ЛР2

library(caret)
library(arules)
library(Boruta)
library(mlbench)
library(FSelectorRcpp)

model_list <- names(getModelInfo())
length(model_list)

## [1] 239

head(model_list, 10)

##  [1] "ada"         "AdaBag"      "AdaBoost.M1" "adaboost"    "amdai"      
##  [6] "ANFIS"       "avNNet"      "awnb"        "awtan"       "bag"

set.seed(123)
x <- matrix(rnorm(50*5), ncol = 5)
colnames(x) <- paste0("Var", 1:5)
y <- factor(rep(c("A", "B"), 25))

# сохранение в jpg
jpeg("plot_box.jpg", width = 800, height = 600)
print(featurePlot(x, y, plot = "box", main = "Box Plot Features"))
dev.off()

## png 
##   2

jpeg("plot_density.jpg", width = 800, height = 600)
print(featurePlot(x, y, plot = "density", main = "Density Plot Features"))
dev.off()

## png 
##   2

jpeg("plot_pairs.jpg", width = 800, height = 600)
print(featurePlot(x, y, plot = "pairs", main = "Pairs Plot Features"))
dev.off()

## png 
##   2

# вывод графиков в отчёт
featurePlot(x, y, plot = "box", main = "Box Plot Features")

featurePlot(x, y, plot = "density", main = "Density Plot Features")

featurePlot(x, y, plot = "pairs", main = "Pairs Plot Features")

data(iris)
weights <- information_gain(Species ~ ., data = iris)
weights_sorted <- weights[order(-weights$importance), , drop = FALSE]
print(weights_sorted)

##     attributes importance
## 4  Petal.Width  0.9554360
## 3 Petal.Length  0.9402853
## 1 Sepal.Length  0.4521286
## 2  Sepal.Width  0.2672750

barplot(weights_sorted$importance, names.arg = weights_sorted$attributes,
        main = "Information Gain", col = "steelblue")

sl <- iris$Sepal.Length

d_interval <- arules::discretize(sl, method = "interval", breaks = 3)
cat("Метод Interval:\n"); print(table(d_interval))

## Метод Interval:

## d_interval
## [4.3,5.5) [5.5,6.7) [6.7,7.9] 
##        52        70        28

d_freq <- arules::discretize(sl, method = "frequency", breaks = 3)
cat("\nМетод Frequency:\n"); print(table(d_freq))

## 
## Метод Frequency:

## d_freq
## [4.3,5.4) [5.4,6.3) [6.3,7.9] 
##        46        53        51

d_cluster <- arules::discretize(sl, method = "cluster", breaks = 3)
cat("\nМетод Cluster:\n"); print(table(d_cluster))

## 
## Метод Cluster:

## d_cluster
##  [4.3,5.33) [5.33,6.27)  [6.27,7.9] 
##          46          53          51

d_fixed <- arules::discretize(sl, method = "fixed", breaks = c(0, 5, 6, 8))
cat("\nМетод Fixed:\n"); print(table(d_fixed))

## 
## Метод Fixed:

## d_fixed
## [0,5) [5,6) [6,8] 
##    22    61    67

data("Ozone")
ozone_clean <- na.omit(Ozone)

set.seed(123)
boruta_output <- Boruta(V4 ~ ., data = ozone_clean, doTrace = 0)
print(boruta_output)

## Boruta performed 18 iterations in 0.2640769 secs.
##  9 attributes confirmed important: V1, V10, V11, V12, V13 and 4 more;
##  3 attributes confirmed unimportant: V2, V3, V6;

getSelectedAttributes(boruta_output, withTentative = FALSE)

## [1] "V1"  "V5"  "V7"  "V8"  "V9"  "V10" "V11" "V12" "V13"

attStats(boruta_output)

##         meanImp   medianImp      minImp     maxImp   normHits  decision
## V1   0.40660229  0.39874710  0.37208192 0.50361585 1.00000000 Confirmed
## V2   0.04391890  0.03169822 -0.02604472 0.13137821 0.11111111  Rejected
## V3  -0.03235907 -0.04528594 -0.08039941 0.03178173 0.00000000  Rejected
## V5   0.37961385  0.37663333  0.33809868 0.42147363 1.00000000 Confirmed
## V6   0.04740489  0.05979747 -0.01572443 0.12718160 0.05555556  Rejected
## V7   0.51883205  0.50588169  0.46725744 0.59114026 1.00000000 Confirmed
## V8   0.77086858  0.76889989  0.73251688 0.81327051 1.00000000 Confirmed
## V9   0.86915268  0.86432645  0.84273725 0.92413075 1.00000000 Confirmed
## V10  0.42389058  0.41739463  0.38164901 0.49245967 1.00000000 Confirmed
## V11  0.55143690  0.54721914  0.49482770 0.61161304 1.00000000 Confirmed
## V12  0.66051279  0.65832222  0.60234621 0.73958611 1.00000000 Confirmed
## V13  0.42761454  0.42057856  0.38618408 0.46920282 1.00000000 Confirmed

plot(boruta_output, cex.axis = 0.7, las = 2, xlab = "",
     main = "Важность признаков (Boruta)")