SVM CLASSIFICATION

Author: RegaipKURT

SVM.R

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+#install.packages("ISLR")
+library(ISLR)
+#install.packages("tidyverse")
+library(tidyverse)
+#install.packages("funModeling")
+library(funModeling)
+#install.packages("caret")
+library(caret)
+#install.packages("pROC")
+library(pROC)
+#install.packages("class")
+library(class)#knn icin
+#install.packages("e1071")
+library(e1071)#knn icin
+#install.packages("kernlab")
+library(kernlab) #svm icin
+#install.packages("ROCR")
+library(ROCR) #roc icin
+#install.packages("neuralnet")
+library(neuralnet)
+#install.packages("GGally")
+library(GGally)
+#install.packages("nnet")
+library(nnet)
+#install.packages("rpart")
+library(rpart)
+#install.packages("cli")
+library(cli)
+#install.packages("tree")
+library(tree)
+#install.packages("rpart.plot")
+library(rpart.plot)
+#install.packages("randomForest")
+library(randomForest)
+#install.packages("gbm")
+library(gbm)
+#install.packages("xgboost")
+library(xgboost)
+#install.packages("DiagrammeR")
+library(DiagrammeR)
+#install.packages("mlbench")
+library(mlbench)
+
+
+set.seed(10111)
+x <- matrix(rnorm(40), 20, 2)
+y <- rep(c(-1,1), c(10,10))
+head(x)
+head(y)
+x[y==1,] <- x[y==1,] + 1 
+head(x)
+plot(x, col=y+3,pch=19)
+
+df <- data.frame(x, y=as.factor(y))
+
+#svm model
+
+svm_model1 <- svm(y~., data = df, kernel="polynomial", cost=10, scale = F) 
+summary(svm_model1)
+
+plot(svm_model1, df)
+
+#SVM MODELİNİN NASIL OLUŞTUĞUNU ANLAMAK
+##===========================================================
+#BURADA DF'NİN MİNİMUM VE MAKSİMUM DEĞERLERİ ARASINDA DEĞERLER OLUŞTURUP
+# SVM YAPISNININ NASIL ORTAYA ÇIKTIĞINI ANLAMAYA ÇALIŞACAĞIZ.
+
+a <- seq(from=apply(df,2, range)[1,1], 
+ to = apply(df, 2, range)[2,1],
+ length = 5)
+b <- seq(from=apply(df,2, range)[1,2], 
+ to = apply(df, 2, range)[2,2],
+ length = 5)
+expand.grid(a,b)
+
+make_grid <- function(x, n = 75) {
+ g_range = apply(x, 2, range)
+ x1 = seq(from = g_range[1,1], to = g_range[2,1], length = n)
+ x2 = seq(from = g_range[1,2], to = g_range[2,2], length = n)
+ expand.grid(X1 = x1, X2 = x2)
+}
+
+x_grid <- make_grid(x)
+x_grid[1:10,]
+
+y_grid <- predict(svm_model1, x_grid)
+
+plot(x_grid, col=c("red", "blue")[as.numeric(y_grid)],
+ pch=19, cex=0.2)
+
+points(x, col=y+3, pch=19)
+points(x[svm_model1$index, ], pch="I", cex=2)
+
+beta <- drop(t(svm_model1$coefs)%*%x[svm_model1$index,])
+b0 <- svm_model1$rho
+
+curve(b0 / beta[2], -beta[1] / beta[2])
+curve((b0 - 1) / beta[2], -beta[1] / beta[2], lty = 2)
+curve((b0 + 1) / beta[2], -beta[1] / beta[2], lty = 2)
+#===========================================================#
+
+
+#svm tahmin bölümü
+
+pred <- predict(svm_model1)
+class_error(df$y, svm_model1$fitted)
+
+df$y <- ifelse(df$y==-1, 0,1)
+svm_model1$fitted <- ifelse(svm_model1$fitted==-1, 0, 1)
+
+tb <- table(svm_model1$fitted, df$y)
+confusionMatrix(tb, positive = "1")
+svm_model1$fitted
+
+# SVM DOĞRUSAL OLMAYAN MODELLER
+
+#İLK MODEL 
+#======================================================
+#load ile yüklenen data R içinden ismiyle çağrılabilir.
+load(file = "ESL.mixture.rda")
+df <- ESL.mixture
+attach(df)
+remove(x, y)
+plot(x, col=y+1)
+df <- data.frame(x=x, y=as.factor(y))
+
+n_svm_fit <- svm(factor(y)~., data = df, scale=F, kernel="radial", cost=5)
+svm_fit
+
+x_grid <- expand.grid(X1 = px1, X2 = px2)
+y_grid <- predict(n_svm_fit, x_grid)
+
+
+plot(x_grid, 
+ col = as.numeric(y_grid), 
+ pch = 20, 
+ cex = .2) 
+
+points(x, col = y + 1, pch = 19)
+
+dv <- predict(n_svm_fit, x_grid, decision.values = TRUE)
+
+contour(px1, px2, 
+ matrix(attributes(dv)$decision, length(px1), length(px2)), 
+ level = 0, 
+ add = TRUE)
+
+
+contour(px1, px2, 
+ matrix(attributes(dv)$decision, length(px1), length(px2)), 
+ level = 0.5, 
+ add = TRUE, 
+ col = "blue", 
+ lwd = 2)
+
+
+# İKİNCİ MODEL VE MODEL TUNİNG
+#=========================================================
+data("segmentationData")
+df <- segmentationData
+as.tibble(df)
+glimpse(df)
+# biz bu veriseti içinde class değişkenini bağımlı değişken olarak alacağız.
+table(df$Class)
+
+svm_train <- df %>% filter(Case=="Train") %>% select(-Case)
+svm_test <- df %>% filter(Case=="Test") %>% select(-Case)
+
+svm_train_x <- svm_train %>% select(-Class)
+svm_test_x <- svm_test %>% select(-Class)
+
+svm_test_y <- svm_test$Class
+svm_train_y <- svm_train$Class
+
+#model tune işlemleri için ayarlamalar.
+
+ctrl <- trainControl(method = "cv", summaryFunction = twoClassSummary,
+ classProbs = TRUE)
+
+svm_grid <- expand.grid(sigma=0.08,
+ C = seq(0.001, 0.2, length=10))
+
+svm_tune <- train(svm_train_x, svm_train_y, method="svmRadial",
+ trControl = ctrl,
+ tuneGrid=svm_grid, metric="ROC") 
+svm_tune
+plot(svm_tune)
+confusionMatrix(predict(svm_tune, test_x), svm_test_y, positive = "WS")
+
+y_pred <- predict(svm_tune, svm_test_x, type="raw")
+
+test_prob <- predict(svm_tune, svm_test_x, type = "prob")
+test_prob$WS
+roc(svm_test_y ~ test_prob$WS, plot = TRUE, print.auc = TRUE)
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