# DAL ToolBox
# version 1.1.727

source("https://raw.githubusercontent.com/cefet-rj-dal/daltoolbox/main/jupyter.R")

#loading DAL
load_library("daltoolbox") 

iris <- datasets::iris
head(iris)

#extracting the levels for the dataset
slevels <- levels(iris$Species)
slevels

# preparing dataset for random sampling
set.seed(1)
sr <- sample_random()
sr <- train_test(sr, iris)
iris_train <- sr$train
iris_test <- sr$test

tbl <- rbind(table(iris[,"Species"]), 
             table(iris_train[,"Species"]), 
             table(iris_test[,"Species"]))
rownames(tbl) <- c("dataset", "training", "test")
head(tbl)

model <- cla_nb("Species", slevels)
model <- fit(model, iris_train)
train_prediction <- predict(model, iris_train)

iris_train_predictand <- adjust_class_label(iris_train[,"Species"])
train_eval <- evaluate(model, iris_train_predictand, train_prediction)
print(train_eval$metrics)

# Test
test_prediction <- predict(model, iris_test)

iris_test_predictand <- adjust_class_label(iris_test[,"Species"])

#Avaliação #setosa
test_eval <- evaluate(model, iris_test_predictand, test_prediction)
print(test_eval$metrics)

#Avaliação #versicolor
test_eval <- evaluate(model, iris_test_predictand, test_prediction, ref=2)
print(test_eval$metrics)

#Avaliação #virginica
test_eval <- evaluate(model, iris_test_predictand, test_prediction, ref=3)
print(test_eval$metrics)