module EasyML
  class Trainer
    # include GlueGun::DSL
    # include EasyML::Logging

    # define_attr :verbose, default: false
    # define_attr :root_dir do |root_dir|
    #   File.join(root_dir, "trainer")
    # end

    # define_config :dataset do |config|
    #   config.define_option :default do |option|
    #     option.set_class EasyML::Data::Dataset
    #     option.define_attr :root_dir
    #     option.define_attr :target
    #     option.define_attr :batch_size
    #   end
    # end

    # define_config :model do |config|
    #   config.define_option :default do |option|
    #     option.set_class EasyML::Model
    #     option.define_attr :root_dir
    #     option.define_attr :name
    #     option.define_attr :hyperparameters
    #   end
    # end

    # def train
    #   log_info("Starting training process") if verbose

    #   dataset.refresh!

    #   log_info("Fitting model") if verbose
    #   dataset.train(split_ys: true) do |xs, ys|
    #     model.fit(xs, ys)
    #   end

    #   log_info("Saving model") if verbose
    #   model.save

    #   log_info("Training completed") if verbose
    # end

    # def evaluate
    #   log_info("Starting evaluation process") if verbose

    #   results = {}

    #   %i[train test valid].each do |split|
    #     log_info("Evaluating on #{split} set") if verbose
    #     predictions = []
    #     actuals = []

    #     dataset.send(split, split_ys: true) do |xs, ys|
    #       batch_predictions = model.predict(xs)
    #       predictions.concat(batch_predictions.to_a)
    #       actuals.concat(ys.to_a)
    #     end

    #     results[split] = calculate_metrics(predictions, actuals)
    #   end

    #   log_info("Evaluation completed") if verbose
    #   results
    # end

    # private

    # def calculate_metrics(predictions, actuals)
    #   # Implement your metric calculations here
    #   # This is a placeholder and should be replaced with actual metric calculations
    #   {
    #     mse: mean_squared_error(predictions, actuals),
    #     mae: mean_absolute_error(predictions, actuals),
    #     r2: r_squared(predictions, actuals)
    #   }
    # end

    # def mean_squared_error(predictions, actuals)
    #   # Implement MSE calculation
    # end

    # def mean_absolute_error(predictions, actuals)
    #   # Implement MAE calculation
    # end

    # def r_squared(predictions, actuals)
    #   # Implement R-squared calculation
    # end
  end
end