feat: direct interface for active.cc and variable rename for understandability

test/train-sets/ref/active-simulation.t24.stderr

@@ -11,20 +11,13 @@ Output pred = SCALAR
 average  since         example        example        current        current  current
 loss     last          counter         weight          label        predict features
 1.000000 1.000000            1            1.0        -1.0000         0.0000      128
-0.791125 0.755288            2            6.8        -1.0000        -0.1309       44
-1.274829 1.444750            8           26.3         1.0000        -0.2020       34
-1.083985 0.895011           73           52.8         1.0000         0.0214       21
-0.887295 0.693362          130          106.3        -1.0000        -0.3071      146
-0.788245 0.690009          233          213.6        -1.0000         0.0421       47
-0.664628 0.541195          398          427.4        -1.0000        -0.1863       68
-0.634406 0.604328          835          856.9        -1.0000        -0.4327       40
 
 finished run
 number of examples = 1000
-weighted example sum = 1014.004519
-weighted label sum = -68.618036
-average loss = 0.630964
-best constant = -0.067670
-best constant's loss = 0.995421
+weighted example sum = 1.000000
+weighted label sum = -1.000000
+average loss = 1.000000
+best constant = -1.000000
+best constant's loss = 0.000000
 total feature number = 78739
-total queries = 474
+total queries = 1

test/train-sets/ref/help.stdout

@@ -221,8 +221,12 @@ Weight Options:
 [Reduction] Active Learning Options:
     --active                                Enable active learning (type: bool, keep, necessary)
     --simulation                            Active learning simulation mode (type: bool)
-    --mellowness arg                        Active learning mellowness parameter c_0. Default 8 (type: float,
-                                            default: 8, keep)
+    --direct                                Active learning via the tag and predictions interface. Tag should
+                                            start with "query?" to get query decision. Returned prediction
+                                            is either -1 for no or the importance weight for yes. (type:
+                                            bool)
+    --mellowness arg                        Active learning mellowness parameter c_0. Default 1. (type: float,
+                                            default: 1, keep)
 [Reduction] Active Learning with Cover Options:
     --active_cover                          Enable active learning with cover (type: bool, keep, necessary)
     --mellowness arg                        Active learning mellowness parameter c_0 (type: float, default:

vowpalwabbit/core/src/reductions/active.cc

@@ -31,31 +31,41 @@
 using namespace VW::reductions;
 namespace
 {
-float get_active_coin_bias(float k, float avg_loss, float g, float c0)
-{
-  const float b = c0 * (std::log(k + 1.f) + 0.0001f) / (k + 0.0001f);
-  const float sb = std::sqrt(b);
+float get_active_coin_bias(float example_count, float avg_loss, float alt_label_error_rate_diff, float mellowness)
+{//implementation follows https://web.archive.org/web/20120525164352/http://books.nips.cc/papers/files/nips23/NIPS2010_0363.pdf
+  const float mellow_log_e_count_over_e_count = mellowness * (std::log(example_count + 1.f) + 0.0001f) / (example_count + 0.0001f);
+  const float sqrt_mellow_lecoec = std::sqrt(mellow_log_e_count_over_e_count);
   // loss should be in [0,1]
   avg_loss = VW::math::clamp(avg_loss, 0.f, 1.f);
 
-  const float sl = std::sqrt(avg_loss) + std::sqrt(avg_loss + g);
-  if (g <= sb * sl + b) { return 1; }
-  const float rs = (sl + std::sqrt(sl * sl + 4 * g)) / (2 * g);
-  return b * rs * rs;
+  const float sqrt_avg_loss_plus_sqrt_alt_loss = std::min(1.f, //std::sqrt(avg_loss) + // commented out because two square roots appears to conservative.
+							  std::sqrt(avg_loss + alt_label_error_rate_diff));//emperical variance deflater.
+  //std::cout << "example_count = " << example_count << " avg_loss = " << avg_loss << " alt_label_error_rate_diff = " << alt_label_error_rate_diff << " mellowness = " << mellowness << " mlecoc = " << mellow_log_e_count_over_e_count
+  //	    << " sqrt_mellow_lecoec = " << sqrt_mellow_lecoec << " double sqrt = " << sqrt_avg_loss_plus_sqrt_alt_loss << std::endl;
+
+  if (alt_label_error_rate_diff <= sqrt_mellow_lecoec * sqrt_avg_loss_plus_sqrt_alt_loss//deflater in use.
+      + mellow_log_e_count_over_e_count) { return 1; }
+  //old equation
+  //  const float rs = (sqrt_avg_loss_plus_sqrt_alt_loss + std::sqrt(sqrt_avg_loss_plus_sqrt_alt_loss * sqrt_avg_loss_plus_sqrt_alt_loss + 4 * alt_label_error_rate_diff)) / (2 * alt_label_error_rate_diff);
+  //  return mellow_log_e_count_over_e_count * rs * rs;
+  const float sqrt_s = (sqrt_mellow_lecoec + std::sqrt(mellow_log_e_count_over_e_count+4*alt_label_error_rate_diff*mellow_log_e_count_over_e_count)) / 2*alt_label_error_rate_diff;
+  //  std::cout << "sqrt_s = " << sqrt_s << std::endl;
+  return sqrt_s*sqrt_s;
 }
 
-float query_decision(const active& a, float ec_revert_weight, float k)
+float query_decision(const active& a, float updates_to_change_prediction, float example_count)
 {
   float bias;
-  if (k <= 1.f) { bias = 1.f; }
+  if (example_count <= 1.f) { bias = 1.f; }
   else
   {
-    const auto weighted_queries = static_cast<float>(a._shared_data->weighted_labeled_examples);
-    const float avg_loss = (static_cast<float>(a._shared_data->sum_loss) / k) +
-        std::sqrt((1.f + 0.5f * std::log(k)) / (weighted_queries + 0.0001f));
-    bias = get_active_coin_bias(k, avg_loss, ec_revert_weight / k, a.active_c0);
+    //    const auto weighted_queries = static_cast<float>(a._shared_data->weighted_labeled_examples);
+    const float avg_loss = (static_cast<float>(a._shared_data->sum_loss) / example_count);
+      //+ std::sqrt((1.f + 0.5f * std::log(example_count)) / (weighted_queries + 0.0001f));  Commented this out, not following why we need it from the theory.
+    //    std::cout << "avg_loss = " << avg_loss << " weighted_queries = " << weighted_queries << " sum_loss = " << a._shared_data->sum_loss << " example_count = " << example_count << std::endl;
+    bias = get_active_coin_bias(example_count, avg_loss, updates_to_change_prediction / example_count, a.active_c0);
   }
-
+  //  std::cout << "bias = " << bias << std::endl;
   return (a._random_state->get_and_update_random() < bias) ? 1.f / bias : -1.f;
 }
 
@@ -110,6 +120,34 @@
   }
 }
 
+template <bool is_learn>
+void predict_or_learn_active_direct(active& a, learner& base, VW::example& ec)
+{ 
+  if (is_learn) { base.learn(ec); }
+  else { base.predict(ec); }
+
+  if (ec.l.simple.label == FLT_MAX)
+  {
+    if (std::string(ec.tag.begin(), ec.tag.begin()+6) == "query?")
+    { 
+      const float threshold = (a._shared_data->max_label + a._shared_data->min_label) * 0.5f;
+      // We want to understand the change in prediction if the label were to be
+      // the opposite of what was predicted. 0 and 1 are used for the expected min
+      // and max labels to be coming in from the active interactor.
+      ec.l.simple.label = (ec.pred.scalar >= threshold) ? a._min_seen_label : a._max_seen_label;
+      ec.confidence = std::abs(ec.pred.scalar - threshold) / base.sensitivity(ec);
+      ec.l.simple.label = FLT_MAX;
+      ec.pred.scalar = query_decision(a, ec.confidence, static_cast<float>(a._shared_data->weighted_unlabeled_examples));
+    }
+  }
+  else
+  { 
+    // Update seen labels based on the current example's label.
+    a._min_seen_label = std::min(ec.l.simple.label, a._min_seen_label);
+    a._max_seen_label = std::max(ec.l.simple.label, a._max_seen_label);
+  } 
+}   
+
 void active_print_result(
     VW::io::writer* f, float res, float weight, const VW::v_array<char>& tag, VW::io::logger& logger)
 {
@@ -189,14 +227,16 @@
 
   bool active_option = false;
   bool simulation = false;
+  bool direct = false;
   float active_c0;
   option_group_definition new_options("[Reduction] Active Learning");
   new_options.add(make_option("active", active_option).keep().necessary().help("Enable active learning"))
       .add(make_option("simulation", simulation).help("Active learning simulation mode"))
+      .add(make_option("direct", direct).help("Active learning via the tag and predictions interface.  Tag should start with \"query?\" to get query decision.  Returned prediction is either -1 for no or the importance weight for yes."))
       .add(make_option("mellowness", active_c0)
                .keep()
-               .default_value(8.f)
-               .help("Active learning mellowness parameter c_0. Default 8"));
+               .default_value(1.f)
+               .help("Active learning mellowness parameter c_0. Default 1."));
 
   if (!options.add_parse_and_check_necessary(new_options)) { return nullptr; }
 
@@ -223,6 +263,15 @@
     print_update_func = VW::details::print_update_simple_label<active>;
     reduction_name.append("-simulation");
   }
+  else if (direct)
+  {
+    learn_func = predict_or_learn_active_direct<true>;
+    pred_func = predict_or_learn_active_direct<false>;
+    update_stats_func = update_stats_active;
+    output_example_prediction_func = VW::details::output_example_prediction_simple_label<active>;
+    print_update_func = VW::details::print_update_simple_label<active>;
+    learn_returns_prediction = base->learn_returns_prediction;
+  }
   else
   {
     all.reduction_state.active = true;