text: // Copyright (c) by respective owners including Yahoo!, Microsoft, and
  // individual contributors. All rights reserved. Released under a BSD (revised)
  // license as described in the file LICENSE.
  
  #include "vw/core/reductions/cb/cb_explore_adf_softmax.h"
  
  #include "vw/config/options.h"
  #include "vw/core/gen_cs_example.h"
  #include "vw/core/label_parser.h"
  #include "vw/core/rand48.h"
  #include "vw/core/reductions/cb/cb_adf.h"
  #include "vw/core/reductions/cb/cb_explore.h"
  #include "vw/core/setup_base.h"
  #include "vw/explore/explore.h"
  
  #include <algorithm>
  #include <cmath>
  #include <vector>
  using namespace VW::cb_explore_adf;
  
  namespace
  {
  struct cb_explore_adf_softmax
  {
  private:
    float _epsilon;
    float _lambda;
  
  public:
    cb_explore_adf_softmax(float epsilon, float lambda);
    ~cb_explore_adf_softmax() = default;
  
    // Should be called through cb_explore_adf_base for pre/post-processing
    void predict(VW::LEARNER::multi_learner& base, VW::multi_ex& examples)
    {
      predict_or_learn_impl<false>(base, examples);
    }
    void learn(VW::LEARNER::multi_learner& base, VW::multi_ex& examples) { predict_or_learn_impl<true>(base, examples); }
  
  private:
    template <bool is_learn>
    void predict_or_learn_impl(VW::LEARNER::multi_learner& base, VW::multi_ex& examples);
  };
  
  cb_explore_adf_softmax::cb_explore_adf_softmax(float epsilon, float lambda) : _epsilon(epsilon), _lambda(lambda) {}
  
  template <bool is_learn>
  void cb_explore_adf_softmax::predict_or_learn_impl(VW::LEARNER::multi_learner& base, VW::multi_ex& examples)
  {
    VW::LEARNER::multiline_learn_or_predict<is_learn>(base, examples, examples[0]->ft_offset);
  
    v_array<ACTION_SCORE::action_score>& preds = examples[0]->pred.a_s;
    exploration::generate_softmax(
        -_lambda, begin_scores(preds), end_scores(preds), begin_scores(preds), end_scores(preds));
  
    exploration::enforce_minimum_probability(_epsilon, true, begin_scores(preds), end_scores(preds));
  }
  }  // namespace
  VW::LEARNER::base_learner* VW::reductions::cb_explore_adf_softmax_setup(VW::setup_base_i& stack_builder)
  {
    VW::config::options_i& options = *stack_builder.get_options();
    VW::workspace& all = *stack_builder.get_all_pointer();
    using config::make_option;
    bool cb_explore_adf_option = false;
    bool softmax = false;
    float epsilon = 0.;
    float lambda = 0.;
    config::option_group_definition new_options("[Reduction] Contextual Bandit Exploration with ADF (softmax)");
    new_options
        .add(make_option("cb_explore_adf", cb_explore_adf_option)
                 .keep()
                 .necessary()
                 .help("Online explore-exploit for a contextual bandit problem with multiline action dependent features"))
        .add(
            make_option("epsilon", epsilon).default_value(0.f).keep().allow_override().help("Epsilon-greedy exploration"))
        .add(make_option("softmax", softmax).keep().necessary().help("Softmax exploration"))
        .add(make_option("lambda", lambda).keep().allow_override().default_value(1.f).help("Parameter for softmax"));
  
    if (!options.add_parse_and_check_necessary(new_options)) { return nullptr; }
  
    if (lambda < 0)
    {  // Lambda should always be positive because we are using a cost basis.
      lambda = -lambda;
    }
  
    // Ensure serialization of cb_adf in all cases.
    if (!options.was_supplied("cb_adf")) { options.insert("cb_adf", ""); }
  
    // Set explore_type
    size_t problem_multiplier = 1;
  
    VW::LEARNER::multi_learner* base = as_multiline(stack_builder.setup_base_learner());
    all.example_parser->lbl_parser = CB::cb_label;
  
    bool with_metrics = options.was_supplied("extra_metrics");
  
    using explore_type = cb_explore_adf_base<cb_explore_adf_softmax>;
    auto data = VW::make_unique<explore_type>(with_metrics, epsilon, lambda);
  
    if (epsilon < 0.0 || epsilon > 1.0) { THROW("The value of epsilon must be in [0,1]"); }
    auto* l = make_reduction_learner(std::move(data), base, explore_type::learn, explore_type::predict,
        stack_builder.get_setupfn_name(cb_explore_adf_softmax_setup))
                  .set_input_label_type(VW::label_type_t::cb)
                  .set_output_label_type(VW::label_type_t::cb)
                  .set_input_prediction_type(VW::prediction_type_t::action_scores)
                  .set_output_prediction_type(VW::prediction_type_t::action_probs)
                  .set_params_per_weight(problem_multiplier)
                  .set_finish_example(explore_type::finish_multiline_example)
                  .set_print_example(explore_type::print_multiline_example)
                  .set_persist_metrics(explore_type::persist_metrics)
                  .build(&all.logger);
    return make_base(*l);
  }
  