automl_nas.mbt

// 神经架构搜索与AutoML（v45.0.0）
// NAS、AutoML、超参数优化、神经架构搜索、模型选择

///|
/// NAS算法类型
pub(all) enum NASAlgorithm {
  ReinforcementLearning
  Evolutionary
  GradientBased
  BayesianOptimization
  RandomSearch
  GridSearch
  Differentiable
  OneShot
  Unknown
} derive(Eq)

///|
/// 超参数优化算法类型
pub(all) enum HyperparameterOptimizationAlgorithm {
  GridSearch
  RandomSearch
  BayesianOptimization
  TPE
  SMAC
  Hyperband
  ASHA
  PopulationBasedTraining
  Unknown
} derive(Eq)

///|
/// 搜索空间类型
pub(all) enum SearchSpaceType {
  Macro
  Micro
  CellBased
  Hierarchical
  Joint
  Unknown
} derive(Eq)

///|
/// 神经网络层类型
pub(all) enum LayerType {
  Convolution
  Pooling
  FullyConnected
  BatchNorm
  Dropout
  Activation
  Attention
  Recurrent
  Transformer
  Unknown
} derive(Eq)

///|
/// 网络架构
pub(all) struct NetworkArchitecture {
  layers : Array[LayerType]
  num_layers : Int
  num_parameters : Int
  flops : Int
  memory_mb : Double
  inference_time_ms : Double
  architecture_id : Int
}

///|
/// 超参数配置
pub(all) struct HyperparameterConfig {
  learning_rate : Double
  batch_size : Int
  num_epochs : Int
  optimizer : String
  weight_decay : Double
  dropout_rate : Double
  hidden_dim : Int
  num_layers : Int
  num_heads : Int
  activation : String
  config_id : Int
}

///|
/// NAS配置
pub(all) struct NASConfig {
  algorithm : NASAlgorithm
  search_space_type : SearchSpaceType
  num_trials : Int
  max_layers : Int
  max_channels : Int
  population_size : Int
  mutation_rate : Double
  crossover_rate : Double
  learning_rate_controller : Double
  use_pretrained : Bool
  use_weight_sharing : Bool
  resource_constraint_mb : Double
  time_budget_s : Double
}

///|
/// 超参数优化配置
pub(all) struct HPOConfig {
  algorithm : HyperparameterOptimizationAlgorithm
  num_trials : Int
  num_epochs_per_trial : Int
  search_space : Array[String]
  use_early_stopping : Bool
  early_stopping_patience : Int
  use_parallel : Bool
  num_workers : Int
  resource_allocation : String
}

///|
/// 搜索结果
pub(all) struct SearchResult {
  best_architecture : NetworkArchitecture
  best_hyperparameters : HyperparameterConfig
  best_accuracy : Double
  best_loss : Double
  num_trials_completed : Int
  search_time_s : Double
  all_accuracies : Array[Double]
  convergence_curve : Array[Double]
}

///|
/// 模型评估指标
pub(all) struct ModelEvaluationMetrics {
  accuracy : Double
  loss : Double
  precision : Double
  recall : Double
  f1_score : Double
  auc_roc : Double
  inference_time_ms : Double
  memory_usage_mb : Double
  model_size_mb : Double
  num_parameters : Int
  flops : Int
}

///|
/// 创建默认NAS配置
pub fn create_default_nas_config() -> NASConfig {
  NASConfig::{
    algorithm: NASAlgorithm::Evolutionary,
    search_space_type: SearchSpaceType::CellBased,
    num_trials: 100,
    max_layers: 20,
    max_channels: 1024,
    population_size: 50,
    mutation_rate: 0.1,
    crossover_rate: 0.8,
    learning_rate_controller: 0.001,
    use_pretrained: false,
    use_weight_sharing: true,
    resource_constraint_mb: 1024.0,
    time_budget_s: 3600.0,
  }
}

///|
/// 创建默认HPO配置
pub fn create_default_hpo_config() -> HPOConfig {
  HPOConfig::{
    algorithm: HyperparameterOptimizationAlgorithm::BayesianOptimization,
    num_trials: 50,
    num_epochs_per_trial: 10,
    search_space: ["learning_rate", "batch_size", "dropout_rate", "hidden_dim"],
    use_early_stopping: true,
    early_stopping_patience: 5,
    use_parallel: false,
    num_workers: 1,
    resource_allocation: "uniform",
  }
}

///|
/// 生成随机网络架构
pub fn generate_random_architecture(
  num_layers : Int,
  max_channels : Int,
) -> NetworkArchitecture {
  let layers : Array[LayerType] = []
  let mut num_params = 0
  let mut flops = 0

  let layer_types = [
    LayerType::Convolution,
    LayerType::Pooling,
    LayerType::FullyConnected,
    LayerType::BatchNorm,
    LayerType::Dropout,
    LayerType::Activation,
    LayerType::Attention,
  ]

  let mut state = 42
  for _ in 0..<num_layers {
    state = ((state * 11035 + 12345).reinterpret_as_uint() % 1000000).reinterpret_as_int()
    let layer_idx = state % layer_types.length()
    let layer = layer_types[layer_idx]
    layers.push(layer)

    // 简化的参数和FLOPs计算
    match layer {
      LayerType::Convolution => {
        num_params += max_channels * 9
        flops += max_channels * 9 * 224 * 224
      }
      LayerType::FullyConnected => {
        num_params += max_channels * max_channels
        flops += max_channels * max_channels
      }
      LayerType::Attention => {
        num_params += max_channels * max_channels * 4
        flops += max_channels * max_channels * 4
      }
      _ => {
        num_params += max_channels
        flops += max_channels * 1000
      }
    }
  }

  NetworkArchitecture::{
    layers,
    num_layers,
    num_parameters: num_params,
    flops,
    memory_mb: num_params.to_double() * 4.0 / 1024.0 / 1024.0,
    inference_time_ms: flops.to_double() / 1000000000.0 * 10.0,
    architecture_id: 0,
  }
}

///|
/// 生成随机超参数配置
pub fn generate_random_hyperparameters() -> HyperparameterConfig {
  let mut state = 42
  state = ((state * 11035 + 12345).reinterpret_as_uint() % 1000000).reinterpret_as_int()
  let lr = 0.0001 + (state % 1000).to_double() / 1000.0 * 0.01
  state = ((state * 11035 + 12345).reinterpret_as_uint() % 1000000).reinterpret_as_int()
  let batch = 16 + state % 8 * 16
  state = ((state * 11035 + 12345).reinterpret_as_uint() % 1000000).reinterpret_as_int()
  let dropout = (state % 100).to_double() / 100.0 * 0.5
  state = ((state * 11035 + 12345).reinterpret_as_uint() % 1000000).reinterpret_as_int()
  let hidden = 64 + state % 8 * 64

  HyperparameterConfig::{
    learning_rate: lr,
    batch_size: batch,
    num_epochs: 50,
    optimizer: "adam",
    weight_decay: 0.0001,
    dropout_rate: dropout,
    hidden_dim: hidden,
    num_layers: 6,
    num_heads: 8,
    activation: "relu",
    config_id: 0,
  }
}

///|
/// 评估模型（简化版）
pub fn evaluate_model(
  architecture : NetworkArchitecture,
  hyperparameters : HyperparameterConfig,
) -> ModelEvaluationMetrics {
  // 简化的模型评估：基于架构和超参数的启发式评分
  let base_accuracy = 0.7
  let architecture_bonus = if architecture.num_layers > 10 {
    0.05
  } else if architecture.num_layers > 5 {
    0.03
  } else {
    0.01
  }

  let lr_bonus = if hyperparameters.learning_rate > 0.0001 &&
    hyperparameters.learning_rate < 0.01 {
    0.05
  } else {
    0.0
  }

  let dropout_bonus = if hyperparameters.dropout_rate > 0.1 &&
    hyperparameters.dropout_rate < 0.4 {
    0.03
  } else {
    0.0
  }

  let accuracy = base_accuracy + architecture_bonus + lr_bonus + dropout_bonus
  let loss = 1.0 - accuracy

  ModelEvaluationMetrics::{
    accuracy,
    loss,
    precision: accuracy * 0.95,
    recall: accuracy * 0.9,
    f1_score: accuracy * 0.92,
    auc_roc: accuracy * 0.98,
    inference_time_ms: architecture.inference_time_ms,
    memory_usage_mb: architecture.memory_mb,
    model_size_mb: architecture.memory_mb,
    num_parameters: architecture.num_parameters,
    flops: architecture.flops,
  }
}

///|
/// 执行神经架构搜索（简化版）
pub fn perform_nas(config : NASConfig) -> SearchResult {
  let all_accuracies : Array[Double] = []
  let convergence_curve : Array[Double] = []
  let mut best_accuracy = 0.0
  let mut best_arch = generate_random_architecture(10, config.max_channels)
  let mut best_hp = generate_random_hyperparameters()

  for trial in 0..<config.num_trials {
    // 生成随机架构
    let num_layers = 5 + trial % 15
    let arch = generate_random_architecture(num_layers, config.max_channels)
    let hp = generate_random_hyperparameters()

    // 评估
    let metrics = evaluate_model(arch, hp)
    all_accuracies.push(metrics.accuracy)

    if metrics.accuracy > best_accuracy {
      best_accuracy = metrics.accuracy
      best_arch = arch
      best_hp = hp
    }

    convergence_curve.push(best_accuracy)
  }

  SearchResult::{
    best_architecture: best_arch,
    best_hyperparameters: best_hp,
    best_accuracy,
    best_loss: 1.0 - best_accuracy,
    num_trials_completed: config.num_trials,
    search_time_s: config.num_trials.to_double() * 10.0,
    all_accuracies,
    convergence_curve,
  }
}

///|
/// 执行超参数优化（简化版）
pub fn perform_hpo(
  config : HPOConfig,
  base_architecture : NetworkArchitecture,
) -> SearchResult {
  let all_accuracies : Array[Double] = []
  let convergence_curve : Array[Double] = []
  let mut best_accuracy = 0.0
  let mut best_hp = generate_random_hyperparameters()

  for _ in 0..<config.num_trials {
    let hp = generate_random_hyperparameters()
    let metrics = evaluate_model(base_architecture, hp)
    all_accuracies.push(metrics.accuracy)

    if metrics.accuracy > best_accuracy {
      best_accuracy = metrics.accuracy
      best_hp = hp
    }

    convergence_curve.push(best_accuracy)
  }

  SearchResult::{
    best_architecture: base_architecture,
    best_hyperparameters: best_hp,
    best_accuracy,
    best_loss: 1.0 - best_accuracy,
    num_trials_completed: config.num_trials,
    search_time_s: config.num_trials.to_double() *
    config.num_epochs_per_trial.to_double() *
    5.0,
    all_accuracies,
    convergence_curve,
  }
}

///|
/// 生成AutoML报告
pub fn automl_report_to_string(
  result : SearchResult,
  config : NASConfig,
  metrics : ModelEvaluationMetrics,
) -> String {
  ignore(metrics)
  let mut report = "## 神经架构搜索与AutoML报告\n\n"

  report = report + "### 搜索配置\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report +
    "| NAS算法 | " +
    (match config.algorithm {
      NASAlgorithm::ReinforcementLearning => "强化学习"
      NASAlgorithm::Evolutionary => "进化算法"
      NASAlgorithm::GradientBased => "基于梯度"
      NASAlgorithm::BayesianOptimization => "贝叶斯优化"
      NASAlgorithm::RandomSearch => "随机搜索"
      _ => "未知"
    }) +
    " |\n"
  report = report +
    "| 搜索空间 | " +
    (match config.search_space_type {
      SearchSpaceType::Macro => "宏观"
      SearchSpaceType::Micro => "微观"
      SearchSpaceType::CellBased => "基于单元"
      _ => "未知"
    }) +
    " |\n"
  report = report + "| 试验次数 | " + config.num_trials.to_string() + " |\n"
  report = report + "| 最大层数 | " + config.max_layers.to_string() + " |\n"
  report = report +
    "| 种群大小 | " +
    config.population_size.to_string() +
    " |\n"
  report = report +
    "| 权重共享 | " +
    (if config.use_weight_sharing { "开启" } else { "关闭" }) +
    " |\n"

  report = report + "\n### 搜索结果\n\n"
  report = report + "| 指标 | 值 |\n|------|------|\n"
  report = report +
    "| 最佳精度 | " +
    (result.best_accuracy * 100.0).to_string() +
    "% |\n"
  report = report + "| 最佳损失 | " + result.best_loss.to_string() + " |\n"
  report = report +
    "| 完成试验 | " +
    result.num_trials_completed.to_string() +
    " |\n"
  report = report +
    "| 搜索时间 | " +
    result.search_time_s.to_string() +
    " s |\n"
  report = report +
    "| 最佳层数 | " +
    result.best_architecture.num_layers.to_string() +
    " |\n"
  report = report +
    "| 参数量 | " +
    result.best_architecture.num_parameters.to_string() +
    " |\n"
  report = report +
    "| FLOPs | " +
    result.best_architecture.flops.to_string() +
    " |\n"
  report = report +
    "| 推理时间 | " +
    result.best_architecture.inference_time_ms.to_string() +
    " ms |\n"
  report = report +
    "| 内存占用 | " +
    result.best_architecture.memory_mb.to_string() +
    " MB |\n"

  report = report + "\n### 最佳超参数\n\n"
  report = report + "| 超参数 | 值 |\n|------|------|\n"
  report = report +
    "| 学习率 | " +
    result.best_hyperparameters.learning_rate.to_string() +
    " |\n"
  report = report +
    "| 批次大小 | " +
    result.best_hyperparameters.batch_size.to_string() +
    " |\n"
  report = report +
    "| 训练轮数 | " +
    result.best_hyperparameters.num_epochs.to_string() +
    " |\n"
  report = report +
    "| 优化器 | " +
    result.best_hyperparameters.optimizer +
    " |\n"
  report = report +
    "| 权重衰减 | " +
    result.best_hyperparameters.weight_decay.to_string() +
    " |\n"
  report = report +
    "| Dropout率 | " +
    result.best_hyperparameters.dropout_rate.to_string() +
    " |\n"
  report = report +
    "| 隐藏维度 | " +
    result.best_hyperparameters.hidden_dim.to_string() +
    " |\n"
  report = report +
    "| 层数 | " +
    result.best_hyperparameters.num_layers.to_string() +
    " |\n"
  report = report +
    "| 注意力头数 | " +
    result.best_hyperparameters.num_heads.to_string() +
    " |\n"
  report = report +
    "| 激活函数 | " +
    result.best_hyperparameters.activation +
    " |\n"

  report
}
src\process\automl_nas\automl_nas.mbt

87.91%
