# tests/testthat/test-logistic_analysis.R

library(testthat)

# Load the test dataset
data(iris)

# Split the data into training and testing sets (70% training, 30% testing)
set.seed(123)  # Set a seed for reproducibility
train_indices <- sample(seq_len(nrow(iris)), size = 0.7 * nrow(iris))
train_data <- iris[train_indices, ]
test_data <- iris[-train_indices, ]

# Test logistic_analysis function
test_that("logistic_analysis fits a multinomial logistic regression model", {
  # Define the formula
  formula <- Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width

  # Fit the model using logistic_analysis on the training set
  model <- logistic_analysis(train_data, formula)

  # Check that the model is of class 'multinom'
  expect_s3_class(model, "multinom")

  # Check if the model has coefficients
  expect_true(length(coef(model)) > 0)
})

# Test logistic_predict function
test_that("logistic_predict makes predictions", {
  # Fit the model using logistic_analysis on the training set
  formula <- Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width
  model <- logistic_analysis(train_data, formula)

  # Make predictions on the testing data
  predictions <- logistic_predict(model, test_data)

  # Check that predictions are of class 'factor'
  expect_s3_class(predictions, "factor")

  # Check that the predicted classes are in the levels of the iris Species
  expect_true(all(predictions %in% levels(iris$Species)))
})

# Test logistic_plot function
test_that("logistic_plot generates a plot", {
  # Fit the model using logistic_analysis on the training set
  formula <- Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width
  model <- logistic_analysis(train_data, formula)

  # Create a plot file
  png(filename = "logistic_plot_test.png")

  # Call logistic_plot to generate the plot
  expect_error(logistic_plot(model, train_data, formula), NA)

  # Close the plot device
  dev.off()

  # Check if the plot file was created
  expect_true(file.exists("logistic_plot_test.png"))

  # Optionally, clean up the file after the test
  file.remove("logistic_plot_test.png")
})