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#include "reductions.h"
#include "multiclass.h"
#include "simple_label.h"
#include "rand48.h"
#include "float.h"
#include "vw.h"
using namespace LEARNER;
namespace ACTIVE {
struct active{
float active_c0;
vw* all;
};
float get_active_coin_bias(float k, float avg_loss, float g, float c0)
{
float b,sb,rs,sl;
b=(float)(c0*(log(k+1.)+0.0001)/(k+0.0001));
sb=sqrt(b);
avg_loss = min(1.f, max(0.f, avg_loss)); //loss should be in [0,1]
sl=sqrt(avg_loss)+sqrt(avg_loss+g);
if (g<=sb*sl+b)
return 1;
rs = (sl+sqrt(sl*sl+4*g))/(2*g);
return b*rs*rs;
}
float query_decision(active& a, example& ec, float k)
{
float bias, avg_loss, weighted_queries;
if (k<=1.)
bias=1.;
else{
weighted_queries = (float)(a.all->initial_t + a.all->sd->weighted_examples - a.all->sd->weighted_unlabeled_examples);
avg_loss = (float)(a.all->sd->sum_loss/k + sqrt((1.+0.5*log(k))/(weighted_queries+0.0001)));
bias = get_active_coin_bias(k, avg_loss, ec.revert_weight/k, a.active_c0);
}
if(frand48() < bias)
return 1.f / bias;
else
return -1.;
}
template <bool is_learn>
void predict_or_learn_simulation(active& a, base_learner& base, example& ec) {
base.predict(ec);
if (is_learn)
{
vw& all = *a.all;
float k = ec.example_t - ec.l.simple.weight;
ec.revert_weight = all.loss->getRevertingWeight(all.sd, ec.pred.scalar, all.eta/powf(k,all.power_t));
float importance = query_decision(a, ec, k);
if(importance > 0){
all.sd->queries += 1;
ec.l.simple.weight *= importance;
base.learn(ec);
}
else
ec.l.simple.label = FLT_MAX;
}
}
template <bool is_learn>
void predict_or_learn_active(active& a, base_learner& base, example& ec) {
if (is_learn)
base.learn(ec);
else
base.predict(ec);
if (ec.l.simple.label == FLT_MAX) {
vw& all = *a.all;
float t = (float)(ec.example_t - all.sd->weighted_holdout_examples);
ec.revert_weight = all.loss->getRevertingWeight(all.sd, ec.pred.scalar,
all.eta/powf(t,all.power_t));
}
}
void active_print_result(int f, float res, float weight, v_array<char> tag)
{
if (f >= 0)
{
std::stringstream ss;
char temp[30];
sprintf(temp, "%f", res);
ss << temp;
if(!print_tag(ss, tag))
ss << ' ';
if(weight >= 0)
{
sprintf(temp, " %f", weight);
ss << temp;
}
ss << '\n';
ssize_t len = ss.str().size();
ssize_t t = io_buf::write_file_or_socket(f, ss.str().c_str(), (unsigned int)len);
if (t != len)
cerr << "write error" << endl;
}
}
void output_and_account_example(vw& all, active& a, example& ec)
{
label_data& ld = ec.l.simple;
if(ec.test_only)
{
all.sd->weighted_holdout_examples += ld.weight;//test weight seen
all.sd->weighted_holdout_examples_since_last_dump += ld.weight;
all.sd->weighted_holdout_examples_since_last_pass += ld.weight;
all.sd->holdout_sum_loss += ec.loss;
all.sd->holdout_sum_loss_since_last_dump += ec.loss;
all.sd->holdout_sum_loss_since_last_pass += ec.loss;//since last pass
}
else
{
if (ld.label != FLT_MAX)
all.sd->weighted_labels += ld.label * ld.weight;
all.sd->weighted_examples += ld.weight;
all.sd->sum_loss += ec.loss;
all.sd->sum_loss_since_last_dump += ec.loss;
all.sd->total_features += ec.num_features;
all.sd->example_number++;
}
all.print(all.raw_prediction, ec.partial_prediction, -1, ec.tag);
float ai=-1;
if(ld.label == FLT_MAX)
ai=query_decision(a, ec, (float)all.sd->weighted_unlabeled_examples);
all.sd->weighted_unlabeled_examples += ld.label == FLT_MAX ? ld.weight : 0;
for (size_t i = 0; i<all.final_prediction_sink.size(); i++)
{
int f = (int)all.final_prediction_sink[i];
active_print_result(f, ec.pred.scalar, ai, ec.tag);
}
print_update(all, ec);
}
void return_active_example(vw& all, active& a, example& ec)
{
output_and_account_example(all, a, ec);
VW::finish_example(all,&ec);
}
base_learner* setup(vw& all, po::variables_map& vm)
{//parse and set arguments
active& data = calloc_or_die<active>();
po::options_description active_opts("Active Learning options");
active_opts.add_options()
("simulation", "active learning simulation mode")
("mellowness", po::value<float>(&(data.active_c0)), "active learning mellowness parameter c_0. Default 8")
;
vm = add_options(all, active_opts);
data.all=&all;
//Create new learner
learner<active>* ret;
if (vm.count("simulation"))
ret = &init_learner(&data, all.l, predict_or_learn_simulation<true>,
predict_or_learn_simulation<false>);
else
{
all.active = true;
ret = &init_learner(&data, all.l, predict_or_learn_active<true>,
predict_or_learn_active<false>);
ret->set_finish_example(return_active_example);
}
return make_base(*ret);
}
}
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