starttime
tab_antminer3<-conf_matrix_table(result_antminer3$class, testset$evaluation)
conf_antminer3 <- confusionMatrix(tab_antminer3)
results[7, i+1]<-c(conf_antminer3$overall['Accuracy'])
cars_result_antminer3[t][[i]]<-result_antminer3
cars_conf_antminer3[t][[i]]<-conf_antminer3
starttime<-Sys.time()
model_antminer4 <- antminer4(trainset, "evaluation", 10, 1000, 10, 10)
model_time[8, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer4 <- predict.antminer4(model_antminer4, subset(testset, select=-evaluation))
predict_time[8, i+1]<-Sys.time()-starttime
tab_antminer4<-conf_matrix_table(result_antminer4$class, testset$evaluation)
conf_antminer4 <- confusionMatrix(tab_antminer4)
results[8, i+1]<-c(conf_antminer4$overall['Accuracy'])
cars_result_antminer4[t][[i]]<-result_antminer4
cars_conf_antminer4[t][[i]]<-conf_antminer4
}
cars_results[[t]]<-results
cars_model_time[[t]]<-model_time
cars_predict_time[[t]]<-predict_time
}
breast_cancer_wisconsin <- read.csv(url("https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data"), header = FALSE)
breast_cancer_wisconsin<-breast_cancer_wisconsin[-1]
names(breast_cancer_wisconsin) <- c("Clump Thickness", "Uniformity of Cell Size", "Uniformity of Cell Shape", "Marginal Adhesion", "Single Epithelial Cell Size", "Bare Nuclei", "Bland Chromatin", "Normal Nucleoli", "Mitoses", "Class")
#przygotowanie danych
#usuniecie 16 przypadkow w ktorych brakuje danych
remove_list<-which(breast_cancer_wisconsin[,6]=='?')
breast_cancer_wisconsin<-breast_cancer_wisconsin[-remove_list,]
#kategoryzacja atrybutow ciaglych
#sa od 1 do 10 wiec nie trzeba
for(i in 1:9) {
breast_cancer_wisconsin[, i]<-as.factor(breast_cancer_wisconsin[, i])
}
#change class data type to factor
bening<-which(breast_cancer_wisconsin$Class==2)
malignant<-which(breast_cancer_wisconsin$Class==4)
breast_cancer_wisconsin$Class[bening]<-'bening'
breast_cancer_wisconsin$Class[malignant]<-'malignant'
breast_cancer_wisconsin$Class<-as.factor(breast_cancer_wisconsin$Class)
#10-fold-cross-validation
#sprawdzian krzyzowy (10 krotna walidacja)
breast_cancer_wisconsin_folds<-list()
breast_cancer_wisconsin_results<-list()
breast_cancer_wisconsin_model_time<-list()
breast_cancer_wisconsin_predict_time<-list()
breast_cancer_wisconsin_result_c50 <-list()
breast_cancer_wisconsin_conf_c50<-list()
breast_cancer_wisconsin_result_svm <-list()
breast_cancer_wisconsin_conf_svm<-list()
breast_cancer_wisconsin_result_bayes <-list()
breast_cancer_wisconsin_conf_bayes<-list()
breast_cancer_wisconsin_result_rpart <-list()
breast_cancer_wisconsin_conf_rpart<-list()
breast_cancer_wisconsin_result_antminer <-list()
breast_cancer_wisconsin_conf_antminer<-list()
breast_cancer_wisconsin_result_antminer2 <-list()
breast_cancer_wisconsin_conf_antminer2<-list()
breast_cancer_wisconsin_result_antminer3 <-list()
breast_cancer_wisconsin_conf_antminer3<-list()
breast_cancer_wisconsin_result_antminer4 <-list()
breast_cancer_wisconsin_conf_antminer4<-list()
for(t in 1:test) {
folds <- createFolds(breast_cancer_wisconsin$Class, k = loop_size, returnTrain = TRUE)
breast_cancer_wisconsin_folds[[t]]<-folds
#data frame with all results
results <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
results[1, 1]<-c("C5.0")
results[2, 1]<-c("svm")
results[3, 1]<-c("naive bayes")
results[4, 1]<-c("cart")
results[5, 1]<-c("antminer")
results[6, 1]<-c("antminer2")
results[7, 1]<-c("antminer3")
results[8, 1]<-c("antminer4")
model_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
model_time[1, 1]<-c("C5.0")
model_time[2, 1]<-c("svm")
model_time[3, 1]<-c("naive bayes")
model_time[4, 1]<-c("cart")
model_time[5, 1]<-c("antminer")
model_time[6, 1]<-c("antminer2")
model_time[7, 1]<-c("antminer3")
model_time[8, 1]<-c("antminer4")
predict_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
predict_time[1, 1]<-c("C5.0")
predict_time[2, 1]<-c("svm")
predict_time[3, 1]<-c("naive bayes")
predict_time[4, 1]<-c("cart")
predict_time[5, 1]<-c("antminer")
predict_time[6, 1]<-c("antminer2")
predict_time[7, 1]<-c("antminer3")
predict_time[8, 1]<-c("antminer4")
for(i in 1:loop_size) {
trainset<-breast_cancer_wisconsin[ folds[[i]] ,]
testset<-breast_cancer_wisconsin[ -folds[[i]] ,]
starttime<-Sys.time()
model_c50 <- C5.0(Class ~ ., data=trainset)
model_time[1, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_c50 <- predict(object=model_c50, newdata=testset, type="class")
predict_time[1, i+1]<-Sys.time()-starttime
tab_c50<-conf_matrix_table(result_c50, testset$Class)
conf_c50 <- confusionMatrix(tab_c50)
results[1, i+1]<-c(conf_c50$overall['Accuracy'])
breast_cancer_wisconsin_result_c50[t][[i]]<-result_c50
breast_cancer_wisconsin_conf_c50[t][[i]]<-conf_c50
starttime<-Sys.time()
model_svm <- svm(Class ~ ., data=trainset, type="C-classification")
model_time[2, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_svm <- predict(object=model_svm, newdata=testset)
predict_time[2, i+1]<-Sys.time()-starttime
tab_svm<-conf_matrix_table(result_svm, testset$Class)
conf_svm <- confusionMatrix(tab_svm)
results[2, i+1]<-c(conf_svm$overall['Accuracy'])
breast_cancer_wisconsin_result_svm[t][[i]]<-result_svm
breast_cancer_wisconsin_conf_svm[t][[i]]<-conf_svm
starttime<-Sys.time()
model_bayes <- naiveBayes(x = subset(trainset, select=-Class), y = trainset$Class)
model_time[3, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_bayes <- predict(object = model_bayes, newdata = testset, type = "class")
predict_time[3, i+1]<-Sys.time()-starttime
tab_bayes<-conf_matrix_table(result_bayes, testset$Class)
conf_bayes <- confusionMatrix(tab_bayes)
results[3, i+1]<-c(conf_bayes$overall['Accuracy'])
breast_cancer_wisconsin_result_bayes[t][[i]]<-result_bayes
breast_cancer_wisconsin_conf_bayes[t][[i]]<-conf_bayes
starttime<-Sys.time()
model_rpart <- rpart(Class ~ ., data=trainset)
model_time[4, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_rpart <- predict(object=model_rpart, newdata=testset, type="class")
predict_time[4, i+1]<-Sys.time()-starttime
tab6<-conf_matrix_table(result_rpart, testset$Class)
conf_rpart <- confusionMatrix(tab6)
results[4, i+1]<-c(conf_rpart$overall['Accuracy'])
breast_cancer_wisconsin_result_rpart[t][[i]]<-result_rpart
breast_cancer_wisconsin_conf_rpart[t][[i]]<-conf_rpart
starttime<-Sys.time()
model_antminer <- antminer(trainset, "Class", 10, 1000, 10, 10)
model_time[5, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer <- predict.antminer(model_antminer, subset(testset, select=-Class))
predict_time[5, i+1]<-Sys.time()-starttime
tab_antminer<-conf_matrix_table(result_antminer$class, testset$Class)
conf_antminer <- confusionMatrix(tab_antminer)
results[5, i+1]<-c(conf_antminer$overall['Accuracy'])
breast_cancer_wisconsin_result_antminer[t][[i]]<-result_antminer
breast_cancer_wisconsin_conf_antminer[t][[i]]<-conf_antminer
starttime<-Sys.time()
model_antminer2 <- antminer2(trainset, "Class", 10, 1000, 10, 10)
model_time[6, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer2 <- predict.antminer2(model_antminer2, subset(testset, select=-Class))
predict_time[6, i+1]<-Sys.time()-starttime
tab_antminer2<-conf_matrix_table(result_antminer2$class, testset$Class)
conf_antminer2 <- confusionMatrix(tab_antminer2)
results[6, i+1]<-c(conf_antminer2$overall['Accuracy'])
breast_cancer_wisconsin_result_antminer2[t][[i]]<-result_antminer2
breast_cancer_wisconsin_conf_antminer2[t][[i]]<-conf_antminer2
starttime<-Sys.time()
model_antminer3 <- antminer3(trainset, "Class", 10, 1000, 10, 10)
model_time[7, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer3 <- predict.antminer3(model_antminer3, subset(testset, select=-Class))
predict_time[7, i+1]<-Sys.time()-starttime
tab_antminer3<-conf_matrix_table(result_antminer3$class, testset$Class)
conf_antminer3 <- confusionMatrix(tab_antminer3)
results[7, i+1]<-c(conf_antminer3$overall['Accuracy'])
breast_cancer_wisconsin_result_antminer3[t][[i]]<-result_antminer3
breast_cancer_wisconsin_conf_antminer3[t][[i]]<-conf_antminer3
starttime<-Sys.time()
model_antminer4 <- antminer4(trainset, "Class", 10, 1000, 10, 10)
model_time[8, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer4 <- predict.antminer4(model_antminer4, subset(testset, select=-Class))
predict_time[8, i+1]<-Sys.time()-starttime
tab_antminer4<-conf_matrix_table(result_antminer4$class, testset$Class)
conf_antminer4 <- confusionMatrix(tab_antminer4)
results[8, i+1]<-c(conf_antminer4$overall['Accuracy'])
breast_cancer_wisconsin_result_antminer4[t][[i]]<-result_antminer4
breast_cancer_wisconsin_conf_antminer4[t][[i]]<-conf_antminer4
}
breast_cancer_wisconsin_results[[t]]<-results
breast_cancer_wisconsin_model_time[[t]]<-model_time
breast_cancer_wisconsin_predict_time[[t]]<-predict_time
}
nursery <- read.csv(url("https://archive.ics.uci.edu/ml/machine-learning-databases/nursery/nursery.data"), header = FALSE)
names(nursery) <- c("parents", "has_nurs", "form", "children", "housing", "finance", "social", "health", "application")
#10-fold-cross-validation
#sprawdzian krzyzowy (10 krotna walidacja)
nursery_folds<-list()
nursery_results<-list()
nursery_model_time<-list()
nursery_predict_time<-list()
nursery_result_c50 <-list()
nursery_conf_c50<-list()
nursery_result_svm <-list()
nursery_conf_svm<-list()
nursery_result_bayes <-list()
nursery_conf_bayes<-list()
nursery_result_rpart <-list()
nursery_conf_rpart<-list()
nursery_result_antminer <-list()
nursery_conf_antminer<-list()
nursery_result_antminer2 <-list()
nursery_conf_antminer2<-list()
nursery_result_antminer3 <-list()
nursery_conf_antminer3<-list()
nursery_result_antminer4 <-list()
nursery_conf_antminer4<-list()
t<-1
loop_size<-10
for(t in 1:test) {
folds <- createFolds(nursery$application, k = loop_size, returnTrain = TRUE)
nursery_folds[[t]]<-folds
#data frame with all results
results <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
results[1, 1]<-c("C5.0")
results[2, 1]<-c("svm")
results[3, 1]<-c("naive bayes")
results[4, 1]<-c("cart")
results[5, 1]<-c("antminer")
results[6, 1]<-c("antminer2")
results[7, 1]<-c("antminer3")
results[8, 1]<-c("antminer4")
model_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
model_time[1, 1]<-c("C5.0")
model_time[2, 1]<-c("svm")
model_time[3, 1]<-c("naive bayes")
model_time[4, 1]<-c("cart")
model_time[5, 1]<-c("antminer")
model_time[6, 1]<-c("antminer2")
model_time[7, 1]<-c("antminer3")
model_time[8, 1]<-c("antminer4")
predict_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
predict_time[1, 1]<-c("C5.0")
predict_time[2, 1]<-c("svm")
predict_time[3, 1]<-c("naive bayes")
predict_time[4, 1]<-c("cart")
predict_time[5, 1]<-c("antminer")
predict_time[6, 1]<-c("antminer2")
predict_time[7, 1]<-c("antminer3")
predict_time[8, 1]<-c("antminer4")
loop_size<-1
for(i in 1:loop_size) {
trainset<-nursery[ folds[[i]] ,]
testset<-nursery[ -folds[[i]] ,]
starttime<-Sys.time()
model_c50 <- C5.0(application ~ ., data=trainset)
model_time[1, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_c50 <- predict(object=model_c50, newdata=testset, type="class")
predict_time[1, i+1]<-Sys.time()-starttime
tab_c50<-conf_matrix_table(result_c50, testset$application)
conf_c50 <- confusionMatrix(tab_c50)
results[1, i+1]<-c(conf_c50$overall['Accuracy'])
nursery_result_c50[t][[i]]<-result_c50
nursery_conf_c50[t][[i]]<-conf_c50
starttime<-Sys.time()
model_svm <- svm(application ~ ., data=trainset, type="C-classification")
model_time[2, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_svm <- predict(object=model_svm, newdata=testset)
predict_time[2, i+1]<-Sys.time()-starttime
tab_svm<-conf_matrix_table(result_svm, testset$application)
conf_svm <- confusionMatrix(tab_svm)
results[2, i+1]<-c(conf_svm$overall['Accuracy'])
nursery_result_svm[t][[i]]<-result_svm
nursery_conf_svm[t][[i]]<-conf_svm
starttime<-Sys.time()
model_bayes <- naiveBayes(x = subset(trainset, select=-application), y = trainset$application)
model_time[3, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_bayes <- predict(object = model_bayes, newdata = testset, type = "class")
predict_time[3, i+1]<-Sys.time()-starttime
tab_bayes<-conf_matrix_table(result_bayes, testset$application)
conf_bayes <- confusionMatrix(tab_bayes)
results[3, i+1]<-c(conf_bayes$overall['Accuracy'])
nursery_result_bayes[t][[i]]<-result_bayes
nursery_conf_bayes[t][[i]]<-conf_bayes
starttime<-Sys.time()
model_rpart <- rpart(application ~ ., data=trainset)
model_time[4, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_rpart <- predict(object=model_rpart, newdata=testset, type="class")
predict_time[4, i+1]<-Sys.time()-starttime
tab6<-conf_matrix_table(result_rpart, testset$application)
conf_rpart <- confusionMatrix(tab6)
results[4, i+1]<-c(conf_rpart$overall['Accuracy'])
nursery_result_rpart[t][[i]]<-result_rpart
nursery_conf_rpart[t][[i]]<-conf_rpart
starttime<-Sys.time()
model_antminer <- antminer(trainset, "application", 10, 1000, 10, 10)
model_time[5, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer <- predict.antminer(model_antminer, subset(testset, select=-application))
predict_time[5, i+1]<-Sys.time()-starttime
tab_antminer<-conf_matrix_table(result_antminer$class, testset$application)
conf_antminer <- confusionMatrix(tab_antminer)
results[5, i+1]<-c(conf_antminer$overall['Accuracy'])
nursery_result_antminer[t][[i]]<-result_antminer
nursery_conf_antminer[t][[i]]<-conf_antminer
# starttime<-Sys.time()
# model_antminer2 <- antminer2(trainset, "application", 10, 1000, 10, 10)
# model_time[6, i+1]<-Sys.time()-starttime
# starttime<-Sys.time()
# result_antminer2 <- predict.antminer2(model_antminer2, subset(testset, select=-application))
# predict_time[6, i+1]<-Sys.time()-starttime
# tab_antminer2<-conf_matrix_table(result_antminer2$class, testset$application)
# conf_antminer2 <- confusionMatrix(tab_antminer2)
# results[6, i+1]<-c(conf_antminer2$overall['Accuracy'])
# nursery_result_antminer2[t][[i]]<-result_antminer2
# nursery_conf_antminer2[t][[i]]<-conf_antminer2
#
# starttime<-Sys.time()
# model_antminer3 <- antminer3(trainset, "application", 10, 1000, 10, 10)
# model_time[7, i+1]<-Sys.time()-starttime
# starttime<-Sys.time()
# result_antminer3 <- predict.antminer3(model_antminer3, subset(testset, select=-application))
# predict_time[7, i+1]<-Sys.time()-starttime
# tab_antminer3<-conf_matrix_table(result_antminer3$class, testset$application)
# conf_antminer3 <- confusionMatrix(tab_antminer3)
# results[7, i+1]<-c(conf_antminer3$overall['Accuracy'])
# nursery_result_antminer3[t][[i]]<-result_antminer3
# nursery_conf_antminer3[t][[i]]<-conf_antminer3
#
# starttime<-Sys.time()
# model_antminer4 <- antminer4(trainset, "application", 10, 1000, 10, 10)
# model_time[8, i+1]<-Sys.time()-starttime
# starttime<-Sys.time()
# result_antminer4 <- predict.antminer4(model_antminer4, subset(testset, select=-application))
# predict_time[8, i+1]<-Sys.time()-starttime
# tab_antminer4<-conf_matrix_table(result_antminer4$class, testset$application)
# conf_antminer4 <- confusionMatrix(tab_antminer4)
# results[8, i+1]<-c(conf_antminer4$overall['Accuracy'])
# nursery_result_antminer4[t][[i]]<-result_antminer4
# nursery_conf_antminer4[t][[i]]<-conf_antminer4
}
nursery_results[[t]]<-results
nursery_model_time[[t]]<-model_time
nursery_predict_time[[t]]<-predict_time
}
mushroom <- read.csv(url("https://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/agaricus-lepiota.data"), header = FALSE)
names(mushroom) <- c("edibility", "cap-shape", "cap_surface", "cap_color", "bruises?", "odor", "gill_attachment", "gill_spacing", "gill_size", "gill_color", "stalk_shape", "stalk_root", "stalk_surface_above_ring", "stalk_surface_below_ring", "stalk_color_above_ring", "stalk_color_below_ring", "veil_type", "veil_color", "ring_number", "ring_type", "spore_print_color", "population", "habitat")
mushroom <- mushroom[-17] #usuwanie atrybutu z jednym przypadkiem
#10-fold-cross-validation
#sprawdzian krzyzowy (10 krotna walidacja)
mushroom_folds<-list()
mushroom_results<-list()
mushroom_model_time<-list()
mushroom_predict_time<-list()
mushroom_result_c50 <-list()
mushroom_conf_c50<-list()
mushroom_result_svm <-list()
mushroom_conf_svm<-list()
mushroom_result_bayes <-list()
mushroom_conf_bayes<-list()
mushroom_result_rpart <-list()
mushroom_conf_rpart<-list()
mushroom_result_antminer <-list()
mushroom_conf_antminer<-list()
mushroom_result_antminer2 <-list()
mushroom_conf_antminer2<-list()
mushroom_result_antminer3 <-list()
mushroom_conf_antminer3<-list()
mushroom_result_antminer4 <-list()
mushroom_conf_antminer4<-list()
for(t in 1:test) {
folds <- createFolds(mushroom$edibility, k = loop_size, returnTrain = TRUE)
mushroom_folds[[t]]<-folds
#data frame with all results
results <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
results[1, 1]<-c("C5.0")
results[2, 1]<-c("svm")
results[3, 1]<-c("naive bayes")
results[4, 1]<-c("cart")
results[5, 1]<-c("antminer")
results[6, 1]<-c("antminer2")
results[7, 1]<-c("antminer3")
results[8, 1]<-c("antminer4")
model_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
model_time[1, 1]<-c("C5.0")
model_time[2, 1]<-c("svm")
model_time[3, 1]<-c("naive bayes")
model_time[4, 1]<-c("cart")
model_time[5, 1]<-c("antminer")
model_time[6, 1]<-c("antminer2")
model_time[7, 1]<-c("antminer3")
model_time[8, 1]<-c("antminer4")
predict_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
predict_time[1, 1]<-c("C5.0")
predict_time[2, 1]<-c("svm")
predict_time[3, 1]<-c("naive bayes")
predict_time[4, 1]<-c("cart")
predict_time[5, 1]<-c("antminer")
predict_time[6, 1]<-c("antminer2")
predict_time[7, 1]<-c("antminer3")
predict_time[8, 1]<-c("antminer4")
for(i in 1:loop_size) {
trainset<-mushroom[ folds[[i]] ,]
testset<-mushroom[ -folds[[i]] ,]
starttime<-Sys.time()
model_c50 <- C5.0(edibility ~ ., data=trainset)
model_time[1, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_c50 <- predict(object=model_c50, newdata=testset, type="class")
predict_time[1, i+1]<-Sys.time()-starttime
tab_c50<-conf_matrix_table(result_c50, testset$edibility)
conf_c50 <- confusionMatrix(tab_c50)
results[1, i+1]<-c(conf_c50$overall['Accuracy'])
mushroom_result_c50[t][[i]]<-result_c50
mushroom_conf_c50[t][[i]]<-conf_c50
starttime<-Sys.time()
model_svm <- svm(edibility ~ ., data=trainset, type="C-classification")
model_time[2, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_svm <- predict(object=model_svm, newdata=testset)
predict_time[2, i+1]<-Sys.time()-starttime
tab_svm<-conf_matrix_table(result_svm, testset$edibility)
conf_svm <- confusionMatrix(tab_svm)
results[2, i+1]<-c(conf_svm$overall['Accuracy'])
mushroom_result_svm[t][[i]]<-result_svm
mushroom_conf_svm[t][[i]]<-conf_svm
starttime<-Sys.time()
model_bayes <- naiveBayes(x = subset(trainset, select=-edibility), y = trainset$edibility)
model_time[3, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_bayes <- predict(object = model_bayes, newdata = testset, type = "class")
predict_time[3, i+1]<-Sys.time()-starttime
tab_bayes<-conf_matrix_table(result_bayes, testset$edibility)
conf_bayes <- confusionMatrix(tab_bayes)
results[3, i+1]<-c(conf_bayes$overall['Accuracy'])
mushroom_result_bayes[t][[i]]<-result_bayes
mushroom_conf_bayes[t][[i]]<-conf_bayes
starttime<-Sys.time()
model_rpart <- rpart(edibility ~ ., data=trainset)
model_time[4, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_rpart <- predict(object=model_rpart, newdata=testset, type="class")
predict_time[4, i+1]<-Sys.time()-starttime
tab6<-conf_matrix_table(result_rpart, testset$edibility)
conf_rpart <- confusionMatrix(tab6)
results[4, i+1]<-c(conf_rpart$overall['Accuracy'])
mushroom_result_rpart[t][[i]]<-result_rpart
mushroom_conf_rpart[t][[i]]<-conf_rpart
starttime<-Sys.time()
model_antminer <- antminer(trainset, "edibility", 10, 1000, 10, 10)
model_time[5, i+1]<-Sys.time()-starttime
Sys.time()-starttime
starttime<-Sys.time()
result_antminer <- predict.antminer(model_antminer, subset(testset, select=-edibility))
predict_time[5, i+1]<-Sys.time()-starttime
tab_antminer<-conf_matrix_table(result_antminer$class, testset$edibility)
conf_antminer <- confusionMatrix(tab_antminer)
results[5, i+1]<-c(conf_antminer$overall['Accuracy'])
mushroom_result_antminer[t][[i]]<-result_antminer
mushroom_conf_antminer[t][[i]]<-conf_antminer
starttime<-Sys.time()
model_antminer2 <- antminer2(trainset, "edibility", 10, 1000, 10, 10)
model_time[6, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer2 <- predict.antminer2(model_antminer2, subset(testset, select=-edibility))
predict_time[6, i+1]<-Sys.time()-starttime
tab_antminer2<-conf_matrix_table(result_antminer2$class, testset$edibility)
conf_antminer2 <- confusionMatrix(tab_antminer2)
results[6, i+1]<-c(conf_antminer2$overall['Accuracy'])
mushroom_result_antminer2[t][[i]]<-result_antminer2
mushroom_conf_antminer2[t][[i]]<-conf_antminer2
starttime<-Sys.time()
model_antminer3 <- antminer3(trainset, "edibility", 10, 1000, 10, 10)
model_time[7, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer3 <- predict.antminer3(model_antminer3, subset(testset, select=-edibility))
predict_time[7, i+1]<-Sys.time()-starttime
tab_antminer3<-conf_matrix_table(result_antminer3$class, testset$edibility)
conf_antminer3 <- confusionMatrix(tab_antminer3)
results[7, i+1]<-c(conf_antminer3$overall['Accuracy'])
mushroom_result_antminer3[t][[i]]<-result_antminer3
mushroom_conf_antminer3[t][[i]]<-conf_antminer3
starttime<-Sys.time()
model_antminer4 <- antminer4(trainset, "edibility", 10, 1000, 10, 10)
model_time[8, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer4 <- predict.antminer4(model_antminer4, subset(testset, select=-edibility))
predict_time[8, i+1]<-Sys.time()-starttime
tab_antminer4<-conf_matrix_table(result_antminer4$class, testset$edibility)
conf_antminer4 <- confusionMatrix(tab_antminer4)
results[8, i+1]<-c(conf_antminer4$overall['Accuracy'])
mushroom_result_antminer4[t][[i]]<-result_antminer4
mushroom_conf_antminer4[t][[i]]<-conf_antminer4
}
mushroom_results[[t]]<-results
mushroom_model_time[[t]]<-model_time
mushroom_predict_time[[t]]<-predict_time
}
tic_tac_toe <- read.csv(url("http://archive.ics.uci.edu/ml/machine-learning-databases/dermatology/dermatology.data"), header = FALSE)
names(tic_tac_toe) <- c("top-left-square", "top-middle-square", "top-right-square", "middle-left-square", "middle-middle-square", "middle-right-square", "bottom-left-square", "bottom-middle-square", "bottom-right-square", "Class")
# for(i in 1:10) {
# tic_tac_toe[, i]<-as.factor(tic_tac_toe[, i])
# }
#10-fold-cross-validation
#sprawdzian krzyzowy (10 krotna walidacja)
tic_tac_toe_folds<-list()
tic_tac_toe_results<-list()
tic_tac_toe_model_time<-list()
tic_tac_toe_predict_time<-list()
tic_tac_toe_result_c50 <-list()
tic_tac_toe_conf_c50<-list()
tic_tac_toe_result_svm <-list()
tic_tac_toe_conf_svm<-list()
tic_tac_toe_result_bayes <-list()
tic_tac_toe_conf_bayes<-list()
tic_tac_toe_result_rpart <-list()
tic_tac_toe_conf_rpart<-list()
tic_tac_toe_result_antminer <-list()
tic_tac_toe_conf_antminer<-list()
tic_tac_toe_result_antminer2 <-list()
tic_tac_toe_conf_antminer2<-list()
tic_tac_toe_result_antminer3 <-list()
tic_tac_toe_conf_antminer3<-list()
tic_tac_toe_result_antminer4 <-list()
tic_tac_toe_conf_antminer4<-list()
for(t in 1:test) {
folds <- createFolds(tic_tac_toe$Class, k = loop_size, returnTrain = TRUE)
tic_tac_toe_folds[[t]]<-folds
#data frame with all results
results <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
results[1, 1]<-c("C5.0")
results[2, 1]<-c("svm")
results[3, 1]<-c("naive bayes")
results[4, 1]<-c("cart")
results[5, 1]<-c("antminer")
results[6, 1]<-c("antminer2")
results[7, 1]<-c("antminer3")
results[8, 1]<-c("antminer4")
model_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
model_time[1, 1]<-c("C5.0")
model_time[2, 1]<-c("svm")
model_time[3, 1]<-c("naive bayes")
model_time[4, 1]<-c("cart")
model_time[5, 1]<-c("antminer")
model_time[6, 1]<-c("antminer2")
model_time[7, 1]<-c("antminer3")
model_time[8, 1]<-c("antminer4")
predict_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
predict_time[1, 1]<-c("C5.0")
predict_time[2, 1]<-c("svm")
predict_time[3, 1]<-c("naive bayes")
predict_time[4, 1]<-c("cart")
predict_time[5, 1]<-c("antminer")
predict_time[6, 1]<-c("antminer2")
predict_time[7, 1]<-c("antminer3")
predict_time[8, 1]<-c("antminer4")
for(i in 1:loop_size) {
trainset<-tic_tac_toe[ folds[[i]] ,]
testset<-tic_tac_toe[ -folds[[i]] ,]
starttime<-Sys.time()
model_c50 <- C5.0(Class ~ ., data=trainset)
model_time[1, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_c50 <- predict(object=model_c50, newdata=testset, type="class")
predict_time[1, i+1]<-Sys.time()-starttime
tab_c50<-conf_matrix_table(result_c50, testset$Class)
conf_c50 <- confusionMatrix(tab_c50)
results[1, i+1]<-c(conf_c50$overall['Accuracy'])
tic_tac_toe_result_c50[t][[i]]<-result_c50
tic_tac_toe_conf_c50[t][[i]]<-conf_c50
starttime<-Sys.time()
model_svm <- svm(Class ~ ., data=trainset, type="C-classification")
model_time[2, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_svm <- predict(object=model_svm, newdata=testset)
predict_time[2, i+1]<-Sys.time()-starttime
tab_svm<-conf_matrix_table(result_svm, testset$Class)
conf_svm <- confusionMatrix(tab_svm)
results[2, i+1]<-c(conf_svm$overall['Accuracy'])
tic_tac_toe_result_svm[t][[i]]<-result_svm
tic_tac_toe_conf_svm[t][[i]]<-conf_svm
starttime<-Sys.time()
model_bayes <- naiveBayes(x = subset(trainset, select=-Class), y = trainset$Class)
model_time[3, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_bayes <- predict(object = model_bayes, newdata = testset, type = "class")
predict_time[3, i+1]<-Sys.time()-starttime
tab_bayes<-conf_matrix_table(result_bayes, testset$Class)
conf_bayes <- confusionMatrix(tab_bayes)
results[3, i+1]<-c(conf_bayes$overall['Accuracy'])
tic_tac_toe_result_bayes[t][[i]]<-result_bayes
tic_tac_toe_conf_bayes[t][[i]]<-conf_bayes
starttime<-Sys.time()
model_rpart <- rpart(Class ~ ., data=trainset)
model_time[4, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_rpart <- predict(object=model_rpart, newdata=testset, type="class")
predict_time[4, i+1]<-Sys.time()-starttime
tab6<-conf_matrix_table(result_rpart, testset$Class)
conf_rpart <- confusionMatrix(tab6)
results[4, i+1]<-c(conf_rpart$overall['Accuracy'])
tic_tac_toe_result_rpart[t][[i]]<-result_rpart
tic_tac_toe_conf_rpart[t][[i]]<-conf_rpart
starttime<-Sys.time()
model_antminer <- antminer(trainset, "Class", 10, 1000, 10, 10)
model_time[5, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer <- predict.antminer(model_antminer, subset(testset, select=-Class))
predict_time[5, i+1]<-Sys.time()-starttime
tab_antminer<-conf_matrix_table(result_antminer$class, testset$Class)
conf_antminer <- confusionMatrix(tab_antminer)
results[5, i+1]<-c(conf_antminer$overall['Accuracy'])
tic_tac_toe_result_antminer[t][[i]]<-result_antminer
tic_tac_toe_conf_antminer[t][[i]]<-conf_antminer
starttime<-Sys.time()
model_antminer2 <- antminer2(trainset, "Class", 10, 1000, 10, 10)
model_time[6, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer2 <- predict.antminer2(model_antminer2, subset(testset, select=-Class))
predict_time[6, i+1]<-Sys.time()-starttime
tab_antminer2<-conf_matrix_table(result_antminer2$class, testset$Class)
conf_antminer2 <- confusionMatrix(tab_antminer2)
results[6, i+1]<-c(conf_antminer2$overall['Accuracy'])
tic_tac_toe_result_antminer2[t][[i]]<-result_antminer2
tic_tac_toe_conf_antminer2[t][[i]]<-conf_antminer2
starttime<-Sys.time()
model_antminer3 <- antminer3(trainset, "Class", 10, 1000, 10, 10)
model_time[7, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer3 <- predict.antminer3(model_antminer3, subset(testset, select=-Class))
predict_time[7, i+1]<-Sys.time()-starttime
tab_antminer3<-conf_matrix_table(result_antminer3$class, testset$Class)
conf_antminer3 <- confusionMatrix(tab_antminer3)
results[7, i+1]<-c(conf_antminer3$overall['Accuracy'])
tic_tac_toe_result_antminer3[t][[i]]<-result_antminer3
tic_tac_toe_conf_antminer3[t][[i]]<-conf_antminer3
starttime<-Sys.time()
model_antminer4 <- antminer4(trainset, "Class", 10, 1000, 10, 10)
model_time[8, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer4 <- predict.antminer4(model_antminer4, subset(testset, select=-Class))
predict_time[8, i+1]<-Sys.time()-starttime
tab_antminer4<-conf_matrix_table(result_antminer4$class, testset$Class)
conf_antminer4 <- confusionMatrix(tab_antminer4)
results[8, i+1]<-c(conf_antminer4$overall['Accuracy'])
tic_tac_toe_result_antminer4[t][[i]]<-result_antminer4
tic_tac_toe_conf_antminer4[t][[i]]<-conf_antminer4
}
tic_tac_toe_results[[t]]<-results
tic_tac_toe_model_time[[t]]<-model_time
tic_tac_toe_predict_time[[t]]<-predict_time
}
dermatology <- read.csv(url("http://archive.ics.uci.edu/ml/machine-learning-databases/dermatology/dermatology.data"), header = FALSE)
names(dermatology) <- c("erythema", "scaling", "definite borders", "itching", "koebner phenomenon", "polygonal papules", "follicular papules", "oral mucosal involvement", "knee and elbow involvement", "scalp involvement","family history","melanin incontinence","eosinophils in the infiltrate","PNL infiltrate","fibrosis of the papillary dermis","exocytosis","acanthosis","hyperkeratosis","parakeratosis","clubbing of the rete ridges","elongation of the rete ridges","thinning of the suprapapillary epidermis","spongiform pustule","munro microabcess","focal hypergranulosis","disappearance of the granular layer","vacuolisation and damage of basal layer","spongiosis","saw-tooth appearance of retes","follicular horn plug","perifollicular parakeratosis","inflammatory monoluclear inflitrate","band-like infiltrate","Age","diagnosis")
#usuniecie przypadkow w ktorych brakuje danych
remove_list<-which(dermatology[,34]=='?')
dermatology<-dermatology[-remove_list,]
# for(i in 1:10) {
# dermatology[, i]<-as.factor(dermatology[, i])
# }
dermatology$Age<-as.numeric(dermatology$Age)
dermatology$Age <- cut(dermatology$Age, breaks = seq(0, 80, by = 10))
for(i in 1:35) {
dermatology[, i]<-as.factor(dermatology[, i])
}
#10-fold-cross-validation
#sprawdzian krzyzowy (10 krotna walidacja)
dermatology_folds<-list()
dermatology_results<-list()
dermatology_model_time<-list()
dermatology_predict_time<-list()
dermatology_result_c50 <-list()
dermatology_conf_c50<-list()
dermatology_result_svm <-list()
dermatology_conf_svm<-list()
dermatology_result_bayes <-list()
dermatology_conf_bayes<-list()
dermatology_result_rpart <-list()
dermatology_conf_rpart<-list()
dermatology_result_antminer <-list()
dermatology_conf_antminer<-list()
dermatology_result_antminer2 <-list()
dermatology_conf_antminer2<-list()
dermatology_result_antminer3 <-list()
dermatology_conf_antminer3<-list()
dermatology_result_antminer4 <-list()
dermatology_conf_antminer4<-list()
for(t in 1:test) {
folds <- createFolds(dermatology$diagnosis, k = loop_size, returnTrain = TRUE)
dermatology_folds[[t]]<-folds
#data frame with all results
results <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
results[1, 1]<-c("C5.0")
results[2, 1]<-c("svm")
results[3, 1]<-c("naive bayes")
results[4, 1]<-c("cart")
results[5, 1]<-c("antminer")
results[6, 1]<-c("antminer2")
results[7, 1]<-c("antminer3")
results[8, 1]<-c("antminer4")
model_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
model_time[1, 1]<-c("C5.0")
model_time[2, 1]<-c("svm")
model_time[3, 1]<-c("naive bayes")
model_time[4, 1]<-c("cart")
model_time[5, 1]<-c("antminer")
model_time[6, 1]<-c("antminer2")
model_time[7, 1]<-c("antminer3")
model_time[8, 1]<-c("antminer4")
predict_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
predict_time[1, 1]<-c("C5.0")
predict_time[2, 1]<-c("svm")
predict_time[3, 1]<-c("naive bayes")
predict_time[4, 1]<-c("cart")
predict_time[5, 1]<-c("antminer")
predict_time[6, 1]<-c("antminer2")
predict_time[7, 1]<-c("antminer3")
predict_time[8, 1]<-c("antminer4")
for(i in 1:loop_size) {
trainset<-dermatology[ folds[[i]] ,]
testset<-dermatology[ -folds[[i]] ,]
starttime<-Sys.time()
model_c50 <- C5.0(diagnosis ~ ., data=trainset)
model_time[1, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_c50 <- predict(object=model_c50, newdata=testset, type="class")
predict_time[1, i+1]<-Sys.time()-starttime
tab_c50<-conf_matrix_table(result_c50, testset$diagnosis)
conf_c50 <- confusionMatrix(tab_c50)
results[1, i+1]<-c(conf_c50$overall['Accuracy'])
dermatology_result_c50[t][[i]]<-result_c50
dermatology_conf_c50[t][[i]]<-conf_c50
starttime<-Sys.time()
model_svm <- svm(diagnosis ~ ., data=trainset, type="C-classification")
model_time[2, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_svm <- predict(object=model_svm, newdata=testset)
predict_time[2, i+1]<-Sys.time()-starttime
tab_svm<-conf_matrix_table(result_svm, testset$diagnosis)
conf_svm <- confusionMatrix(tab_svm)
results[2, i+1]<-c(conf_svm$overall['Accuracy'])
dermatology_result_svm[t][[i]]<-result_svm
dermatology_conf_svm[t][[i]]<-conf_svm
starttime<-Sys.time()
model_bayes <- naiveBayes(x = subset(trainset, select=-diagnosis), y = trainset$diagnosis)
model_time[3, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_bayes <- predict(object = model_bayes, newdata = testset, type = "class")
predict_time[3, i+1]<-Sys.time()-starttime
tab_bayes<-conf_matrix_table(result_bayes, testset$diagnosis)
conf_bayes <- confusionMatrix(tab_bayes)
results[3, i+1]<-c(conf_bayes$overall['Accuracy'])
dermatology_result_bayes[t][[i]]<-result_bayes
dermatology_conf_bayes[t][[i]]<-conf_bayes
starttime<-Sys.time()
model_rpart <- rpart(diagnosis ~ ., data=trainset)
model_time[4, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_rpart <- predict(object=model_rpart, newdata=testset, type="class")
predict_time[4, i+1]<-Sys.time()-starttime
tab6<-conf_matrix_table(result_rpart, testset$diagnosis)
conf_rpart <- confusionMatrix(tab6)
results[4, i+1]<-c(conf_rpart$overall['Accuracy'])
dermatology_result_rpart[t][[i]]<-result_rpart
dermatology_conf_rpart[t][[i]]<-conf_rpart
starttime<-Sys.time()
model_antminer <- antminer(trainset, "diagnosis", 10, 1000, 10, 10)
model_time[5, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer <- predict.antminer(model_antminer, subset(testset, select=-diagnosis))
predict_time[5, i+1]<-Sys.time()-starttime
tab_antminer<-conf_matrix_table(result_antminer$class, testset$diagnosis)
conf_antminer <- confusionMatrix(tab_antminer)
results[5, i+1]<-c(conf_antminer$overall['Accuracy'])
dermatology_result_antminer[t][[i]]<-result_antminer
dermatology_conf_antminer[t][[i]]<-conf_antminer
starttime<-Sys.time()
model_antminer2 <- antminer2(trainset, "diagnosis", 10, 1000, 10, 10)
model_time[6, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer2 <- predict.antminer2(model_antminer2, subset(testset, select=-diagnosis))
predict_time[6, i+1]<-Sys.time()-starttime
tab_antminer2<-conf_matrix_table(result_antminer2$class, testset$diagnosis)
conf_antminer2 <- confusionMatrix(tab_antminer2)
results[6, i+1]<-c(conf_antminer2$overall['Accuracy'])
dermatology_result_antminer2[t][[i]]<-result_antminer2
dermatology_conf_antminer2[t][[i]]<-conf_antminer2
starttime<-Sys.time()
model_antminer3 <- antminer3(trainset, "diagnosis", 10, 1000, 10, 10)
model_time[7, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer3 <- predict.antminer3(model_antminer3, subset(testset, select=-diagnosis))
predict_time[7, i+1]<-Sys.time()-starttime
tab_antminer3<-conf_matrix_table(result_antminer3$class, testset$diagnosis)
conf_antminer3 <- confusionMatrix(tab_antminer3)
results[7, i+1]<-c(conf_antminer3$overall['Accuracy'])
dermatology_result_antminer3[t][[i]]<-result_antminer3
dermatology_conf_antminer3[t][[i]]<-conf_antminer3
starttime<-Sys.time()
model_antminer4 <- antminer4(trainset, "diagnosis", 10, 1000, 10, 10)
model_time[8, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer4 <- predict.antminer4(model_antminer4, subset(testset, select=-diagnosis))
predict_time[8, i+1]<-Sys.time()-starttime
tab_antminer4<-conf_matrix_table(result_antminer4$class, testset$diagnosis)
conf_antminer4 <- confusionMatrix(tab_antminer4)
results[8, i+1]<-c(conf_antminer4$overall['Accuracy'])
dermatology_result_antminer4[t][[i]]<-result_antminer4
dermatology_conf_antminer4[t][[i]]<-conf_antminer4
}
dermatology_results[[t]]<-results
dermatology_model_time[[t]]<-model_time
dermatology_predict_time[[t]]<-predict_time
}
heart_disease <- read.csv(url("http://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data"), header = FALSE)
names(heart_disease) <- c("age", "sex", "cp", "trestbps", "chol", "fbs", "restecg", "thalach", "exang","oldpeak","slope","ca","thal","num")
#usuniecie przypadkow w ktorych brakuje danych
remove_list<-which(heart_disease[,12]=='?')
heart_disease<-heart_disease[-remove_list,]
remove_list<-which(heart_disease[,13]=='?')
heart_disease<-heart_disease[-remove_list,]
# for(i in 1:10) {
# dermatology[, i]<-as.factor(dermatology[, i])
# }
heart_disease$age<-as.numeric(heart_disease$age)
heart_disease$age <- cut(heart_disease$age, breaks = seq(20, 80, by = 10))
heart_disease$trestbps <- cut(heart_disease$trestbps, breaks = seq(90, 200, by = 10))
heart_disease$chol<-as.numeric(heart_disease$chol)
heart_disease$chol <- cut(heart_disease$chol, breaks = seq(125, 575, by = 25))
heart_disease$thalach <- cut(heart_disease$thalach, breaks = seq(70, 210, by = 10))
heart_disease$oldpeak<-as.numeric(heart_disease$oldpeak)
heart_disease$oldpeak <- cut(heart_disease$oldpeak, breaks = seq(0, 6.5, by = 0.5), right=FALSE)
for(i in 1:14) {
heart_disease[, i]<-as.factor(heart_disease[, i])
}
#10-fold-cross-validation
#sprawdzian krzyzowy (10 krotna walidacja)
heart_disease_folds<-list()
heart_disease_results<-list()
heart_disease_model_time<-list()
heart_disease_predict_time<-list()
heart_disease_result_c50 <-list()
heart_disease_conf_c50<-list()
heart_disease_result_svm <-list()
heart_disease_conf_svm<-list()
heart_disease_result_bayes <-list()
heart_disease_conf_bayes<-list()
heart_disease_result_rpart <-list()
heart_disease_conf_rpart<-list()
heart_disease_result_antminer <-list()
heart_disease_conf_antminer<-list()
heart_disease_result_antminer2 <-list()
heart_disease_conf_antminer2<-list()
heart_disease_result_antminer3 <-list()
heart_disease_conf_antminer3<-list()
heart_disease_result_antminer4 <-list()
heart_disease_conf_antminer4<-list()
for(t in 1:test) {
folds <- createFolds(heart_disease$num, k = loop_size, returnTrain = TRUE)
heart_disease_folds[[t]]<-folds
#data frame with all results
results <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
results[1, 1]<-c("C5.0")
results[2, 1]<-c("svm")
results[3, 1]<-c("naive bayes")
results[4, 1]<-c("cart")
results[5, 1]<-c("antminer")
results[6, 1]<-c("antminer2")
results[7, 1]<-c("antminer3")
results[8, 1]<-c("antminer4")
model_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
model_time[1, 1]<-c("C5.0")
model_time[2, 1]<-c("svm")
model_time[3, 1]<-c("naive bayes")
model_time[4, 1]<-c("cart")
model_time[5, 1]<-c("antminer")
model_time[6, 1]<-c("antminer2")
model_time[7, 1]<-c("antminer3")
model_time[8, 1]<-c("antminer4")
predict_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
predict_time[1, 1]<-c("C5.0")
predict_time[2, 1]<-c("svm")
predict_time[3, 1]<-c("naive bayes")
predict_time[4, 1]<-c("cart")
predict_time[5, 1]<-c("antminer")
predict_time[6, 1]<-c("antminer2")
predict_time[7, 1]<-c("antminer3")
predict_time[8, 1]<-c("antminer4")
for(i in 1:loop_size) {
trainset<-heart_disease[ folds[[i]] ,]
testset<-heart_disease[ -folds[[i]] ,]
starttime<-Sys.time()
model_c50 <- C5.0(num ~ ., data=trainset)
model_time[1, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_c50 <- predict(object=model_c50, newdata=testset, type="class")
predict_time[1, i+1]<-Sys.time()-starttime
tab_c50<-conf_matrix_table(result_c50, testset$num)
conf_c50 <- confusionMatrix(tab_c50)
results[1, i+1]<-c(conf_c50$overall['Accuracy'])
heart_disease_result_c50[t][[i]]<-result_c50
heart_disease_conf_c50[t][[i]]<-conf_c50
starttime<-Sys.time()
model_svm <- svm(num ~ ., data=trainset, type="C-classification")
model_time[2, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_svm <- predict(object=model_svm, newdata=testset)
predict_time[2, i+1]<-Sys.time()-starttime
tab_svm<-conf_matrix_table(result_svm, testset$num)
conf_svm <- confusionMatrix(tab_svm)
results[2, i+1]<-c(conf_svm$overall['Accuracy'])
heart_disease_result_svm[t][[i]]<-result_svm
heart_disease_conf_svm[t][[i]]<-conf_svm
starttime<-Sys.time()
model_bayes <- naiveBayes(x = subset(trainset, select=-num), y = trainset$num)
model_time[3, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_bayes <- predict(object = model_bayes, newdata = testset, type = "class")
predict_time[3, i+1]<-Sys.time()-starttime
tab_bayes<-conf_matrix_table(result_bayes, testset$num)
conf_bayes <- confusionMatrix(tab_bayes)
results[3, i+1]<-c(conf_bayes$overall['Accuracy'])
heart_disease_result_bayes[t][[i]]<-result_bayes
heart_disease_conf_bayes[t][[i]]<-conf_bayes
starttime<-Sys.time()
model_rpart <- rpart(num ~ ., data=trainset)
model_time[4, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_rpart <- predict(object=model_rpart, newdata=testset, type="class")
predict_time[4, i+1]<-Sys.time()-starttime
tab6<-conf_matrix_table(result_rpart, testset$num)
conf_rpart <- confusionMatrix(tab6)
results[4, i+1]<-c(conf_rpart$overall['Accuracy'])
heart_disease_result_rpart[t][[i]]<-result_rpart
heart_disease_conf_rpart[t][[i]]<-conf_rpart
starttime<-Sys.time()
model_antminer <- antminer(trainset, "num", 10, 1000, 10, 10)
model_time[5, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer <- predict.antminer(model_antminer, subset(testset, select=-num))
predict_time[5, i+1]<-Sys.time()-starttime
tab_antminer<-conf_matrix_table(result_antminer$class, testset$num)
conf_antminer <- confusionMatrix(tab_antminer)
results[5, i+1]<-c(conf_antminer$overall['Accuracy'])
heart_disease_result_antminer[t][[i]]<-result_antminer
heart_disease_conf_antminer[t][[i]]<-conf_antminer
starttime<-Sys.time()
model_antminer2 <- antminer2(trainset, "num", 10, 1000, 10, 10)
model_time[6, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer2 <- predict.antminer2(model_antminer2, subset(testset, select=-num))
predict_time[6, i+1]<-Sys.time()-starttime
tab_antminer2<-conf_matrix_table(result_antminer2$class, testset$num)
conf_antminer2 <- confusionMatrix(tab_antminer2)
results[6, i+1]<-c(conf_antminer2$overall['Accuracy'])
heart_disease_result_antminer2[t][[i]]<-result_antminer2
heart_disease_conf_antminer2[t][[i]]<-conf_antminer2
starttime<-Sys.time()
model_antminer3 <- antminer3(trainset, "num", 10, 1000, 10, 10)
model_time[7, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer3 <- predict.antminer3(model_antminer3, subset(testset, select=-num))
predict_time[7, i+1]<-Sys.time()-starttime
tab_antminer3<-conf_matrix_table(result_antminer3$class, testset$num)
conf_antminer3 <- confusionMatrix(tab_antminer3)
results[7, i+1]<-c(conf_antminer3$overall['Accuracy'])
heart_disease_result_antminer3[t][[i]]<-result_antminer3
heart_disease_conf_antminer3[t][[i]]<-conf_antminer3
starttime<-Sys.time()
model_antminer4 <- antminer4(trainset, "num", 10, 1000, 10, 10)
model_time[8, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer4 <- predict.antminer4(model_antminer4, subset(testset, select=-num))
predict_time[8, i+1]<-Sys.time()-starttime
tab_antminer4<-conf_matrix_table(result_antminer4$class, testset$num)
conf_antminer4 <- confusionMatrix(tab_antminer4)
results[8, i+1]<-c(conf_antminer4$overall['Accuracy'])
heart_disease_result_antminer4[t][[i]]<-result_antminer4
heart_disease_conf_antminer4[t][[i]]<-conf_antminer4
}
heart_disease_results[[t]]<-results
heart_disease_model_time[[t]]<-model_time
heart_disease_predict_time[[t]]<-predict_time
}
adriansidor/antminer documentation built on May 20, 2019, 3:28 p.m.
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test <- 3
loop_size<-10
cars <- read.csv(url("https://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data"), header = FALSE)
names(cars) <- c("buying", "maint", "doors", "persons", "lug_boot", "safety", "evaluation")
#10-fold-cross-validation
#sprawdzian krzyzowy (10 krotna walidacja)
cars_folds<-list()
cars_results<-list()
cars_model_time<-list()
cars_predict_time<-list()
cars_result_c50 <-list()
cars_conf_c50<-list()
cars_result_svm <-list()
cars_conf_svm<-list()
cars_result_bayes <-list()
cars_conf_bayes<-list()
cars_result_rpart <-list()
cars_conf_rpart<-list()
cars_result_antminer <-list()
cars_conf_antminer<-list()
cars_result_antminer2 <-list()
cars_conf_antminer2<-list()
cars_result_antminer3 <-list()
cars_conf_antminer3<-list()
cars_result_antminer4 <-list()
cars_conf_antminer4<-list()
for(t in 1:test) {
folds <- createFolds(cars$evaluation, k = loop_size, returnTrain = TRUE)
cars_folds[[t]]<-folds
#data frame with all results
results <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
results[1, 1]<-c("C5.0")
results[2, 1]<-c("svm")
results[3, 1]<-c("naive bayes")
results[4, 1]<-c("cart")
results[5, 1]<-c("antminer")
results[6, 1]<-c("antminer2")
results[7, 1]<-c("antminer3")
results[8, 1]<-c("antminer4")
model_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
model_time[1, 1]<-c("C5.0")
model_time[2, 1]<-c("svm")
model_time[3, 1]<-c("naive bayes")
model_time[4, 1]<-c("cart")
model_time[5, 1]<-c("antminer")
model_time[6, 1]<-c("antminer2")
model_time[7, 1]<-c("antminer3")
model_time[8, 1]<-c("antminer4")
predict_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
predict_time[1, 1]<-c("C5.0")
predict_time[2, 1]<-c("svm")
predict_time[3, 1]<-c("naive bayes")
predict_time[4, 1]<-c("cart")
predict_time[5, 1]<-c("antminer")
predict_time[6, 1]<-c("antminer2")
predict_time[7, 1]<-c("antminer3")
predict_time[8, 1]<-c("antminer4")
for(i in 1:loop_size) {
trainset<-cars[ folds[[i]] ,]
testset<-cars[ -folds[[i]] ,]
starttime<-Sys.time()
model_c50 <- C5.0(evaluation ~ ., data=trainset)
model_time[1, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_c50 <- predict(object=model_c50, newdata=testset, type="class")
predict_time[1, i+1]<-Sys.time()-starttime
tab_c50<-conf_matrix_table(result_c50, testset$evaluation)
conf_c50 <- confusionMatrix(tab_c50)
results[1, i+1]<-c(conf_c50$overall['Accuracy'])
cars_result_c50[t][[i]]<-result_c50
cars_conf_c50[t][[i]]<-conf_c50
starttime<-Sys.time()
model_svm <- svm(evaluation ~ ., data=trainset, type="C-classification")
model_time[2, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_svm <- predict(object=model_svm, newdata=testset)
predict_time[2, i+1]<-Sys.time()-starttime
tab_svm<-conf_matrix_table(result_svm, testset$evaluation)
conf_svm <- confusionMatrix(tab_svm)
results[2, i+1]<-c(conf_svm$overall['Accuracy'])
cars_result_svm[t][[i]]<-result_svm
cars_conf_svm[t][[i]]<-conf_svm
starttime<-Sys.time()
model_bayes <- naiveBayes(x = subset(trainset, select=-evaluation), y = trainset$evaluation)
model_time[3, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_bayes <- predict(object = model_bayes, newdata = testset, type = "class")
predict_time[3, i+1]<-Sys.time()-starttime
tab_bayes<-conf_matrix_table(result_bayes, testset$evaluation)
conf_bayes <- confusionMatrix(tab_bayes)
results[3, i+1]<-c(conf_bayes$overall['Accuracy'])
cars_result_bayes[t][[i]]<-result_bayes
cars_conf_bayes[t][[i]]<-conf_bayes
starttime<-Sys.time()
model_rpart <- rpart(evaluation ~ ., data=trainset)
model_time[4, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_rpart <- predict(object=model_rpart, newdata=testset, type="class")
predict_time[4, i+1]<-Sys.time()-starttime
tab6<-conf_matrix_table(result_rpart, testset$evaluation)
conf_rpart <- confusionMatrix(tab6)
results[4, i+1]<-c(conf_rpart$overall['Accuracy'])
cars_result_rpart[t][[i]]<-result_rpart
cars_conf_rpart[t][[i]]<-conf_rpart
starttime<-Sys.time()
model_antminer <- antminer(trainset, "evaluation", 10, 1000, 10, 10)
model_time[5, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer <- predict.antminer(model_antminer, subset(testset, select=-evaluation))
predict_time[5, i+1]<-Sys.time()-starttime
tab_antminer<-conf_matrix_table(result_antminer$class, testset$evaluation)
conf_antminer <- confusionMatrix(tab_antminer)
results[5, i+1]<-c(conf_antminer$overall['Accuracy'])
cars_result_antminer[t][[i]]<-result_antminer
cars_conf_antminer[t][[i]]<-conf_antminer
starttime<-Sys.time()
model_antminer2 <- antminer2(trainset, "evaluation", 10, 1000, 10, 10)
model_time[6, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer2 <- predict.antminer2(model_antminer2, subset(testset, select=-evaluation))
predict_time[6, i+1]<-Sys.time()-starttime
tab_antminer2<-conf_matrix_table(result_antminer2$class, testset$evaluation)
conf_antminer2 <- confusionMatrix(tab_antminer2)
results[6, i+1]<-c(conf_antminer2$overall['Accuracy'])
cars_result_antminer2[t][[i]]<-result_antminer2
cars_conf_antminer2[t][[i]]<-conf_antminer2
starttime<-Sys.time()
model_antminer3 <- antminer3(trainset, "evaluation", 10, 1000, 10, 10)
model_time[7, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer3 <- predict.antminer3(model_antminer3, subset(testset, select=-evaluation))
predict_time[7, i+1]<-Sys.time()-starttime
tab_antminer3<-conf_matrix_table(result_antminer3$class, testset$evaluation)
conf_antminer3 <- confusionMatrix(tab_antminer3)
results[7, i+1]<-c(conf_antminer3$overall['Accuracy'])
cars_result_antminer3[t][[i]]<-result_antminer3
cars_conf_antminer3[t][[i]]<-conf_antminer3
starttime<-Sys.time()
model_antminer4 <- antminer4(trainset, "evaluation", 10, 1000, 10, 10)
model_time[8, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer4 <- predict.antminer4(model_antminer4, subset(testset, select=-evaluation))
predict_time[8, i+1]<-Sys.time()-starttime
tab_antminer4<-conf_matrix_table(result_antminer4$class, testset$evaluation)
conf_antminer4 <- confusionMatrix(tab_antminer4)
results[8, i+1]<-c(conf_antminer4$overall['Accuracy'])
cars_result_antminer4[t][[i]]<-result_antminer4
cars_conf_antminer4[t][[i]]<-conf_antminer4
}
cars_results[[t]]<-results
cars_model_time[[t]]<-model_time
cars_predict_time[[t]]<-predict_time
}
breast_cancer_wisconsin <- read.csv(url("https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data"), header = FALSE)
breast_cancer_wisconsin<-breast_cancer_wisconsin[-1]
names(breast_cancer_wisconsin) <- c("Clump Thickness", "Uniformity of Cell Size", "Uniformity of Cell Shape", "Marginal Adhesion", "Single Epithelial Cell Size", "Bare Nuclei", "Bland Chromatin", "Normal Nucleoli", "Mitoses", "Class")
#przygotowanie danych
#usuniecie 16 przypadkow w ktorych brakuje danych
remove_list<-which(breast_cancer_wisconsin[,6]=='?')
breast_cancer_wisconsin<-breast_cancer_wisconsin[-remove_list,]
#kategoryzacja atrybutow ciaglych
#sa od 1 do 10 wiec nie trzeba
for(i in 1:9) {
breast_cancer_wisconsin[, i]<-as.factor(breast_cancer_wisconsin[, i])
}
#change class data type to factor
bening<-which(breast_cancer_wisconsin$Class==2)
malignant<-which(breast_cancer_wisconsin$Class==4)
breast_cancer_wisconsin$Class[bening]<-'bening'
breast_cancer_wisconsin$Class[malignant]<-'malignant'
breast_cancer_wisconsin$Class<-as.factor(breast_cancer_wisconsin$Class)
#10-fold-cross-validation
#sprawdzian krzyzowy (10 krotna walidacja)
breast_cancer_wisconsin_folds<-list()
breast_cancer_wisconsin_results<-list()
breast_cancer_wisconsin_model_time<-list()
breast_cancer_wisconsin_predict_time<-list()
breast_cancer_wisconsin_result_c50 <-list()
breast_cancer_wisconsin_conf_c50<-list()
breast_cancer_wisconsin_result_svm <-list()
breast_cancer_wisconsin_conf_svm<-list()
breast_cancer_wisconsin_result_bayes <-list()
breast_cancer_wisconsin_conf_bayes<-list()
breast_cancer_wisconsin_result_rpart <-list()
breast_cancer_wisconsin_conf_rpart<-list()
breast_cancer_wisconsin_result_antminer <-list()
breast_cancer_wisconsin_conf_antminer<-list()
breast_cancer_wisconsin_result_antminer2 <-list()
breast_cancer_wisconsin_conf_antminer2<-list()
breast_cancer_wisconsin_result_antminer3 <-list()
breast_cancer_wisconsin_conf_antminer3<-list()
breast_cancer_wisconsin_result_antminer4 <-list()
breast_cancer_wisconsin_conf_antminer4<-list()
for(t in 1:test) {
folds <- createFolds(breast_cancer_wisconsin$Class, k = loop_size, returnTrain = TRUE)
breast_cancer_wisconsin_folds[[t]]<-folds
#data frame with all results
results <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
results[1, 1]<-c("C5.0")
results[2, 1]<-c("svm")
results[3, 1]<-c("naive bayes")
results[4, 1]<-c("cart")
results[5, 1]<-c("antminer")
results[6, 1]<-c("antminer2")
results[7, 1]<-c("antminer3")
results[8, 1]<-c("antminer4")
model_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
model_time[1, 1]<-c("C5.0")
model_time[2, 1]<-c("svm")
model_time[3, 1]<-c("naive bayes")
model_time[4, 1]<-c("cart")
model_time[5, 1]<-c("antminer")
model_time[6, 1]<-c("antminer2")
model_time[7, 1]<-c("antminer3")
model_time[8, 1]<-c("antminer4")
predict_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
predict_time[1, 1]<-c("C5.0")
predict_time[2, 1]<-c("svm")
predict_time[3, 1]<-c("naive bayes")
predict_time[4, 1]<-c("cart")
predict_time[5, 1]<-c("antminer")
predict_time[6, 1]<-c("antminer2")
predict_time[7, 1]<-c("antminer3")
predict_time[8, 1]<-c("antminer4")
for(i in 1:loop_size) {
trainset<-breast_cancer_wisconsin[ folds[[i]] ,]
testset<-breast_cancer_wisconsin[ -folds[[i]] ,]
starttime<-Sys.time()
model_c50 <- C5.0(Class ~ ., data=trainset)
model_time[1, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_c50 <- predict(object=model_c50, newdata=testset, type="class")
predict_time[1, i+1]<-Sys.time()-starttime
tab_c50<-conf_matrix_table(result_c50, testset$Class)
conf_c50 <- confusionMatrix(tab_c50)
results[1, i+1]<-c(conf_c50$overall['Accuracy'])
breast_cancer_wisconsin_result_c50[t][[i]]<-result_c50
breast_cancer_wisconsin_conf_c50[t][[i]]<-conf_c50
starttime<-Sys.time()
model_svm <- svm(Class ~ ., data=trainset, type="C-classification")
model_time[2, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_svm <- predict(object=model_svm, newdata=testset)
predict_time[2, i+1]<-Sys.time()-starttime
tab_svm<-conf_matrix_table(result_svm, testset$Class)
conf_svm <- confusionMatrix(tab_svm)
results[2, i+1]<-c(conf_svm$overall['Accuracy'])
breast_cancer_wisconsin_result_svm[t][[i]]<-result_svm
breast_cancer_wisconsin_conf_svm[t][[i]]<-conf_svm
starttime<-Sys.time()
model_bayes <- naiveBayes(x = subset(trainset, select=-Class), y = trainset$Class)
model_time[3, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_bayes <- predict(object = model_bayes, newdata = testset, type = "class")
predict_time[3, i+1]<-Sys.time()-starttime
tab_bayes<-conf_matrix_table(result_bayes, testset$Class)
conf_bayes <- confusionMatrix(tab_bayes)
results[3, i+1]<-c(conf_bayes$overall['Accuracy'])
breast_cancer_wisconsin_result_bayes[t][[i]]<-result_bayes
breast_cancer_wisconsin_conf_bayes[t][[i]]<-conf_bayes
starttime<-Sys.time()
model_rpart <- rpart(Class ~ ., data=trainset)
model_time[4, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_rpart <- predict(object=model_rpart, newdata=testset, type="class")
predict_time[4, i+1]<-Sys.time()-starttime
tab6<-conf_matrix_table(result_rpart, testset$Class)
conf_rpart <- confusionMatrix(tab6)
results[4, i+1]<-c(conf_rpart$overall['Accuracy'])
breast_cancer_wisconsin_result_rpart[t][[i]]<-result_rpart
breast_cancer_wisconsin_conf_rpart[t][[i]]<-conf_rpart
starttime<-Sys.time()
model_antminer <- antminer(trainset, "Class", 10, 1000, 10, 10)
model_time[5, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer <- predict.antminer(model_antminer, subset(testset, select=-Class))
predict_time[5, i+1]<-Sys.time()-starttime
tab_antminer<-conf_matrix_table(result_antminer$class, testset$Class)
conf_antminer <- confusionMatrix(tab_antminer)
results[5, i+1]<-c(conf_antminer$overall['Accuracy'])
breast_cancer_wisconsin_result_antminer[t][[i]]<-result_antminer
breast_cancer_wisconsin_conf_antminer[t][[i]]<-conf_antminer
starttime<-Sys.time()
model_antminer2 <- antminer2(trainset, "Class", 10, 1000, 10, 10)
model_time[6, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer2 <- predict.antminer2(model_antminer2, subset(testset, select=-Class))
predict_time[6, i+1]<-Sys.time()-starttime
tab_antminer2<-conf_matrix_table(result_antminer2$class, testset$Class)
conf_antminer2 <- confusionMatrix(tab_antminer2)
results[6, i+1]<-c(conf_antminer2$overall['Accuracy'])
breast_cancer_wisconsin_result_antminer2[t][[i]]<-result_antminer2
breast_cancer_wisconsin_conf_antminer2[t][[i]]<-conf_antminer2
starttime<-Sys.time()
model_antminer3 <- antminer3(trainset, "Class", 10, 1000, 10, 10)
model_time[7, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer3 <- predict.antminer3(model_antminer3, subset(testset, select=-Class))
predict_time[7, i+1]<-Sys.time()-starttime
tab_antminer3<-conf_matrix_table(result_antminer3$class, testset$Class)
conf_antminer3 <- confusionMatrix(tab_antminer3)
results[7, i+1]<-c(conf_antminer3$overall['Accuracy'])
breast_cancer_wisconsin_result_antminer3[t][[i]]<-result_antminer3
breast_cancer_wisconsin_conf_antminer3[t][[i]]<-conf_antminer3
starttime<-Sys.time()
model_antminer4 <- antminer4(trainset, "Class", 10, 1000, 10, 10)
model_time[8, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer4 <- predict.antminer4(model_antminer4, subset(testset, select=-Class))
predict_time[8, i+1]<-Sys.time()-starttime
tab_antminer4<-conf_matrix_table(result_antminer4$class, testset$Class)
conf_antminer4 <- confusionMatrix(tab_antminer4)
results[8, i+1]<-c(conf_antminer4$overall['Accuracy'])
breast_cancer_wisconsin_result_antminer4[t][[i]]<-result_antminer4
breast_cancer_wisconsin_conf_antminer4[t][[i]]<-conf_antminer4
}
breast_cancer_wisconsin_results[[t]]<-results
breast_cancer_wisconsin_model_time[[t]]<-model_time
breast_cancer_wisconsin_predict_time[[t]]<-predict_time
}
nursery <- read.csv(url("https://archive.ics.uci.edu/ml/machine-learning-databases/nursery/nursery.data"), header = FALSE)
names(nursery) <- c("parents", "has_nurs", "form", "children", "housing", "finance", "social", "health", "application")
#10-fold-cross-validation
#sprawdzian krzyzowy (10 krotna walidacja)
nursery_folds<-list()
nursery_results<-list()
nursery_model_time<-list()
nursery_predict_time<-list()
nursery_result_c50 <-list()
nursery_conf_c50<-list()
nursery_result_svm <-list()
nursery_conf_svm<-list()
nursery_result_bayes <-list()
nursery_conf_bayes<-list()
nursery_result_rpart <-list()
nursery_conf_rpart<-list()
nursery_result_antminer <-list()
nursery_conf_antminer<-list()
nursery_result_antminer2 <-list()
nursery_conf_antminer2<-list()
nursery_result_antminer3 <-list()
nursery_conf_antminer3<-list()
nursery_result_antminer4 <-list()
nursery_conf_antminer4<-list()
t<-1
loop_size<-10
for(t in 1:test) {
folds <- createFolds(nursery$application, k = loop_size, returnTrain = TRUE)
nursery_folds[[t]]<-folds
#data frame with all results
results <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
results[1, 1]<-c("C5.0")
results[2, 1]<-c("svm")
results[3, 1]<-c("naive bayes")
results[4, 1]<-c("cart")
results[5, 1]<-c("antminer")
results[6, 1]<-c("antminer2")
results[7, 1]<-c("antminer3")
results[8, 1]<-c("antminer4")
model_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
model_time[1, 1]<-c("C5.0")
model_time[2, 1]<-c("svm")
model_time[3, 1]<-c("naive bayes")
model_time[4, 1]<-c("cart")
model_time[5, 1]<-c("antminer")
model_time[6, 1]<-c("antminer2")
model_time[7, 1]<-c("antminer3")
model_time[8, 1]<-c("antminer4")
predict_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
predict_time[1, 1]<-c("C5.0")
predict_time[2, 1]<-c("svm")
predict_time[3, 1]<-c("naive bayes")
predict_time[4, 1]<-c("cart")
predict_time[5, 1]<-c("antminer")
predict_time[6, 1]<-c("antminer2")
predict_time[7, 1]<-c("antminer3")
predict_time[8, 1]<-c("antminer4")
loop_size<-1
for(i in 1:loop_size) {
trainset<-nursery[ folds[[i]] ,]
testset<-nursery[ -folds[[i]] ,]
starttime<-Sys.time()
model_c50 <- C5.0(application ~ ., data=trainset)
model_time[1, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_c50 <- predict(object=model_c50, newdata=testset, type="class")
predict_time[1, i+1]<-Sys.time()-starttime
tab_c50<-conf_matrix_table(result_c50, testset$application)
conf_c50 <- confusionMatrix(tab_c50)
results[1, i+1]<-c(conf_c50$overall['Accuracy'])
nursery_result_c50[t][[i]]<-result_c50
nursery_conf_c50[t][[i]]<-conf_c50
starttime<-Sys.time()
model_svm <- svm(application ~ ., data=trainset, type="C-classification")
model_time[2, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_svm <- predict(object=model_svm, newdata=testset)
predict_time[2, i+1]<-Sys.time()-starttime
tab_svm<-conf_matrix_table(result_svm, testset$application)
conf_svm <- confusionMatrix(tab_svm)
results[2, i+1]<-c(conf_svm$overall['Accuracy'])
nursery_result_svm[t][[i]]<-result_svm
nursery_conf_svm[t][[i]]<-conf_svm
starttime<-Sys.time()
model_bayes <- naiveBayes(x = subset(trainset, select=-application), y = trainset$application)
model_time[3, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_bayes <- predict(object = model_bayes, newdata = testset, type = "class")
predict_time[3, i+1]<-Sys.time()-starttime
tab_bayes<-conf_matrix_table(result_bayes, testset$application)
conf_bayes <- confusionMatrix(tab_bayes)
results[3, i+1]<-c(conf_bayes$overall['Accuracy'])
nursery_result_bayes[t][[i]]<-result_bayes
nursery_conf_bayes[t][[i]]<-conf_bayes
starttime<-Sys.time()
model_rpart <- rpart(application ~ ., data=trainset)
model_time[4, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_rpart <- predict(object=model_rpart, newdata=testset, type="class")
predict_time[4, i+1]<-Sys.time()-starttime
tab6<-conf_matrix_table(result_rpart, testset$application)
conf_rpart <- confusionMatrix(tab6)
results[4, i+1]<-c(conf_rpart$overall['Accuracy'])
nursery_result_rpart[t][[i]]<-result_rpart
nursery_conf_rpart[t][[i]]<-conf_rpart
starttime<-Sys.time()
model_antminer <- antminer(trainset, "application", 10, 1000, 10, 10)
model_time[5, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer <- predict.antminer(model_antminer, subset(testset, select=-application))
predict_time[5, i+1]<-Sys.time()-starttime
tab_antminer<-conf_matrix_table(result_antminer$class, testset$application)
conf_antminer <- confusionMatrix(tab_antminer)
results[5, i+1]<-c(conf_antminer$overall['Accuracy'])
nursery_result_antminer[t][[i]]<-result_antminer
nursery_conf_antminer[t][[i]]<-conf_antminer
# starttime<-Sys.time()
# model_antminer2 <- antminer2(trainset, "application", 10, 1000, 10, 10)
# model_time[6, i+1]<-Sys.time()-starttime
# starttime<-Sys.time()
# result_antminer2 <- predict.antminer2(model_antminer2, subset(testset, select=-application))
# predict_time[6, i+1]<-Sys.time()-starttime
# tab_antminer2<-conf_matrix_table(result_antminer2$class, testset$application)
# conf_antminer2 <- confusionMatrix(tab_antminer2)
# results[6, i+1]<-c(conf_antminer2$overall['Accuracy'])
# nursery_result_antminer2[t][[i]]<-result_antminer2
# nursery_conf_antminer2[t][[i]]<-conf_antminer2
#
# starttime<-Sys.time()
# model_antminer3 <- antminer3(trainset, "application", 10, 1000, 10, 10)
# model_time[7, i+1]<-Sys.time()-starttime
# starttime<-Sys.time()
# result_antminer3 <- predict.antminer3(model_antminer3, subset(testset, select=-application))
# predict_time[7, i+1]<-Sys.time()-starttime
# tab_antminer3<-conf_matrix_table(result_antminer3$class, testset$application)
# conf_antminer3 <- confusionMatrix(tab_antminer3)
# results[7, i+1]<-c(conf_antminer3$overall['Accuracy'])
# nursery_result_antminer3[t][[i]]<-result_antminer3
# nursery_conf_antminer3[t][[i]]<-conf_antminer3
#
# starttime<-Sys.time()
# model_antminer4 <- antminer4(trainset, "application", 10, 1000, 10, 10)
# model_time[8, i+1]<-Sys.time()-starttime
# starttime<-Sys.time()
# result_antminer4 <- predict.antminer4(model_antminer4, subset(testset, select=-application))
# predict_time[8, i+1]<-Sys.time()-starttime
# tab_antminer4<-conf_matrix_table(result_antminer4$class, testset$application)
# conf_antminer4 <- confusionMatrix(tab_antminer4)
# results[8, i+1]<-c(conf_antminer4$overall['Accuracy'])
# nursery_result_antminer4[t][[i]]<-result_antminer4
# nursery_conf_antminer4[t][[i]]<-conf_antminer4
}
nursery_results[[t]]<-results
nursery_model_time[[t]]<-model_time
nursery_predict_time[[t]]<-predict_time
}
mushroom <- read.csv(url("https://archive.ics.uci.edu/ml/machine-learning-databases/mushroom/agaricus-lepiota.data"), header = FALSE)
names(mushroom) <- c("edibility", "cap-shape", "cap_surface", "cap_color", "bruises?", "odor", "gill_attachment", "gill_spacing", "gill_size", "gill_color", "stalk_shape", "stalk_root", "stalk_surface_above_ring", "stalk_surface_below_ring", "stalk_color_above_ring", "stalk_color_below_ring", "veil_type", "veil_color", "ring_number", "ring_type", "spore_print_color", "population", "habitat")
mushroom <- mushroom[-17] #usuwanie atrybutu z jednym przypadkiem
#10-fold-cross-validation
#sprawdzian krzyzowy (10 krotna walidacja)
mushroom_folds<-list()
mushroom_results<-list()
mushroom_model_time<-list()
mushroom_predict_time<-list()
mushroom_result_c50 <-list()
mushroom_conf_c50<-list()
mushroom_result_svm <-list()
mushroom_conf_svm<-list()
mushroom_result_bayes <-list()
mushroom_conf_bayes<-list()
mushroom_result_rpart <-list()
mushroom_conf_rpart<-list()
mushroom_result_antminer <-list()
mushroom_conf_antminer<-list()
mushroom_result_antminer2 <-list()
mushroom_conf_antminer2<-list()
mushroom_result_antminer3 <-list()
mushroom_conf_antminer3<-list()
mushroom_result_antminer4 <-list()
mushroom_conf_antminer4<-list()
for(t in 1:test) {
folds <- createFolds(mushroom$edibility, k = loop_size, returnTrain = TRUE)
mushroom_folds[[t]]<-folds
#data frame with all results
results <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
results[1, 1]<-c("C5.0")
results[2, 1]<-c("svm")
results[3, 1]<-c("naive bayes")
results[4, 1]<-c("cart")
results[5, 1]<-c("antminer")
results[6, 1]<-c("antminer2")
results[7, 1]<-c("antminer3")
results[8, 1]<-c("antminer4")
model_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
model_time[1, 1]<-c("C5.0")
model_time[2, 1]<-c("svm")
model_time[3, 1]<-c("naive bayes")
model_time[4, 1]<-c("cart")
model_time[5, 1]<-c("antminer")
model_time[6, 1]<-c("antminer2")
model_time[7, 1]<-c("antminer3")
model_time[8, 1]<-c("antminer4")
predict_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
predict_time[1, 1]<-c("C5.0")
predict_time[2, 1]<-c("svm")
predict_time[3, 1]<-c("naive bayes")
predict_time[4, 1]<-c("cart")
predict_time[5, 1]<-c("antminer")
predict_time[6, 1]<-c("antminer2")
predict_time[7, 1]<-c("antminer3")
predict_time[8, 1]<-c("antminer4")
for(i in 1:loop_size) {
trainset<-mushroom[ folds[[i]] ,]
testset<-mushroom[ -folds[[i]] ,]
starttime<-Sys.time()
model_c50 <- C5.0(edibility ~ ., data=trainset)
model_time[1, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_c50 <- predict(object=model_c50, newdata=testset, type="class")
predict_time[1, i+1]<-Sys.time()-starttime
tab_c50<-conf_matrix_table(result_c50, testset$edibility)
conf_c50 <- confusionMatrix(tab_c50)
results[1, i+1]<-c(conf_c50$overall['Accuracy'])
mushroom_result_c50[t][[i]]<-result_c50
mushroom_conf_c50[t][[i]]<-conf_c50
starttime<-Sys.time()
model_svm <- svm(edibility ~ ., data=trainset, type="C-classification")
model_time[2, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_svm <- predict(object=model_svm, newdata=testset)
predict_time[2, i+1]<-Sys.time()-starttime
tab_svm<-conf_matrix_table(result_svm, testset$edibility)
conf_svm <- confusionMatrix(tab_svm)
results[2, i+1]<-c(conf_svm$overall['Accuracy'])
mushroom_result_svm[t][[i]]<-result_svm
mushroom_conf_svm[t][[i]]<-conf_svm
starttime<-Sys.time()
model_bayes <- naiveBayes(x = subset(trainset, select=-edibility), y = trainset$edibility)
model_time[3, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_bayes <- predict(object = model_bayes, newdata = testset, type = "class")
predict_time[3, i+1]<-Sys.time()-starttime
tab_bayes<-conf_matrix_table(result_bayes, testset$edibility)
conf_bayes <- confusionMatrix(tab_bayes)
results[3, i+1]<-c(conf_bayes$overall['Accuracy'])
mushroom_result_bayes[t][[i]]<-result_bayes
mushroom_conf_bayes[t][[i]]<-conf_bayes
starttime<-Sys.time()
model_rpart <- rpart(edibility ~ ., data=trainset)
model_time[4, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_rpart <- predict(object=model_rpart, newdata=testset, type="class")
predict_time[4, i+1]<-Sys.time()-starttime
tab6<-conf_matrix_table(result_rpart, testset$edibility)
conf_rpart <- confusionMatrix(tab6)
results[4, i+1]<-c(conf_rpart$overall['Accuracy'])
mushroom_result_rpart[t][[i]]<-result_rpart
mushroom_conf_rpart[t][[i]]<-conf_rpart
starttime<-Sys.time()
model_antminer <- antminer(trainset, "edibility", 10, 1000, 10, 10)
model_time[5, i+1]<-Sys.time()-starttime
Sys.time()-starttime
starttime<-Sys.time()
result_antminer <- predict.antminer(model_antminer, subset(testset, select=-edibility))
predict_time[5, i+1]<-Sys.time()-starttime
tab_antminer<-conf_matrix_table(result_antminer$class, testset$edibility)
conf_antminer <- confusionMatrix(tab_antminer)
results[5, i+1]<-c(conf_antminer$overall['Accuracy'])
mushroom_result_antminer[t][[i]]<-result_antminer
mushroom_conf_antminer[t][[i]]<-conf_antminer
starttime<-Sys.time()
model_antminer2 <- antminer2(trainset, "edibility", 10, 1000, 10, 10)
model_time[6, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer2 <- predict.antminer2(model_antminer2, subset(testset, select=-edibility))
predict_time[6, i+1]<-Sys.time()-starttime
tab_antminer2<-conf_matrix_table(result_antminer2$class, testset$edibility)
conf_antminer2 <- confusionMatrix(tab_antminer2)
results[6, i+1]<-c(conf_antminer2$overall['Accuracy'])
mushroom_result_antminer2[t][[i]]<-result_antminer2
mushroom_conf_antminer2[t][[i]]<-conf_antminer2
starttime<-Sys.time()
model_antminer3 <- antminer3(trainset, "edibility", 10, 1000, 10, 10)
model_time[7, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer3 <- predict.antminer3(model_antminer3, subset(testset, select=-edibility))
predict_time[7, i+1]<-Sys.time()-starttime
tab_antminer3<-conf_matrix_table(result_antminer3$class, testset$edibility)
conf_antminer3 <- confusionMatrix(tab_antminer3)
results[7, i+1]<-c(conf_antminer3$overall['Accuracy'])
mushroom_result_antminer3[t][[i]]<-result_antminer3
mushroom_conf_antminer3[t][[i]]<-conf_antminer3
starttime<-Sys.time()
model_antminer4 <- antminer4(trainset, "edibility", 10, 1000, 10, 10)
model_time[8, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer4 <- predict.antminer4(model_antminer4, subset(testset, select=-edibility))
predict_time[8, i+1]<-Sys.time()-starttime
tab_antminer4<-conf_matrix_table(result_antminer4$class, testset$edibility)
conf_antminer4 <- confusionMatrix(tab_antminer4)
results[8, i+1]<-c(conf_antminer4$overall['Accuracy'])
mushroom_result_antminer4[t][[i]]<-result_antminer4
mushroom_conf_antminer4[t][[i]]<-conf_antminer4
}
mushroom_results[[t]]<-results
mushroom_model_time[[t]]<-model_time
mushroom_predict_time[[t]]<-predict_time
}
tic_tac_toe <- read.csv(url("http://archive.ics.uci.edu/ml/machine-learning-databases/dermatology/dermatology.data"), header = FALSE)
names(tic_tac_toe) <- c("top-left-square", "top-middle-square", "top-right-square", "middle-left-square", "middle-middle-square", "middle-right-square", "bottom-left-square", "bottom-middle-square", "bottom-right-square", "Class")
# for(i in 1:10) {
# tic_tac_toe[, i]<-as.factor(tic_tac_toe[, i])
# }
#10-fold-cross-validation
#sprawdzian krzyzowy (10 krotna walidacja)
tic_tac_toe_folds<-list()
tic_tac_toe_results<-list()
tic_tac_toe_model_time<-list()
tic_tac_toe_predict_time<-list()
tic_tac_toe_result_c50 <-list()
tic_tac_toe_conf_c50<-list()
tic_tac_toe_result_svm <-list()
tic_tac_toe_conf_svm<-list()
tic_tac_toe_result_bayes <-list()
tic_tac_toe_conf_bayes<-list()
tic_tac_toe_result_rpart <-list()
tic_tac_toe_conf_rpart<-list()
tic_tac_toe_result_antminer <-list()
tic_tac_toe_conf_antminer<-list()
tic_tac_toe_result_antminer2 <-list()
tic_tac_toe_conf_antminer2<-list()
tic_tac_toe_result_antminer3 <-list()
tic_tac_toe_conf_antminer3<-list()
tic_tac_toe_result_antminer4 <-list()
tic_tac_toe_conf_antminer4<-list()
for(t in 1:test) {
folds <- createFolds(tic_tac_toe$Class, k = loop_size, returnTrain = TRUE)
tic_tac_toe_folds[[t]]<-folds
#data frame with all results
results <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
results[1, 1]<-c("C5.0")
results[2, 1]<-c("svm")
results[3, 1]<-c("naive bayes")
results[4, 1]<-c("cart")
results[5, 1]<-c("antminer")
results[6, 1]<-c("antminer2")
results[7, 1]<-c("antminer3")
results[8, 1]<-c("antminer4")
model_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
model_time[1, 1]<-c("C5.0")
model_time[2, 1]<-c("svm")
model_time[3, 1]<-c("naive bayes")
model_time[4, 1]<-c("cart")
model_time[5, 1]<-c("antminer")
model_time[6, 1]<-c("antminer2")
model_time[7, 1]<-c("antminer3")
model_time[8, 1]<-c("antminer4")
predict_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
predict_time[1, 1]<-c("C5.0")
predict_time[2, 1]<-c("svm")
predict_time[3, 1]<-c("naive bayes")
predict_time[4, 1]<-c("cart")
predict_time[5, 1]<-c("antminer")
predict_time[6, 1]<-c("antminer2")
predict_time[7, 1]<-c("antminer3")
predict_time[8, 1]<-c("antminer4")
for(i in 1:loop_size) {
trainset<-tic_tac_toe[ folds[[i]] ,]
testset<-tic_tac_toe[ -folds[[i]] ,]
starttime<-Sys.time()
model_c50 <- C5.0(Class ~ ., data=trainset)
model_time[1, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_c50 <- predict(object=model_c50, newdata=testset, type="class")
predict_time[1, i+1]<-Sys.time()-starttime
tab_c50<-conf_matrix_table(result_c50, testset$Class)
conf_c50 <- confusionMatrix(tab_c50)
results[1, i+1]<-c(conf_c50$overall['Accuracy'])
tic_tac_toe_result_c50[t][[i]]<-result_c50
tic_tac_toe_conf_c50[t][[i]]<-conf_c50
starttime<-Sys.time()
model_svm <- svm(Class ~ ., data=trainset, type="C-classification")
model_time[2, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_svm <- predict(object=model_svm, newdata=testset)
predict_time[2, i+1]<-Sys.time()-starttime
tab_svm<-conf_matrix_table(result_svm, testset$Class)
conf_svm <- confusionMatrix(tab_svm)
results[2, i+1]<-c(conf_svm$overall['Accuracy'])
tic_tac_toe_result_svm[t][[i]]<-result_svm
tic_tac_toe_conf_svm[t][[i]]<-conf_svm
starttime<-Sys.time()
model_bayes <- naiveBayes(x = subset(trainset, select=-Class), y = trainset$Class)
model_time[3, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_bayes <- predict(object = model_bayes, newdata = testset, type = "class")
predict_time[3, i+1]<-Sys.time()-starttime
tab_bayes<-conf_matrix_table(result_bayes, testset$Class)
conf_bayes <- confusionMatrix(tab_bayes)
results[3, i+1]<-c(conf_bayes$overall['Accuracy'])
tic_tac_toe_result_bayes[t][[i]]<-result_bayes
tic_tac_toe_conf_bayes[t][[i]]<-conf_bayes
starttime<-Sys.time()
model_rpart <- rpart(Class ~ ., data=trainset)
model_time[4, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_rpart <- predict(object=model_rpart, newdata=testset, type="class")
predict_time[4, i+1]<-Sys.time()-starttime
tab6<-conf_matrix_table(result_rpart, testset$Class)
conf_rpart <- confusionMatrix(tab6)
results[4, i+1]<-c(conf_rpart$overall['Accuracy'])
tic_tac_toe_result_rpart[t][[i]]<-result_rpart
tic_tac_toe_conf_rpart[t][[i]]<-conf_rpart
starttime<-Sys.time()
model_antminer <- antminer(trainset, "Class", 10, 1000, 10, 10)
model_time[5, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer <- predict.antminer(model_antminer, subset(testset, select=-Class))
predict_time[5, i+1]<-Sys.time()-starttime
tab_antminer<-conf_matrix_table(result_antminer$class, testset$Class)
conf_antminer <- confusionMatrix(tab_antminer)
results[5, i+1]<-c(conf_antminer$overall['Accuracy'])
tic_tac_toe_result_antminer[t][[i]]<-result_antminer
tic_tac_toe_conf_antminer[t][[i]]<-conf_antminer
starttime<-Sys.time()
model_antminer2 <- antminer2(trainset, "Class", 10, 1000, 10, 10)
model_time[6, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer2 <- predict.antminer2(model_antminer2, subset(testset, select=-Class))
predict_time[6, i+1]<-Sys.time()-starttime
tab_antminer2<-conf_matrix_table(result_antminer2$class, testset$Class)
conf_antminer2 <- confusionMatrix(tab_antminer2)
results[6, i+1]<-c(conf_antminer2$overall['Accuracy'])
tic_tac_toe_result_antminer2[t][[i]]<-result_antminer2
tic_tac_toe_conf_antminer2[t][[i]]<-conf_antminer2
starttime<-Sys.time()
model_antminer3 <- antminer3(trainset, "Class", 10, 1000, 10, 10)
model_time[7, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer3 <- predict.antminer3(model_antminer3, subset(testset, select=-Class))
predict_time[7, i+1]<-Sys.time()-starttime
tab_antminer3<-conf_matrix_table(result_antminer3$class, testset$Class)
conf_antminer3 <- confusionMatrix(tab_antminer3)
results[7, i+1]<-c(conf_antminer3$overall['Accuracy'])
tic_tac_toe_result_antminer3[t][[i]]<-result_antminer3
tic_tac_toe_conf_antminer3[t][[i]]<-conf_antminer3
starttime<-Sys.time()
model_antminer4 <- antminer4(trainset, "Class", 10, 1000, 10, 10)
model_time[8, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer4 <- predict.antminer4(model_antminer4, subset(testset, select=-Class))
predict_time[8, i+1]<-Sys.time()-starttime
tab_antminer4<-conf_matrix_table(result_antminer4$class, testset$Class)
conf_antminer4 <- confusionMatrix(tab_antminer4)
results[8, i+1]<-c(conf_antminer4$overall['Accuracy'])
tic_tac_toe_result_antminer4[t][[i]]<-result_antminer4
tic_tac_toe_conf_antminer4[t][[i]]<-conf_antminer4
}
tic_tac_toe_results[[t]]<-results
tic_tac_toe_model_time[[t]]<-model_time
tic_tac_toe_predict_time[[t]]<-predict_time
}
dermatology <- read.csv(url("http://archive.ics.uci.edu/ml/machine-learning-databases/dermatology/dermatology.data"), header = FALSE)
names(dermatology) <- c("erythema", "scaling", "definite borders", "itching", "koebner phenomenon", "polygonal papules", "follicular papules", "oral mucosal involvement", "knee and elbow involvement", "scalp involvement","family history","melanin incontinence","eosinophils in the infiltrate","PNL infiltrate","fibrosis of the papillary dermis","exocytosis","acanthosis","hyperkeratosis","parakeratosis","clubbing of the rete ridges","elongation of the rete ridges","thinning of the suprapapillary epidermis","spongiform pustule","munro microabcess","focal hypergranulosis","disappearance of the granular layer","vacuolisation and damage of basal layer","spongiosis","saw-tooth appearance of retes","follicular horn plug","perifollicular parakeratosis","inflammatory monoluclear inflitrate","band-like infiltrate","Age","diagnosis")
#usuniecie przypadkow w ktorych brakuje danych
remove_list<-which(dermatology[,34]=='?')
dermatology<-dermatology[-remove_list,]
# for(i in 1:10) {
# dermatology[, i]<-as.factor(dermatology[, i])
# }
dermatology$Age<-as.numeric(dermatology$Age)
dermatology$Age <- cut(dermatology$Age, breaks = seq(0, 80, by = 10))
for(i in 1:35) {
dermatology[, i]<-as.factor(dermatology[, i])
}
#10-fold-cross-validation
#sprawdzian krzyzowy (10 krotna walidacja)
dermatology_folds<-list()
dermatology_results<-list()
dermatology_model_time<-list()
dermatology_predict_time<-list()
dermatology_result_c50 <-list()
dermatology_conf_c50<-list()
dermatology_result_svm <-list()
dermatology_conf_svm<-list()
dermatology_result_bayes <-list()
dermatology_conf_bayes<-list()
dermatology_result_rpart <-list()
dermatology_conf_rpart<-list()
dermatology_result_antminer <-list()
dermatology_conf_antminer<-list()
dermatology_result_antminer2 <-list()
dermatology_conf_antminer2<-list()
dermatology_result_antminer3 <-list()
dermatology_conf_antminer3<-list()
dermatology_result_antminer4 <-list()
dermatology_conf_antminer4<-list()
for(t in 1:test) {
folds <- createFolds(dermatology$diagnosis, k = loop_size, returnTrain = TRUE)
dermatology_folds[[t]]<-folds
#data frame with all results
results <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
results[1, 1]<-c("C5.0")
results[2, 1]<-c("svm")
results[3, 1]<-c("naive bayes")
results[4, 1]<-c("cart")
results[5, 1]<-c("antminer")
results[6, 1]<-c("antminer2")
results[7, 1]<-c("antminer3")
results[8, 1]<-c("antminer4")
model_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
model_time[1, 1]<-c("C5.0")
model_time[2, 1]<-c("svm")
model_time[3, 1]<-c("naive bayes")
model_time[4, 1]<-c("cart")
model_time[5, 1]<-c("antminer")
model_time[6, 1]<-c("antminer2")
model_time[7, 1]<-c("antminer3")
model_time[8, 1]<-c("antminer4")
predict_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
predict_time[1, 1]<-c("C5.0")
predict_time[2, 1]<-c("svm")
predict_time[3, 1]<-c("naive bayes")
predict_time[4, 1]<-c("cart")
predict_time[5, 1]<-c("antminer")
predict_time[6, 1]<-c("antminer2")
predict_time[7, 1]<-c("antminer3")
predict_time[8, 1]<-c("antminer4")
for(i in 1:loop_size) {
trainset<-dermatology[ folds[[i]] ,]
testset<-dermatology[ -folds[[i]] ,]
starttime<-Sys.time()
model_c50 <- C5.0(diagnosis ~ ., data=trainset)
model_time[1, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_c50 <- predict(object=model_c50, newdata=testset, type="class")
predict_time[1, i+1]<-Sys.time()-starttime
tab_c50<-conf_matrix_table(result_c50, testset$diagnosis)
conf_c50 <- confusionMatrix(tab_c50)
results[1, i+1]<-c(conf_c50$overall['Accuracy'])
dermatology_result_c50[t][[i]]<-result_c50
dermatology_conf_c50[t][[i]]<-conf_c50
starttime<-Sys.time()
model_svm <- svm(diagnosis ~ ., data=trainset, type="C-classification")
model_time[2, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_svm <- predict(object=model_svm, newdata=testset)
predict_time[2, i+1]<-Sys.time()-starttime
tab_svm<-conf_matrix_table(result_svm, testset$diagnosis)
conf_svm <- confusionMatrix(tab_svm)
results[2, i+1]<-c(conf_svm$overall['Accuracy'])
dermatology_result_svm[t][[i]]<-result_svm
dermatology_conf_svm[t][[i]]<-conf_svm
starttime<-Sys.time()
model_bayes <- naiveBayes(x = subset(trainset, select=-diagnosis), y = trainset$diagnosis)
model_time[3, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_bayes <- predict(object = model_bayes, newdata = testset, type = "class")
predict_time[3, i+1]<-Sys.time()-starttime
tab_bayes<-conf_matrix_table(result_bayes, testset$diagnosis)
conf_bayes <- confusionMatrix(tab_bayes)
results[3, i+1]<-c(conf_bayes$overall['Accuracy'])
dermatology_result_bayes[t][[i]]<-result_bayes
dermatology_conf_bayes[t][[i]]<-conf_bayes
starttime<-Sys.time()
model_rpart <- rpart(diagnosis ~ ., data=trainset)
model_time[4, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_rpart <- predict(object=model_rpart, newdata=testset, type="class")
predict_time[4, i+1]<-Sys.time()-starttime
tab6<-conf_matrix_table(result_rpart, testset$diagnosis)
conf_rpart <- confusionMatrix(tab6)
results[4, i+1]<-c(conf_rpart$overall['Accuracy'])
dermatology_result_rpart[t][[i]]<-result_rpart
dermatology_conf_rpart[t][[i]]<-conf_rpart
starttime<-Sys.time()
model_antminer <- antminer(trainset, "diagnosis", 10, 1000, 10, 10)
model_time[5, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer <- predict.antminer(model_antminer, subset(testset, select=-diagnosis))
predict_time[5, i+1]<-Sys.time()-starttime
tab_antminer<-conf_matrix_table(result_antminer$class, testset$diagnosis)
conf_antminer <- confusionMatrix(tab_antminer)
results[5, i+1]<-c(conf_antminer$overall['Accuracy'])
dermatology_result_antminer[t][[i]]<-result_antminer
dermatology_conf_antminer[t][[i]]<-conf_antminer
starttime<-Sys.time()
model_antminer2 <- antminer2(trainset, "diagnosis", 10, 1000, 10, 10)
model_time[6, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer2 <- predict.antminer2(model_antminer2, subset(testset, select=-diagnosis))
predict_time[6, i+1]<-Sys.time()-starttime
tab_antminer2<-conf_matrix_table(result_antminer2$class, testset$diagnosis)
conf_antminer2 <- confusionMatrix(tab_antminer2)
results[6, i+1]<-c(conf_antminer2$overall['Accuracy'])
dermatology_result_antminer2[t][[i]]<-result_antminer2
dermatology_conf_antminer2[t][[i]]<-conf_antminer2
starttime<-Sys.time()
model_antminer3 <- antminer3(trainset, "diagnosis", 10, 1000, 10, 10)
model_time[7, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer3 <- predict.antminer3(model_antminer3, subset(testset, select=-diagnosis))
predict_time[7, i+1]<-Sys.time()-starttime
tab_antminer3<-conf_matrix_table(result_antminer3$class, testset$diagnosis)
conf_antminer3 <- confusionMatrix(tab_antminer3)
results[7, i+1]<-c(conf_antminer3$overall['Accuracy'])
dermatology_result_antminer3[t][[i]]<-result_antminer3
dermatology_conf_antminer3[t][[i]]<-conf_antminer3
starttime<-Sys.time()
model_antminer4 <- antminer4(trainset, "diagnosis", 10, 1000, 10, 10)
model_time[8, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer4 <- predict.antminer4(model_antminer4, subset(testset, select=-diagnosis))
predict_time[8, i+1]<-Sys.time()-starttime
tab_antminer4<-conf_matrix_table(result_antminer4$class, testset$diagnosis)
conf_antminer4 <- confusionMatrix(tab_antminer4)
results[8, i+1]<-c(conf_antminer4$overall['Accuracy'])
dermatology_result_antminer4[t][[i]]<-result_antminer4
dermatology_conf_antminer4[t][[i]]<-conf_antminer4
}
dermatology_results[[t]]<-results
dermatology_model_time[[t]]<-model_time
dermatology_predict_time[[t]]<-predict_time
}
heart_disease <- read.csv(url("http://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data"), header = FALSE)
names(heart_disease) <- c("age", "sex", "cp", "trestbps", "chol", "fbs", "restecg", "thalach", "exang","oldpeak","slope","ca","thal","num")
#usuniecie przypadkow w ktorych brakuje danych
remove_list<-which(heart_disease[,12]=='?')
heart_disease<-heart_disease[-remove_list,]
remove_list<-which(heart_disease[,13]=='?')
heart_disease<-heart_disease[-remove_list,]
# for(i in 1:10) {
# dermatology[, i]<-as.factor(dermatology[, i])
# }
heart_disease$age<-as.numeric(heart_disease$age)
heart_disease$age <- cut(heart_disease$age, breaks = seq(20, 80, by = 10))
heart_disease$trestbps <- cut(heart_disease$trestbps, breaks = seq(90, 200, by = 10))
heart_disease$chol<-as.numeric(heart_disease$chol)
heart_disease$chol <- cut(heart_disease$chol, breaks = seq(125, 575, by = 25))
heart_disease$thalach <- cut(heart_disease$thalach, breaks = seq(70, 210, by = 10))
heart_disease$oldpeak<-as.numeric(heart_disease$oldpeak)
heart_disease$oldpeak <- cut(heart_disease$oldpeak, breaks = seq(0, 6.5, by = 0.5), right=FALSE)
for(i in 1:14) {
heart_disease[, i]<-as.factor(heart_disease[, i])
}
#10-fold-cross-validation
#sprawdzian krzyzowy (10 krotna walidacja)
heart_disease_folds<-list()
heart_disease_results<-list()
heart_disease_model_time<-list()
heart_disease_predict_time<-list()
heart_disease_result_c50 <-list()
heart_disease_conf_c50<-list()
heart_disease_result_svm <-list()
heart_disease_conf_svm<-list()
heart_disease_result_bayes <-list()
heart_disease_conf_bayes<-list()
heart_disease_result_rpart <-list()
heart_disease_conf_rpart<-list()
heart_disease_result_antminer <-list()
heart_disease_conf_antminer<-list()
heart_disease_result_antminer2 <-list()
heart_disease_conf_antminer2<-list()
heart_disease_result_antminer3 <-list()
heart_disease_conf_antminer3<-list()
heart_disease_result_antminer4 <-list()
heart_disease_conf_antminer4<-list()
for(t in 1:test) {
folds <- createFolds(heart_disease$num, k = loop_size, returnTrain = TRUE)
heart_disease_folds[[t]]<-folds
#data frame with all results
results <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
results[1, 1]<-c("C5.0")
results[2, 1]<-c("svm")
results[3, 1]<-c("naive bayes")
results[4, 1]<-c("cart")
results[5, 1]<-c("antminer")
results[6, 1]<-c("antminer2")
results[7, 1]<-c("antminer3")
results[8, 1]<-c("antminer4")
model_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
model_time[1, 1]<-c("C5.0")
model_time[2, 1]<-c("svm")
model_time[3, 1]<-c("naive bayes")
model_time[4, 1]<-c("cart")
model_time[5, 1]<-c("antminer")
model_time[6, 1]<-c("antminer2")
model_time[7, 1]<-c("antminer3")
model_time[8, 1]<-c("antminer4")
predict_time <- as.data.frame(setNames(replicate(loop_size+1,numeric(0), simplify = F), c("algorithm", "accuracy")))
predict_time[1, 1]<-c("C5.0")
predict_time[2, 1]<-c("svm")
predict_time[3, 1]<-c("naive bayes")
predict_time[4, 1]<-c("cart")
predict_time[5, 1]<-c("antminer")
predict_time[6, 1]<-c("antminer2")
predict_time[7, 1]<-c("antminer3")
predict_time[8, 1]<-c("antminer4")
for(i in 1:loop_size) {
trainset<-heart_disease[ folds[[i]] ,]
testset<-heart_disease[ -folds[[i]] ,]
starttime<-Sys.time()
model_c50 <- C5.0(num ~ ., data=trainset)
model_time[1, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_c50 <- predict(object=model_c50, newdata=testset, type="class")
predict_time[1, i+1]<-Sys.time()-starttime
tab_c50<-conf_matrix_table(result_c50, testset$num)
conf_c50 <- confusionMatrix(tab_c50)
results[1, i+1]<-c(conf_c50$overall['Accuracy'])
heart_disease_result_c50[t][[i]]<-result_c50
heart_disease_conf_c50[t][[i]]<-conf_c50
starttime<-Sys.time()
model_svm <- svm(num ~ ., data=trainset, type="C-classification")
model_time[2, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_svm <- predict(object=model_svm, newdata=testset)
predict_time[2, i+1]<-Sys.time()-starttime
tab_svm<-conf_matrix_table(result_svm, testset$num)
conf_svm <- confusionMatrix(tab_svm)
results[2, i+1]<-c(conf_svm$overall['Accuracy'])
heart_disease_result_svm[t][[i]]<-result_svm
heart_disease_conf_svm[t][[i]]<-conf_svm
starttime<-Sys.time()
model_bayes <- naiveBayes(x = subset(trainset, select=-num), y = trainset$num)
model_time[3, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_bayes <- predict(object = model_bayes, newdata = testset, type = "class")
predict_time[3, i+1]<-Sys.time()-starttime
tab_bayes<-conf_matrix_table(result_bayes, testset$num)
conf_bayes <- confusionMatrix(tab_bayes)
results[3, i+1]<-c(conf_bayes$overall['Accuracy'])
heart_disease_result_bayes[t][[i]]<-result_bayes
heart_disease_conf_bayes[t][[i]]<-conf_bayes
starttime<-Sys.time()
model_rpart <- rpart(num ~ ., data=trainset)
model_time[4, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_rpart <- predict(object=model_rpart, newdata=testset, type="class")
predict_time[4, i+1]<-Sys.time()-starttime
tab6<-conf_matrix_table(result_rpart, testset$num)
conf_rpart <- confusionMatrix(tab6)
results[4, i+1]<-c(conf_rpart$overall['Accuracy'])
heart_disease_result_rpart[t][[i]]<-result_rpart
heart_disease_conf_rpart[t][[i]]<-conf_rpart
starttime<-Sys.time()
model_antminer <- antminer(trainset, "num", 10, 1000, 10, 10)
model_time[5, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer <- predict.antminer(model_antminer, subset(testset, select=-num))
predict_time[5, i+1]<-Sys.time()-starttime
tab_antminer<-conf_matrix_table(result_antminer$class, testset$num)
conf_antminer <- confusionMatrix(tab_antminer)
results[5, i+1]<-c(conf_antminer$overall['Accuracy'])
heart_disease_result_antminer[t][[i]]<-result_antminer
heart_disease_conf_antminer[t][[i]]<-conf_antminer
starttime<-Sys.time()
model_antminer2 <- antminer2(trainset, "num", 10, 1000, 10, 10)
model_time[6, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer2 <- predict.antminer2(model_antminer2, subset(testset, select=-num))
predict_time[6, i+1]<-Sys.time()-starttime
tab_antminer2<-conf_matrix_table(result_antminer2$class, testset$num)
conf_antminer2 <- confusionMatrix(tab_antminer2)
results[6, i+1]<-c(conf_antminer2$overall['Accuracy'])
heart_disease_result_antminer2[t][[i]]<-result_antminer2
heart_disease_conf_antminer2[t][[i]]<-conf_antminer2
starttime<-Sys.time()
model_antminer3 <- antminer3(trainset, "num", 10, 1000, 10, 10)
model_time[7, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer3 <- predict.antminer3(model_antminer3, subset(testset, select=-num))
predict_time[7, i+1]<-Sys.time()-starttime
tab_antminer3<-conf_matrix_table(result_antminer3$class, testset$num)
conf_antminer3 <- confusionMatrix(tab_antminer3)
results[7, i+1]<-c(conf_antminer3$overall['Accuracy'])
heart_disease_result_antminer3[t][[i]]<-result_antminer3
heart_disease_conf_antminer3[t][[i]]<-conf_antminer3
starttime<-Sys.time()
model_antminer4 <- antminer4(trainset, "num", 10, 1000, 10, 10)
model_time[8, i+1]<-Sys.time()-starttime
starttime<-Sys.time()
result_antminer4 <- predict.antminer4(model_antminer4, subset(testset, select=-num))
predict_time[8, i+1]<-Sys.time()-starttime
tab_antminer4<-conf_matrix_table(result_antminer4$class, testset$num)
conf_antminer4 <- confusionMatrix(tab_antminer4)
results[8, i+1]<-c(conf_antminer4$overall['Accuracy'])
heart_disease_result_antminer4[t][[i]]<-result_antminer4
heart_disease_conf_antminer4[t][[i]]<-conf_antminer4
}
heart_disease_results[[t]]<-results
heart_disease_model_time[[t]]<-model_time
heart_disease_predict_time[[t]]<-predict_time
}
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