style: forgotten lint fix (#4688)

* lint

* undo fake commit

* undo fake commit

* forgotten space

Author: ataymano

vowpalwabbit/core/src/reductions/active.cc

@@ -32,25 +32,37 @@ using namespace VW::reductions;
 namespace
 {
 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);
+{  // 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 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;
+  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;
+  return sqrt_s * sqrt_s;
 }
 
 float query_decision(const active& a, float updates_to_change_prediction, float example_count)
@@ -61,8 +73,10 @@ float query_decision(const active& a, float updates_to_change_prediction, float
   {
     //    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;
+    //+ 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;
@@ -122,31 +136,32 @@ void predict_or_learn_active(active& a, learner& base, VW::example& ec)
 
 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?")
-    { 
+    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));
+      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)
@@ -232,7 +247,9 @@ std::shared_ptr<VW::LEARNER::learner> VW::reductions::active_setup(VW::setup_bas
   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("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(1.f)