scout

defmodule Scout.Integration.Axon do
  @moduledoc """
  Integration with Axon neural network library for Elixir.
  
  Provides hyperparameter optimization for:
  - Network architecture (layers, units, activation functions)
  - Training parameters (learning rate, batch size, epochs)
  - Regularization (dropout, weight decay)
  - Optimizers (Adam, SGD, RMSprop parameters)
  """
  
  alias Scout.{Study, Trial}
  
  @doc """
  Optimizes Axon model hyperparameters.
  
  ## Example
  
      def create_model(params) do
        Axon.input("input", shape: {nil, 784})
        |> Axon.dense(params[:hidden1_units], activation: params[:activation1])
        |> Axon.dropout(rate: params[:dropout1])
        |> Axon.dense(params[:hidden2_units], activation: params[:activation2])
        |> Axon.dropout(rate: params[:dropout2])
        |> Axon.dense(10, activation: :softmax)
      end
      
      def train_model(model, params, data) do
        model
        |> Axon.Loop.trainer(:categorical_cross_entropy, 
             Axon.Optimizers.adam(params[:learning_rate]))
        |> Axon.Loop.metric(:accuracy)
        |> Axon.Loop.run(data, %{}, epochs: params[:epochs], 
             batch_size: params[:batch_size])
      end
      
      Scout.Integration.Axon.optimize(
        &create_model/1,
        &train_model/3,
        train_data,
        val_data,
        search_space: %{
          hidden1_units: {:int, 32, 512},
          hidden2_units: {:int, 32, 512},
          activation1: {:choice, [:relu, :tanh, :sigmoid]},
          activation2: {:choice, [:relu, :tanh, :sigmoid]},
          dropout1: {:uniform, 0.0, 0.5},
          dropout2: {:uniform, 0.0, 0.5},
          learning_rate: {:log_uniform, 1.0e-5, 1.0e-1},
          batch_size: {:choice, [16, 32, 64, 128]},
          epochs: {:int, 10, 100}
        },
        n_trials: 100,
        pruner: Scout.Pruner.MedianPruner
      )
  """
  def optimize(model_fn, train_fn, train_data, val_data, opts \\ []) do
    search_space = Keyword.fetch!(opts, :search_space)
    n_trials = Keyword.get(opts, :n_trials, 100)
    direction = Keyword.get(opts, :direction, :maximize)
    pruner = Keyword.get(opts, :pruner)
    sampler = Keyword.get(opts, :sampler, Scout.Sampler.TPE)
    metric = Keyword.get(opts, :metric, :accuracy)
    
    objective = fn trial ->
      # Sample hyperparameters
      params = sample_params(trial, search_space)
      
      # Create model
      model = model_fn.(params)
      
      # Train with pruning support
      result = train_with_pruning(
        model, 
        params, 
        train_data, 
        val_data,
        train_fn,
        trial,
        metric
      )
      
      result
    end
    
    study = %Study{
      id: "axon_optimization_#{System.unique_integer([:positive])}",
      goal: direction,
      max_trials: n_trials,
      parallelism: 1,  # Add required field
      search_space: fn _ix -> search_space end,
      objective: objective,
      sampler: sampler,
      sampler_opts: %{},
      pruner: pruner,
      pruner_opts: %{},
      metadata: %{framework: "axon"}
    }
    
    Scout.run(study)
  end
  
  defp sample_params(trial, search_space) do
    for {param_name, spec} <- search_space, into: %{} do
      value = case spec do
        {:uniform, min, max} ->
          Trial.suggest_float(trial, param_name, min, max)
        {:log_uniform, min, max} ->
          Trial.suggest_float(trial, param_name, min, max, log: true)
        {:int, min, max} ->
          Trial.suggest_int(trial, param_name, min, max)
        {:choice, choices} ->
          Trial.suggest_categorical(trial, param_name, choices)
      end
      
      {param_name, value}
    end
  end
  
  defp train_with_pruning(model, params, train_data, val_data, train_fn, trial, metric) do
    # Custom training loop with intermediate reporting
    epochs = Map.get(params, :epochs, 10)
    
    Enum.reduce_while(1..epochs, nil, fn epoch, _acc ->
      # Train for one epoch
      epoch_result = train_single_epoch(model, params, train_data, train_fn)
      
      # Evaluate on validation set
      val_metric = evaluate(model, val_data, metric)
      
      # Report intermediate value
      Trial.report(trial, val_metric, epoch)
      
      # Check for pruning
      if Trial.should_prune?(trial) do
        {:halt, :pruned}
      else
        if epoch == epochs do
          {:halt, val_metric}
        else
          {:cont, val_metric}
        end
      end
    end)
  end
  
  defp train_single_epoch(_model, _params, _train_data, _train_fn) do
    # Placeholder - would integrate with actual Axon training
    %{loss: :rand.uniform()}
  end
  
  defp evaluate(_model, _val_data, _metric) do
    # Placeholder - would integrate with actual Axon evaluation
    :rand.uniform()
  end
  
  @doc """
  Pruning callback for Axon training loops.
  
  Use this in your Axon.Loop to enable pruning during training:
  
      loop
      |> Axon.Loop.handle_event(:epoch_completed, 
           &Scout.Integration.Axon.pruning_callback(&1, &2, trial))
  """
  def pruning_callback(state, _metadata, trial) do
    # Extract metric from state
    metric_value = get_in(state, [:metrics, :validation, :accuracy])
    
    # Report to trial
    epoch = Map.get(state, :epoch, 0)
    Trial.report(trial, metric_value, epoch)
    
    # Check for pruning
    if Trial.should_prune?(trial) do
      {:halt_loop, state}
    else
      {:continue, state}
    end
  end
  
  @doc """
  Suggests optimal architecture based on dataset characteristics.
  """
  def suggest_architecture(input_shape, output_shape, task_type \\ :classification) do
    input_size = elem(input_shape, -1)
    output_size = case output_shape do
      n when is_integer(n) -> n
      shape when is_tuple(shape) -> elem(shape, -1)
    end
    
    case task_type do
      :classification ->
        %{
          hidden_layers: suggest_hidden_layers(input_size, output_size),
          activation: :relu,
          dropout: 0.2,
          output_activation: if(output_size == 1, do: :sigmoid, else: :softmax)
        }
        
      :regression ->
        %{
          hidden_layers: suggest_hidden_layers(input_size, output_size),
          activation: :relu,
          dropout: 0.1,
          output_activation: :linear
        }
        
      :autoencoder ->
        %{
          encoder_layers: suggest_encoder_layers(input_size),
          decoder_layers: suggest_decoder_layers(input_size),
          activation: :relu,
          latent_dim: suggest_latent_dim(input_size)
        }
    end
  end
  
  defp suggest_hidden_layers(input_size, output_size) do
    # Rule of thumb: geometric mean for first hidden layer
    first_layer = round(:math.sqrt(input_size * output_size))
    
    cond do
      input_size < 100 ->
        [first_layer]
      input_size < 1000 ->
        [first_layer, round(first_layer / 2)]
      true ->
        [first_layer, round(first_layer / 2), round(first_layer / 4)]
    end
  end
  
  defp suggest_encoder_layers(input_size) do
    [
      round(input_size / 2),
      round(input_size / 4),
      round(input_size / 8)
    ]
    |> Enum.filter(&(&1 > 10))
  end
  
  defp suggest_decoder_layers(input_size) do
    suggest_encoder_layers(input_size)
    |> Enum.reverse()
  end
  
  defp suggest_latent_dim(input_size) do
    max(2, round(:math.log2(input_size)))
  end
end