mnist_classification.R

training_images <- readr::read_csv(
    "train.csv",
    col_types = "cf"
)

testing_images <- readr::read_csv(
    "test.csv",
    col_types = "cf"
)

# Prepare Training Data
n <- length(training_images$filepath)
training_matrix <- matrix(
    nrow = n,
    ncol = 28 * 28
)

for (i in 1:n){
    training_matrix[i, ] <- 
        as.vector(
            png::readPNG(
                training_images$filepath[i]
            )
        )
}

training_data <- cbind(
    dplyr::select(training_images, label),
    as.data.frame(
        training_matrix
    )
)

start <- Sys.time()
# Model Fitting
rf_fit <- parsnip::fit(
    parsnip::rand_forest(
        mode = "classification"
    ),
    data = training_data,
    formula = label ~ .
)
end <- Sys.time()
print(end - start)

# Save model
saveRDS(rf_fit, "mnist_model_fit.rds")

# Prepare Test Data
n_test <- length(testing_images$filepath)
testing_matrix <- matrix(
    nrow = n,
    ncol = 28 * 28
)

for (i in 1:n_test){
    testing_matrix[i, ] <- 
        as.vector(
            png::readPNG(
                testing_images$filepath[i]
            )
        )
}

testing_data <- na.omit(
    cbind(
        dplyr::select(testing_images, label),
        as.data.frame(
            testing_matrix
        )
    )
)

# Model Evaluation
predictions <- predict(
    rf_fit,
    testing_data
)

final_result <-
    dplyr::bind_cols(
        predictions,
        dplyr::select(
            testing_data,
            label
        )
    )

yardstick::metrics(
    final_result,
    truth = "label",
    estimate = ".pred_class"
)

wrong_idx <- which(final_result$label != final_result$.pred_class)
right_idx <- which(final_result$label == final_result$.pred_class)

length(wrong_idx)

random_right <- sample(right_idx, 3)
random_wrong <- sample(wrong_idx, 3)

# Plot the mistakes
ggplot2::ggplot(
    data = data.frame(
        x = seq(1, 10, length.out = 6),
        y = 1,
        images = testing_images$filepath[c(random_right, random_wrong)]
    ),
    ggplot2::aes(
        x, 
        y,
        image = images,
        label = paste(final_result$.pred_class[c(random_right, random_wrong)]) 
    )
) +
    ggimage::geom_image(
        size=.10
    ) +
    ggplot2::scale_y_continuous(
        limits = c(0, 2)
    ) +
    ggplot2::scale_x_continuous(
        limits = c(0, 11)
    ) +
    ggplot2::geom_text(
        size = 10,
        nudge_y = 0.25,
        color = c("green", "green", "green", "red", "red", "red")
    ) +
    ggplot2::theme_void()