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svm_hands_on_breast_cancer.R
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svm_hands_on_breast_cancer.R
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source("josephs_functions.R")
check_install_package("mlbench") #BReast|Cncer data
check_install_package("e1071") #ssvm
check_install_package("caret") #confusionmatrix function
# *********************************************************************
# ************************** understand data **************************
# *********************************************************************
data(BreastCancer)
head(BreastCancer, 100)
summary(BreastCancer)
levels(BreastCancer$Class)
str(BreastCancer)
# *********************************************************************
# *********** Noticed that Bare.nuclei had NA values. removing them from set
# *********************************************************************
BreastCancerClean <- na.omit(BreastCancer)
summary(BreastCancerClean)
# *********************************************************************
# ****** make chars categorical values, numeric ***********************
# *********************************************************************
BreastCancerClean[,"Cl.thickness"] <- as.numeric(as.character(BreastCancerClean[,"Cl.thickness"]))
BreastCancerClean[,"Cell.size"] <- as.numeric(as.character(BreastCancerClean[,"Cell.size"]))
BreastCancerClean[,"Cell.shape"] <- as.numeric(as.character(BreastCancerClean[,"Cell.shape"]))
BreastCancerClean[,"Marg.adhesion"] <- as.numeric(as.character(BreastCancerClean[,"Marg.adhesion"]))
BreastCancerClean[,"Epith.c.size"] <- as.numeric(as.character(BreastCancerClean[,"Epith.c.size"]))
BreastCancerClean[,"Bare.nuclei"] <- as.numeric(as.character(BreastCancerClean[,"Bare.nuclei"]))
BreastCancerClean[,"Bl.cromatin"] <- as.numeric(as.character(BreastCancerClean[,"Bl.cromatin"]))
BreastCancerClean[,"Normal.nucleoli"] <- as.numeric(as.character(BreastCancerClean[,"Normal.nucleoli"]))
BreastCancerClean[,"Mitoses"] <- as.numeric(as.character(BreastCancerClean[,"Mitoses"]))
#"Cl.thickness" "Cell.size" "Cell.shape" "Marg.adhesion" [6] "Epith.c.size"
#"Bare.nuclei" "Bl.cromatin" "Normal.nucleoli" "Mitoses" [11] "Class" names(BreastCancerClean)
plot(BreastCancerClean$Cl.thickness, BreastCancerClean$Cell.size, pch=21, bg=c("blue", "red")[unclass(BreastCancerClean$Class)])
plot(BreastCancerClean$Cell.size, BreastCancerClean$Cell.shape, pch=21, bg=c("blue", "red")[unclass(BreastCancerClean$Class)])
plot(BreastCancerClean$Cell.size, BreastCancerClean$Epith.c.size, pch=21, bg=c("blue", "red")[unclass(BreastCancerClean$Class)])
plot(BreastCancerClean$Bare.nuclei, BreastCancerClean$Bl.cromatin, pch=21, bg=c("blue", "red")[unclass(BreastCancerClean$Class)])
plot(BreastCancerClean$Bare.nuclei, BreastCancerClean$Normal.nucleoli, pch=21, bg=c("blue", "red")[unclass(BreastCancerClean$Class)])
plot(BreastCancerClean$Mitoses, BreastCancerClean$Marg.adhesion, pch=21, bg=c("blue", "red")[unclass(BreastCancerClean$Class)])
plot(BreastCancerClean$Marg.adhesion, BreastCancerClean$Epith.c.size, pch=21, bg=c("blue", "red")[unclass(BreastCancerClean$Class)])
# *********************************************************************
# Plot histograms of single variables
# *********************************************************************
par(mfrow=c(3,3))
for(i in 2:10) {
#barplot(table(BreastCancerClean$Class, BreastCancerClean[,i]), main=names(BreastCancer)[i], legend.text=unique(BreastCancerClean$Class))
barplot(table(BreastCancerClean$Class, BreastCancerClean[,i]), main=names(BreastCancer)[i])
}
par(mfrow=c(3,3))
for(i in 2:10) {
hist(BreastCancerClean[,i], main=names(BreastCancer)[i])
}
# *********************************************************************
#hyper params
# *********************************************************************
train_sample_percentage <- 0.75
sampling_random_seed <- 342
# *********************************************************************
# **** Cost matrix assumptions
# **** predict benign cancer on actual malignant tumor = 100
# **** predict malign cancer on actual benign tumor = 2
# *********************************************************************
cost_matrix <- matrix(c(0,100,2, 0), nrow = 2, byrow = TRUE)
cost_matrix
# *********************************************************************
#split data set in train and test data
# *********************************************************************
splitted_data <- split_train_test_data(BreastCancerClean, train_sample_percentage, sampling_random_seed)
train_data <- splitted_data$train_data # train data
test_data <- splitted_data$test_data # test data
# *********************************************************************
# create a data frame to see several models using candidate variable combinations
# *********************************************************************
result_models <- data.frame(matrix(nrow = 0, ncol = 8))
result_models <- setNames(result_models, c("formula_name","kernel_function","polynomial_degree", "gamma", "coef0", "cost_parameter", "accuracy", "prediction_cost"))
names(result_models)
# *********************************************************************
# Class ~ Cell.size + Cl.thickness - linear C = 10
# *********************************************************************
formula <- Class ~ Cell.size + Cl.thickness
kernel_function <- "linear"
C <- 10
svm_model <- svm(formula, kernel = kernel_function, data=train_data, C = C)
prediction <- predict(svm_model, test_data)
confusion_matrix <- table(prediction, test_data$Class)
accuracy <- sum(diag(confusion_matrix)) / sum(confusion_matrix)
prediction_cost <- sum(confusion_matrix * cost_matrix)
result_models[nrow(result_models) + 1, ] =
c(
paste(deparse(formula), collapse = ""),
kernel_function,
"N/A",
"N/A",
"N/A",
C,
accuracy,
prediction_cost)
# *********************************************************************
# Class ~ Cell.size + Cl.thickness - linear C = 100
# *********************************************************************
formula <- Class ~ Cell.size + Cl.thickness
kernel_function <- "linear"
C <- 100
svm_model <- svm(formula, kernel = kernel_function, data=train_data, C = C)
prediction <- predict(svm_model, test_data)
confusion_matrix <- table(prediction, test_data$Class)
accuracy <- sum(diag(confusion_matrix)) / sum(confusion_matrix)
prediction_cost <- sum(confusion_matrix * cost_matrix)
result_models[nrow(result_models) + 1, ] =
c(
paste(deparse(formula), collapse = ""),
kernel_function,
"N/A",
"N/A",
"N/A",
C,
accuracy,
prediction_cost)
# *********************************************************************
# LINEAR C = 10
# Class ~
# Cl.thickness +
# Cell.size +
# Cell.shape +
# Marg.adhesion +
# Epith.c.size +
# Bare.nuclei +
# Bl.cromatin +
# Normal.nucleoli +
# Mitoses
# *********************************************************************
formula <- Class ~
Cl.thickness +
Cell.size +
Cell.shape +
Marg.adhesion +
Epith.c.size +
Bare.nuclei +
Bl.cromatin +
Normal.nucleoli +
Mitoses
kernel_function <- "linear"
C <- 10
svm_model <- svm(formula, kernel=kernel_function, data=train_data, C = C)
prediction <- predict(svm_model, test_data)
confusion_matrix <- table(prediction, test_data$Class)
accuracy <- sum(diag(confusion_matrix)) / sum(confusion_matrix)
prediction_cost <- sum(confusion_matrix * cost_matrix)
result_models[nrow(result_models) + 1, ] =
c(
paste(deparse(formula), collapse = ""),
kernel_function,
"N/A",
"N/A",
"N/A",
C,
accuracy,
prediction_cost)
# *********************************************************************
# LINEAR C = 100
# Class ~
# Cl.thickness +
# Cell.size +
# Cell.shape +
# Marg.adhesion +
# Epith.c.size +
# Bare.nuclei +
# Bl.cromatin +
# Normal.nucleoli +
# Mitoses
# *********************************************************************
formula <- Class ~
Cl.thickness +
Cell.size +
Cell.shape +
Marg.adhesion +
Epith.c.size +
Bare.nuclei +
Bl.cromatin +
Normal.nucleoli +
Mitoses
kernel_function <- "linear"
C <- 100
svm_model <- svm(formula, kernel=kernel_function, data=train_data, C = C)
prediction <- predict(svm_model, test_data)
confusion_matrix <- table(prediction, test_data$Class)
accuracy <- sum(diag(confusion_matrix)) / sum(confusion_matrix)
prediction_cost <- sum(confusion_matrix * cost_matrix)
result_models[nrow(result_models) + 1, ] =
c(
paste(deparse(formula), collapse = ""),
kernel_function,
"N/A",
"N/A",
"N/A",
C,
accuracy,
prediction_cost)
# *********************************************************************
# POLYNOMIAL degree=3 gamma=2.5 C = 10
# Class ~
# Cl.thickness +
# Cell.size +
# Cell.shape +
# Marg.adhesion +
# Epith.c.size +
# Bare.nuclei +
# Bl.cromatin +
# Normal.nucleoli +
# Mitoses
# *********************************************************************
formula <- Class ~
Cl.thickness +
Cell.size +
Cell.shape +
Marg.adhesion +
Epith.c.size +
Bare.nuclei +
Bl.cromatin +
Normal.nucleoli +
Mitoses
kernel_function <- "polynomial"
degree <- 3
gamma <- 2.5
C <- 10
svm_model <- svm(formula, kernel=kernel_function, data=train_data,
degree = degree,
gamma = gamma,
C = C)
prediction <- predict(svm_model, test_data)
confusion_matrix <- table(prediction, test_data$Class)
accuracy <- sum(diag(confusion_matrix)) / sum(confusion_matrix)
prediction_cost <- sum(confusion_matrix * cost_matrix)
result_models[nrow(result_models) + 1, ] =
c(
paste(deparse(formula), collapse = ""),
kernel_function,
degree,
gamma,
"N/A",
C,
accuracy,
prediction_cost)
# *********************************************************************
# POLYNOMIAL degree=4 gamma=5 C = 1000
# Class ~
# Cl.thickness +
# Cell.size +
# Cell.shape +
# Marg.adhesion +
# Epith.c.size +
# Bare.nuclei +
# Bl.cromatin +
# Normal.nucleoli +
# Mitoses
# *********************************************************************
formula <- Class ~
Cl.thickness +
Cell.size +
Cell.shape +
Marg.adhesion +
Epith.c.size +
Bare.nuclei +
Bl.cromatin +
Normal.nucleoli +
Mitoses
kernel_function <- "polynomial"
degree <- 4
gamma <- 5
C <- 1000
svm_model <- svm(formula, kernel=kernel_function, data=train_data,
degree = degree,
gamma = gamma,
C = C)
prediction <- predict(svm_model, test_data)
confusion_matrix <- table(prediction, test_data$Class)
accuracy <- sum(diag(confusion_matrix)) / sum(confusion_matrix)
prediction_cost <- sum(confusion_matrix * cost_matrix)
result_models[nrow(result_models) + 1, ] =
c(
paste(deparse(formula), collapse = ""),
kernel_function,
degree,
gamma,
"N/A",
C,
accuracy,
prediction_cost)
# *********************************************************************
# RADIAL gamma=2 C = 10
# Class ~
# Cl.thickness +
# Cell.size +
# Cell.shape +
# Marg.adhesion +
# Epith.c.size +
# Bare.nuclei +
# Bl.cromatin +
# Normal.nucleoli +
# Mitoses
# *********************************************************************
formula <- Class ~
Cl.thickness +
Cell.size +
Cell.shape +
Marg.adhesion +
Epith.c.size +
Bare.nuclei +
Bl.cromatin +
Normal.nucleoli +
Mitoses
kernel_function <- "radial"
gamma <- 2
C <- 10
svm_model <- svm(formula, kernel=kernel_function, data=train_data,
gamma = gamma,
C = C)
prediction <- predict(svm_model, test_data)
confusion_matrix <- table(prediction, test_data$Class)
accuracy <- sum(diag(confusion_matrix)) / sum(confusion_matrix)
prediction_cost <- sum(confusion_matrix * cost_matrix)
result_models[nrow(result_models) + 1, ] =
c(
paste(deparse(formula), collapse = ""),
kernel_function,
"N/A",
gamma,
"N/A",
C,
accuracy,
prediction_cost)
result_models
write.csv(result_models, "result_models.csv")