Class: DSPy::Teleprompt::SimpleOptimizer

Inherits:
Teleprompter show all
Extended by:
T::Sig
Defined in:
lib/dspy/teleprompt/simple_optimizer.rb

Overview

Simple optimization algorithm using random/grid search Uses grounded proposer for instruction generation and bootstrap for examples

Defined Under Namespace

Classes: OptimizerConfig, TrialResult

Instance Attribute Summary collapse

Attributes inherited from Teleprompter

#config, #evaluator, #metric

Instance Method Summary collapse

Methods inherited from Teleprompter

#create_evaluator, #ensure_typed_examples, #evaluate_program, #save_results, #validate_inputs

Constructor Details

#initialize(metric: nil, config: nil) ⇒ SimpleOptimizer

Returns a new instance of SimpleOptimizer.



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# File 'lib/dspy/teleprompt/simple_optimizer.rb', line 109

def initialize(metric: nil, config: nil)
  @optimizer_config = config || OptimizerConfig.new
  super(metric: metric, config: @optimizer_config)
  
  @proposer = if @optimizer_config.use_instruction_optimization
    DSPy::Propose::GroundedProposer.new(config: @optimizer_config.proposer_config)
  else
    nil
  end
end

Instance Attribute Details

#optimizer_config ⇒ Object (readonly)

Returns the value of attribute optimizer_config.



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# File 'lib/dspy/teleprompt/simple_optimizer.rb', line 98

def optimizer_config
  @optimizer_config
end

#proposer ⇒ Object (readonly)

Returns the value of attribute proposer.



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# File 'lib/dspy/teleprompt/simple_optimizer.rb', line 101

def proposer
  @proposer
end

Instance Method Details

#compile(program, trainset:, valset: nil) ⇒ Object 



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# File 'lib/dspy/teleprompt/simple_optimizer.rb', line 128

def compile(program, trainset:, valset: nil)
  validate_inputs(program, trainset, valset)

  instrument_step('compile', {
    trainset_size: trainset.size,
    valset_size: valset&.size || 0,
    num_trials: @optimizer_config.num_trials,
    search_strategy: @optimizer_config.search_strategy
  }) do
    # Convert examples to typed format
    typed_trainset = ensure_typed_examples(trainset)
    typed_valset = valset ? ensure_typed_examples(valset) : nil

    # Use validation set if available, otherwise use part of training set
    evaluation_set = typed_valset || typed_trainset.take(10)

    # Bootstrap few-shot examples if enabled
    bootstrap_result = nil
    if @optimizer_config.use_few_shot_optimization
      bootstrap_result = bootstrap_examples(program, typed_trainset)
    end

    # Generate instruction candidates if enabled
    instruction_candidates = []
    if @optimizer_config.use_instruction_optimization && @proposer
      instruction_candidates = generate_instruction_candidates(program, typed_trainset, bootstrap_result)
    end

    # Run optimization trials
    trials = run_optimization_trials(
      program,
      evaluation_set,
      instruction_candidates,
      bootstrap_result
    )

    # Find best trial
    best_trial = find_best_trial(trials)

    # Build optimization result
    optimization_result = build_optimization_result(best_trial, trials)
    
    save_results(optimization_result)
    optimization_result
  end
end