added lab 5
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@@ -1,22 +1,28 @@
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install.packages(c("e1071","caret","randomForest","ggplot2","pROC"), dependencies = TRUE)
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install.packages(
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c("e1071", "caret", "randomForest", "ggplot2", "pROC"),
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repos = c("https://cloud.r-project.org/"),
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dependencies = TRUE
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)
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suppressPackageStartupMessages({
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library(e1071) # for svm/tune.svm
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library(caret) # for metrics
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library(randomForest) # alternative classifier
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library(ggplot2)
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library(e1071) # for svm/tune.svm
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library(caret) # for metrics
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library(randomForest) # alternative classifier
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library(ggplot2)
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})
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set.seed(42)
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read_wine <- function() {
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df <- read.csv("wine.data", header = FALSE)
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colnames(df) <- c("Class",
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"Alcohol","Malic.acid","Ash","Alcalinity.of.ash","Magnesium",
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"Total.phenols","Flavanoids","Nonflavanoid.phenols","Proanthocyanins",
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"Color.intensity","Hue","OD280.OD315","Proline")
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df$Class <- factor(df$Class)
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df
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df <- read.csv("wine.data", header = FALSE)
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colnames(df) <- c(
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"Class",
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"Alcohol", "Malic.acid", "Ash", "Alcalinity.of.ash", "Magnesium",
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"Total.phenols", "Flavanoids", "Nonflavanoid.phenols", "Proanthocyanins",
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"Color.intensity", "Hue", "OD280.OD315", "Proline"
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)
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df$Class <- factor(df$Class)
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df
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}
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df <- read_wine()
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@@ -24,56 +30,62 @@ df <- read_wine()
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# split into train/test
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idx <- createDataPartition(df$Class, p = 0.8, list = FALSE)
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train <- df[idx, ]
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test <- df[-idx, ]
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test <- df[-idx, ]
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# choose a subset of features based on ANOVA F-test
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# I picked this sbuset before the runs:
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# alcohol, flavanoids, color intensity, od280/od315, proline, total phenols
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features <- c("Alcohol","Flavanoids","Color.intensity","OD280.OD315","Proline","Total.phenols")
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features <- c("Alcohol", "Flavanoids", "Color.intensity", "OD280.OD315", "Proline", "Total.phenols")
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x_train <- train[, features]
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y_train <- train$Class
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x_test <- test[, features]
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y_test <- test$Class
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x_test <- test[, features]
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y_test <- test$Class
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# scale features
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pp <- preProcess(x_train, method = c("center","scale"))
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pp <- preProcess(x_train, method = c("center", "scale"))
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x_train_s <- predict(pp, x_train)
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x_test_s <- predict(pp, x_test)
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x_test_s <- predict(pp, x_test)
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# 1) linear kernel svm with hyperparameter tuning (C)
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# linear kernel svm with hyperparameter tuning (C)
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set.seed(42)
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lin_grid <- data.frame(cost = c(0.1, 1, 10, 100))
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tune_lin <- tune.svm(x = x_train_s, y = y_train,
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kernel = "linear",
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cost = lin_grid$cost,
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tunecontrol = tune.control(cross = 5))
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tune_lin <- tune.svm(
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x = x_train_s, y = y_train,
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kernel = "linear",
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cost = lin_grid$cost,
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tunecontrol = tune.control(cross = 5)
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)
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lin_best <- tune_lin$best.model
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# 2) rbf kernel svm with tuning (C, gamma)
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# rbf kernel svm with tuning (C, gamma)
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set.seed(42)
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rbf_grid_cost <- c(0.1, 1, 10, 100, 1000)
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rbf_grid_gamma <- c(0.001, 0.01, 0.1, 1)
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tune_rbf <- tune.svm(x = x_train_s, y = y_train,
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kernel = "radial",
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cost = rbf_grid_cost,
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gamma = rbf_grid_gamma,
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tunecontrol = tune.control(cross = 5))
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tune_rbf <- tune.svm(
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x = x_train_s, y = y_train,
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kernel = "radial",
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cost = rbf_grid_cost,
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gamma = rbf_grid_gamma,
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tunecontrol = tune.control(cross = 5)
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)
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rbf_best <- tune_rbf$best.model
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# 3) alternative classifier: random forest (same features)
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# alt classifier: random forest (same features)
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set.seed(42)
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rf_fit <- randomForest(x = x_train, y = y_train, ntree = 500, mtry = 2, importance = TRUE)
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# evaluation helper
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eval_model <- function(model, x_test_s, y_test, name) {
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pred <- predict(model, x_test_s)
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cm <- confusionMatrix(pred, y_test)
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pr <- data.frame(model = name,
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accuracy = cm$overall["Accuracy"],
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precision_macro = mean(cm$byClass[,"Precision"], na.rm=TRUE),
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recall_macro = mean(cm$byClass[,"Recall"], na.rm=TRUE),
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f1_macro = mean(cm$byClass[,"F1"], na.rm=TRUE))
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list(cm = cm, pr = pr)
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pred <- predict(model, x_test_s)
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cm <- confusionMatrix(pred, y_test)
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pr <- data.frame(
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model = name,
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accuracy = cm$overall["Accuracy"],
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precision_macro = mean(cm$byClass[, "Precision"], na.rm = TRUE),
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recall_macro = mean(cm$byClass[, "Recall"], na.rm = TRUE),
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f1_macro = mean(cm$byClass[, "F1"], na.rm = TRUE)
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)
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list(cm = cm, pr = pr)
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}
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# eval svm models (use scaled features)
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@@ -84,11 +96,13 @@ rbf_eval <- eval_model(rbf_best, x_test_s, y_test, "svm_rbf")
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rf_pred <- predict(rf_fit, x_test)
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rf_cm <- confusionMatrix(rf_pred, y_test)
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rf_pr <- data.frame(model = "random_forest",
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accuracy = rf_cm$overall["Accuracy"],
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precision_macro = mean(rf_cm$byClass[,"Precision"], na.rm=TRUE),
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recall_macro = mean(rf_cm$byClass[,"Recall"], na.rm=TRUE),
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f1_macro = mean(rf_cm$byClass[,"F1"], na.rm=TRUE))
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rf_pr <- data.frame(
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model = "random_forest",
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accuracy = rf_cm$overall["Accuracy"],
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precision_macro = mean(rf_cm$byClass[, "Precision"], na.rm = TRUE),
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recall_macro = mean(rf_cm$byClass[, "Recall"], na.rm = TRUE),
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f1_macro = mean(rf_cm$byClass[, "F1"], na.rm = TRUE)
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)
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perf <- rbind(lin_eval$pr, rbf_eval$pr, rf_pr)
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@@ -98,14 +112,17 @@ cat("best params (rbf svm): C =", rbf_best$cost, " gamma =", rbf_best$gamma, "\n
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print(perf)
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# macro-f1 comparison
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ggplot(perf, aes(x = model, y = f1_macro)) +
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geom_col() +
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labs(title = "macro-F1 by model (wine test set)")
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ggplot(perf, aes(x = model, y = f1_macro)) +
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geom_col() +
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labs(title = "macro-F1 by model (wine test set)")
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# save outputs
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write.table(perf, file = "lab5_performance_table.txt", sep = "\t", row.names = FALSE, quote = FALSE)
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sink("lab5_confusion_matrices.txt")
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cat("=== svm linear ===\n"); print(lin_eval$cm)
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cat("\n=== svm rbf ===\n"); print(rbf_eval$cm)
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cat("\n=== random forest ===\n"); print(rf_cm)
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sink()
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cat("=== svm linear ===\n")
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print(lin_eval$cm)
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cat("\n=== svm rbf ===\n")
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print(rbf_eval$cm)
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cat("\n=== random forest ===\n")
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print(rf_cm)
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sink()
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@@ -0,0 +1,95 @@
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=== svm linear ===
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Confusion Matrix and Statistics
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Reference
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Prediction 1 2 3
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1 11 1 0
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2 0 13 0
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3 0 0 9
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Overall Statistics
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Accuracy : 0.9706
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95% CI : (0.8467, 0.9993)
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No Information Rate : 0.4118
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P-Value [Acc > NIR] : 3.92e-12
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Kappa : 0.9553
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Mcnemar's Test P-Value : NA
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Statistics by Class:
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Class: 1 Class: 2 Class: 3
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Sensitivity 1.0000 0.9286 1.0000
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Specificity 0.9565 1.0000 1.0000
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Pos Pred Value 0.9167 1.0000 1.0000
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Neg Pred Value 1.0000 0.9524 1.0000
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Prevalence 0.3235 0.4118 0.2647
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Detection Rate 0.3235 0.3824 0.2647
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Detection Prevalence 0.3529 0.3824 0.2647
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Balanced Accuracy 0.9783 0.9643 1.0000
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=== svm rbf ===
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Confusion Matrix and Statistics
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Reference
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Prediction 1 2 3
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1 11 1 0
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2 0 13 0
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3 0 0 9
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Overall Statistics
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Accuracy : 0.9706
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95% CI : (0.8467, 0.9993)
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No Information Rate : 0.4118
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P-Value [Acc > NIR] : 3.92e-12
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Kappa : 0.9553
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Mcnemar's Test P-Value : NA
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Statistics by Class:
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Class: 1 Class: 2 Class: 3
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Sensitivity 1.0000 0.9286 1.0000
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Specificity 0.9565 1.0000 1.0000
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Pos Pred Value 0.9167 1.0000 1.0000
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Neg Pred Value 1.0000 0.9524 1.0000
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Prevalence 0.3235 0.4118 0.2647
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Detection Rate 0.3235 0.3824 0.2647
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Detection Prevalence 0.3529 0.3824 0.2647
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Balanced Accuracy 0.9783 0.9643 1.0000
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=== random forest ===
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Confusion Matrix and Statistics
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Reference
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Prediction 1 2 3
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1 11 1 0
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2 0 13 0
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3 0 0 9
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Overall Statistics
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Accuracy : 0.9706
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95% CI : (0.8467, 0.9993)
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No Information Rate : 0.4118
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P-Value [Acc > NIR] : 3.92e-12
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Kappa : 0.9553
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Mcnemar's Test P-Value : NA
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Statistics by Class:
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Class: 1 Class: 2 Class: 3
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Sensitivity 1.0000 0.9286 1.0000
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Specificity 0.9565 1.0000 1.0000
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Pos Pred Value 0.9167 1.0000 1.0000
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Neg Pred Value 1.0000 0.9524 1.0000
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Prevalence 0.3235 0.4118 0.2647
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Detection Rate 0.3235 0.3824 0.2647
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Detection Prevalence 0.3529 0.3824 0.2647
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Balanced Accuracy 0.9783 0.9643 1.0000
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@@ -0,0 +1,4 @@
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model accuracy precision_macro recall_macro f1_macro
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svm_linear 0.970588235294118 0.972222222222222 0.976190476190476 0.973161567364466
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svm_rbf 0.970588235294118 0.972222222222222 0.976190476190476 0.973161567364466
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random_forest 0.970588235294118 0.972222222222222 0.976190476190476 0.973161567364466
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