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-rw-r--r--vowpalwabbit/cb.cc110
1 files changed, 55 insertions, 55 deletions
diff --git a/vowpalwabbit/cb.cc b/vowpalwabbit/cb.cc
index 9d2437ac..a3214ba1 100644
--- a/vowpalwabbit/cb.cc
+++ b/vowpalwabbit/cb.cc
@@ -223,9 +223,9 @@ namespace CB
return NULL;
}
- void gen_cs_example_ips(vw& all, cb& c, example* ec, CSOAA::label& cs_ld)
+ void gen_cs_example_ips(vw& all, cb& c, example& ec, CSOAA::label& cs_ld)
{//this implements the inverse propensity score method, where cost are importance weighted by the probability of the chosen action
- CB::label* ld = (CB::label*)ec->ld;
+ CB::label* ld = (CB::label*)ec.ld;
//generate cost-sensitive example
cs_ld.costs.erase();
@@ -282,7 +282,7 @@ namespace CB
}
template <bool is_learn>
- void call_scorer(vw& all, cb& c, example* ec, uint32_t index)
+ void call_scorer(vw& all, cb& c, example& ec, uint32_t index)
{
float old_min = all.sd->min_label;
//all.sd->min_label = c.min_cost;
@@ -297,9 +297,9 @@ namespace CB
}
template <bool is_learn>
- float get_cost_pred(vw& all, cb& c, example* ec, uint32_t index)
+ float get_cost_pred(vw& all, cb& c, example& ec, uint32_t index)
{
- CB::label* ld = (CB::label*)ec->ld;
+ CB::label* ld = (CB::label*)ec.ld;
label_data simple_temp;
simple_temp.initial = 0.;
@@ -314,21 +314,21 @@ namespace CB
simple_temp.weight = 0.;
}
- ec->ld = &simple_temp;
+ ec.ld = &simple_temp;
call_scorer<is_learn>(all, c, ec, index);
- ec->ld = ld;
+ ec.ld = ld;
- float cost = ec->final_prediction;
+ float cost = ec.final_prediction;
return cost;
}
template <bool is_learn>
- void gen_cs_example_dm(vw& all, cb& c, example* ec, CSOAA::label& cs_ld)
+ void gen_cs_example_dm(vw& all, cb& c, example& ec, CSOAA::label& cs_ld)
{
//this implements the direct estimation method, where costs are directly specified by the learned regressor.
- CB::label* ld = (CB::label*)ec->ld;
+ CB::label* ld = (CB::label*)ec.ld;
float min = FLT_MAX;
size_t argmin = 1;
@@ -393,13 +393,13 @@ namespace CB
}
}
- ec->final_prediction = (float)argmin;
+ ec.final_prediction = (float)argmin;
}
template <bool is_learn>
- void gen_cs_example_dr(vw& all, cb& c, example* ec, CSOAA::label& cs_ld)
+ void gen_cs_example_dr(vw& all, cb& c, example& ec, CSOAA::label& cs_ld)
{//this implements the doubly robust method
- CB::label* ld = (CB::label*)ec->ld;
+ CB::label* ld = (CB::label*)ec.ld;
//generate cost sensitive example
cs_ld.costs.erase();
@@ -455,9 +455,9 @@ namespace CB
}
}
- void cb_test_to_cs_test_label(vw& all, example* ec, CSOAA::label& cs_ld)
+ void cb_test_to_cs_test_label(vw& all, example& ec, CSOAA::label& cs_ld)
{
- CB::label* ld = (CB::label*)ec->ld;
+ CB::label* ld = (CB::label*)ec.ld;
cs_ld.costs.erase();
if(ld->costs.size() > 0)
@@ -479,67 +479,67 @@ namespace CB
}
template <bool is_learn>
- void predict_or_learn(cb* c, learner& base, example* ec) {
- vw* all = c->all;
- CB::label* ld = (CB::label*)ec->ld;
+ void predict_or_learn(cb& c, learner& base, example& ec) {
+ vw* all = c.all;
+ CB::label* ld = (CB::label*)ec.ld;
//check if this is a test example where we just want a prediction
if( CB::is_test_label(ld) )
{
//if so just query base cost-sensitive learner
- cb_test_to_cs_test_label(*all,ec,c->cb_cs_ld);
+ cb_test_to_cs_test_label(*all,ec,c.cb_cs_ld);
- ec->ld = &c->cb_cs_ld;
+ ec.ld = &c.cb_cs_ld;
base.predict(ec);
- ec->ld = ld;
+ ec.ld = ld;
for (size_t i=0; i<ld->costs.size(); i++)
- ld->costs[i].partial_prediction = c->cb_cs_ld.costs[i].partial_prediction;
+ ld->costs[i].partial_prediction = c.cb_cs_ld.costs[i].partial_prediction;
return;
}
//now this is a training example
- c->known_cost = get_observed_cost(ld);
- c->min_cost = min (c->min_cost, c->known_cost->cost);
- c->max_cost = max (c->max_cost, c->known_cost->cost);
+ c.known_cost = get_observed_cost(ld);
+ c.min_cost = min (c.min_cost, c.known_cost->cost);
+ c.max_cost = max (c.max_cost, c.known_cost->cost);
//generate a cost-sensitive example to update classifiers
- switch(c->cb_type)
+ switch(c.cb_type)
{
case CB_TYPE_IPS:
- gen_cs_example_ips(*all,*c,ec,c->cb_cs_ld);
+ gen_cs_example_ips(*all,c,ec,c.cb_cs_ld);
break;
case CB_TYPE_DM:
- gen_cs_example_dm<is_learn>(*all,*c,ec,c->cb_cs_ld);
+ gen_cs_example_dm<is_learn>(*all,c,ec,c.cb_cs_ld);
break;
case CB_TYPE_DR:
- gen_cs_example_dr<is_learn>(*all,*c,ec,c->cb_cs_ld);
+ gen_cs_example_dr<is_learn>(*all,c,ec,c.cb_cs_ld);
break;
default:
- std::cerr << "Unknown cb_type specified for contextual bandit learning: " << c->cb_type << ". Exiting." << endl;
+ std::cerr << "Unknown cb_type specified for contextual bandit learning: " << c.cb_type << ". Exiting." << endl;
throw exception();
}
- if (c->cb_type != CB_TYPE_DM)
+ if (c.cb_type != CB_TYPE_DM)
{
- ec->ld = &c->cb_cs_ld;
+ ec.ld = &c.cb_cs_ld;
if (is_learn)
base.learn(ec);
else
base.predict(ec);
- ec->ld = ld;
+ ec.ld = ld;
for (size_t i=0; i<ld->costs.size(); i++)
- ld->costs[i].partial_prediction = c->cb_cs_ld.costs[i].partial_prediction;
+ ld->costs[i].partial_prediction = c.cb_cs_ld.costs[i].partial_prediction;
}
}
- void init_driver(cb*)
+ void init_driver(cb&)
{
fprintf(stderr, "*estimate* *estimate* avglossreg last pred last correct\n");
}
- void print_update(vw& all, cb& c, bool is_test, example *ec)
+ void print_update(vw& all, cb& c, bool is_test, example& ec)
{
if (all.sd->weighted_examples >= all.sd->dump_interval && !all.quiet && !all.bfgs)
{
@@ -565,8 +565,8 @@ namespace CB
(long int)all.sd->example_number,
all.sd->weighted_examples,
label_buf,
- (long unsigned int)ec->final_prediction,
- (long unsigned int)ec->num_features,
+ (long unsigned int)ec.final_prediction,
+ (long unsigned int)ec.num_features,
c.avg_loss_regressors,
c.last_pred_reg,
c.last_correct_cost);
@@ -581,8 +581,8 @@ namespace CB
(long int)all.sd->example_number,
all.sd->weighted_examples,
label_buf,
- (long unsigned int)ec->final_prediction,
- (long unsigned int)ec->num_features,
+ (long unsigned int)ec.final_prediction,
+ (long unsigned int)ec.num_features,
c.avg_loss_regressors,
c.last_pred_reg,
c.last_correct_cost);
@@ -593,14 +593,14 @@ namespace CB
}
}
- void output_example(vw& all, cb& c, example* ec)
+ void output_example(vw& all, cb& c, example& ec)
{
- CB::label* ld = (CB::label*)ec->ld;
+ CB::label* ld = (CB::label*)ec.ld;
float loss = 0.;
if (!CB::is_test_label(ld))
{//need to compute exact loss
- size_t pred = (size_t)ec->final_prediction;
+ size_t pred = (size_t)ec.final_prediction;
float chosen_loss = FLT_MAX;
if( know_all_cost_example(ld) ) {
@@ -626,11 +626,11 @@ namespace CB
loss = chosen_loss;
}
- if(ec->test_only)
+ if(ec.test_only)
{
- all.sd->weighted_holdout_examples += ec->global_weight;//test weight seen
- all.sd->weighted_holdout_examples_since_last_dump += ec->global_weight;
- all.sd->weighted_holdout_examples_since_last_pass += ec->global_weight;
+ all.sd->weighted_holdout_examples += ec.global_weight;//test weight seen
+ all.sd->weighted_holdout_examples_since_last_dump += ec.global_weight;
+ all.sd->weighted_holdout_examples_since_last_pass += ec.global_weight;
all.sd->holdout_sum_loss += loss;
all.sd->holdout_sum_loss_since_last_dump += loss;
all.sd->holdout_sum_loss_since_last_pass += loss;//since last pass
@@ -640,30 +640,30 @@ namespace CB
all.sd->sum_loss += loss;
all.sd->sum_loss_since_last_dump += loss;
all.sd->weighted_examples += 1.;
- all.sd->total_features += ec->num_features;
+ all.sd->total_features += ec.num_features;
all.sd->example_number++;
}
for (size_t i = 0; i<all.final_prediction_sink.size(); i++)
{
int f = all.final_prediction_sink[i];
- all.print(f, ec->final_prediction, 0, ec->tag);
+ all.print(f, ec.final_prediction, 0, ec.tag);
}
- print_update(all, c, CB::is_test_label((CB::label*)ec->ld), ec);
+ print_update(all, c, CB::is_test_label((CB::label*)ec.ld), ec);
}
- void finish(cb* c)
+ void finish(cb& c)
{
- c->cb_cs_ld.costs.delete_v();
+ c.cb_cs_ld.costs.delete_v();
}
- void finish_example(vw& all, cb* c, example* ec)
+ void finish_example(vw& all, cb& c, example& ec)
{
- output_example(all, *c, ec);
- VW::finish_example(all, ec);
+ output_example(all, c, ec);
+ VW::finish_example(all, &ec);
}
learner* setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
generated by cgit v1.2.3 (git 2.25.1) at 2024-08-31 21:14:08 +0300