Merge branch 'master' into disable_windows_only_cmake_build

ext_libs/ext_libs.cmake

@@ -38,7 +38,7 @@ if(RAPIDJSON_SYS_DEP)
   # Since EXACT is not specified, any version compatible with 1.1.0 is accepted (>= 1.1.0)
   find_package(RapidJSON 1.1.0 CONFIG REQUIRED)
   add_library(RapidJSON INTERFACE)
-  target_include_directories(RapidJSON INTERFACE ${RapidJSON_INCLUDE_DIRS})
+  target_include_directories(RapidJSON INTERFACE ${RapidJSON_INCLUDE_DIRS} ${RAPIDJSON_INCLUDE_DIRS})
 else()
   add_library(RapidJSON INTERFACE)
   target_include_directories(RapidJSON SYSTEM INTERFACE "${CMAKE_CURRENT_LIST_DIR}/rapidjson/include")
@@ -127,4 +127,4 @@ if(VW_FEAT_CB_GRAPH_FEEDBACK)
   target_include_directories(mlpack_ensmallen SYSTEM INTERFACE ${CMAKE_CURRENT_LIST_DIR}/armadillo-code/include)
 
   target_include_directories(mlpack_ensmallen SYSTEM INTERFACE ${CMAKE_CURRENT_LIST_DIR}/ensmallen/include)
-endif()
+endif()

python/docs/source/tutorials/cmd_first_steps.md

@@ -116,6 +116,6 @@ The model predicted a value of **0**. This result means our house will not need
 ## More to explore
 
 - See [Python tutorial](python_first_steps.ipynb) for a quick introduction to the basics of training and testing your model.
-- To learn more about how to approach a contextual bandits problem using  tVowpal Wabbit — including how to  work with different contextual bandits approaches, how to format data, and understand the results — see the [Contextual Bandit Reinforcement Learning Tutorial](python_Contextual_bandits_and_Vowpal_Wabbit.ipynb).
+- To learn more about how to approach a contextual bandits problem using Vowpal Wabbit — including how to work with different contextual bandits approaches, how to format data, and understand the results — see the [Contextual Bandit Reinforcement Learning Tutorial](python_Contextual_bandits_and_Vowpal_Wabbit.ipynb).
 - For more on the contextual bandits approach to reinforcement learning, including a content personalization scenario, see the [Contextual Bandit Simulation Tutorial](python_Simulating_a_news_personalization_scenario_using_Contextual_Bandits.ipynb).
 - See the [Linear Regression Tutorial](cmd_linear_regression.md) for a different look at the roof replacement problem and learn more about Vowpal Wabbit's format and understanding the results.

test/core.vwtest.json

@@ -6073,5 +6073,34 @@
     "depends_on": [
       467
     ]
+  },
+  {
+    "id": 469,
+    "desc": "https://github.com/VowpalWabbit/vowpal_wabbit/issues/4669",
+    "vw_command": "--ccb_explore_adf --dsjson -d train-sets/issue4669.dsjson -f issue4669.model",
+    "diff_files": {
+      "stderr": "train-sets/ref/issue4669_train.stderr",
+      "stdout": "train-sets/ref/issue4669_train.stdout"
+    },
+    "input_files": [
+      "train-sets/issue4669.dsjson"
+    ]
+  },
+  {
+    "id": 470,
+    "desc": "https://github.com/VowpalWabbit/vowpal_wabbit/issues/4669",
+    "vw_command": "--ccb_explore_adf --dsjson --all_slots_loss --epsilon 0 -t -i issue4669.model -t -d train-sets/issue4669.dsjson -p issue4669_test_pred.txt",
+    "diff_files": {
+      "stderr": "train-sets/ref/issue4669_test.stderr",
+      "stdout": "train-sets/ref/issue4669_test.stdout",
+      "issue4669_test_pred.txt": "train-sets/ref/issue4669_test_pred.txt"
+    },
+    "input_files": [
+      "train-sets/issue4669.dsjson",
+      "issue4669.model"
+    ],
+    "depends_on": [
+      469
+    ]
   }
 ]

test/train-sets/issue4669.dsjson

@@ -0,0 +1 @@
+{"c":{"_multi":[{"f":"1"},{"f":"2"}],"_slots":[{"_inc":[0,1]},{"_inc":[1]}]},"_outcomes":[{"_label_cost":1.0,"_a":[0,1],"_p":[0.5,0.5]},{"_label_cost":0.0,"_a":[1],"_p":[1]}]}

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:

test/train-sets/ref/issue4669_test.stderr

@@ -0,0 +1,23 @@
+only testing
+predictions = issue4669_test_pred.txt
+using no cache
+Reading datafile = train-sets/issue4669.dsjson
+num sources = 1
+Num weight bits = 18
+learning rate = 0.5
+initial_t = 1
+power_t = 0.5
+cb_type = mtr
+Enabled learners: gd, generate_interactions, scorer-identity, csoaa_ldf-rank, cb_adf, cb_explore_adf_greedy, cb_sample, shared_feature_merger, ccb_explore_adf
+Input label = CCB
+Output pred = DECISION_PROBS
+average  since         example        example        current        current  current
+loss     last          counter         weight          label        predict features
+0.000000 0.000000            1            1.0        0:1,1:0         1,None        9
+
+finished run
+number of examples = 1
+weighted example sum = 1.000000
+weighted label sum = 0.000000
+average loss = 0.000000
+total feature number = 9

test/train-sets/ref/issue4669_test_pred.txt

@@ -0,0 +1,3 @@
+1:1,0:0
+
+

test/train-sets/ref/issue4669_train.stderr

@@ -0,0 +1,22 @@
+final_regressor = issue4669.model
+using no cache
+Reading datafile = train-sets/issue4669.dsjson
+num sources = 1
+Num weight bits = 18
+learning rate = 0.5
+initial_t = 0
+power_t = 0.5
+cb_type = mtr
+Enabled learners: gd, generate_interactions, scorer-identity, csoaa_ldf-rank, cb_adf, cb_explore_adf_greedy, cb_sample, shared_feature_merger, ccb_explore_adf
+Input label = CCB
+Output pred = DECISION_PROBS
+average  since         example        example        current        current  current
+loss     last          counter         weight          label        predict features
+1.000000 1.000000            1            1.0        0:1,1:0            0,1       12
+
+finished run
+number of examples = 1
+weighted example sum = 1.000000
+weighted label sum = 0.000000
+average loss = 1.000000
+total feature number = 12

vowpalwabbit/core/include/vw/core/error_data.h

@@ -25,6 +25,9 @@ ERROR_CODE_DEFINITION(
 ERROR_CODE_DEFINITION(
     13, fb_parser_size_mismatch_ft_names_ft_values, "Size of feature names and feature values do not match. ")
 ERROR_CODE_DEFINITION(14, unknown_label_type, "Label type in Flatbuffer not understood. ")
+ERROR_CODE_DEFINITION(15, fb_parser_span_misaligned, "Input Flatbuffer span is not aligned to an 8-byte boundary. ")
+ERROR_CODE_DEFINITION(
+    16, fb_parser_span_length_mismatch, "Input Flatbuffer span does not match flatbuffer size prefix. ")
 
 // TODO: This is temporary until we switch to the new error handling mechanism.
 ERROR_CODE_DEFINITION(10000, vw_exception, "vw_exception: ")

vowpalwabbit/core/src/decision_scores.cc

@@ -26,7 +26,8 @@ void print_update(VW::workspace& all, const VW::multi_ex& slots, const VW::decis
   std::string delim;
   for (const auto& slot : decision_scores)
   {
-    pred_ss << delim << slot[0].action;
+    if (slot.empty()) { pred_ss << delim << "None"; }
+    else { pred_ss << delim << slot[0].action; }
     delim = ",";
   }
   all.sd->print_update(*all.output_runtime.trace_message, all.passes_config.holdout_set_off,

vowpalwabbit/core/src/reductions/active.cc

@@ -31,31 +31,55 @@ using namespace VW::config;
 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 +134,35 @@ 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?")
+    {
+      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 +242,18 @@ std::shared_ptr<VW::LEARNER::learner> VW::reductions::active_setup(VW::setup_bas
 
   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 +280,15 @@ std::shared_ptr<VW::LEARNER::learner> VW::reductions::active_setup(VW::setup_bas
     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;

vowpalwabbit/core/src/reductions/conditional_contextual_bandit.cc

@@ -5,6 +5,7 @@
 #include "vw/core/reductions/conditional_contextual_bandit.h"
 
 #include "vw/config/options.h"
+#include "vw/core/cb.h"
 #include "vw/core/ccb_label.h"
 #include "vw/core/ccb_reduction_features.h"
 #include "vw/core/constant.h"
@@ -213,8 +214,12 @@ void clear_pred_and_label(ccb_data& data)
   data.actions[data.action_with_label]->l.cb.costs.clear();
 }
 
-// true if there exists at least 1 action in the cb multi-example
-bool has_action(VW::multi_ex& cb_ex) { return !cb_ex.empty(); }
+// true if there exists at least 2 examples (since there can only be up to 1
+// shared example), or the 0th example is not shared.
+bool has_action(VW::multi_ex& cb_ex)
+{
+  return cb_ex.size() > 1 || (!cb_ex.empty() && !VW::ec_is_example_header_cb(*cb_ex[0]));
+}
 
 // This function intentionally does not handle increasing the num_features of the example because
 // the output_example function has special logic to ensure the number of features is correctly calculated.
@@ -309,7 +314,11 @@ void build_cb_example(VW::multi_ex& cb_ex, VW::example* slot, const VW::ccb_labe
     // First time seeing this, initialize the vector with falses so we can start setting each included action.
     if (data.include_list.empty()) { data.include_list.assign(data.actions.size(), false); }
 
-    for (uint32_t included_action_id : explicit_includes) { data.include_list[included_action_id] = true; }
+    for (uint32_t included_action_id : explicit_includes)
+    {
+      // The action may be included but not actually exist in the list of possible actions.
+      if (included_action_id < data.actions.size()) { data.include_list[included_action_id] = true; }
+    }
   }
 
   // set the available actions in the cb multi-example
@@ -545,6 +554,9 @@ void update_stats_ccb(const VW::workspace& /* all */, shared_data& sd, const ccb
       if (outcome != nullptr)
       {
         num_labeled++;
+        // It is possible for the prediction to be empty if there were no actions available at the time of taking the
+        // slot decision. In this case it does not contribute to loss.
+        if (preds[i].empty()) { continue; }
         if (i == 0 || data.all_slots_loss_report)
         {
           const float l = VW::get_cost_estimate(outcome->probabilities[VW::details::TOP_ACTION_INDEX], outcome->cost,