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/*
Copyright (c) by respective owners including Yahoo!, Microsoft, and
individual contributors. All rights reserved. Released under a BSD (revised)
license as described in the file LICENSE.
*/
#include <float.h>
#include <math.h>
#include <stdio.h>
#include <sstream>
#include <numeric>
#include <vector>
#include "io_buf.h"
#include "parse_primitives.h"
#include "example.h"
#include "parse_args.h"
#include "v_hashmap.h"
#include "simple_label.h"
#include "bs.h"
#include "cache.h"
#include "v_hashmap.h"
#include "vw.h"
#include "rand48.h"
using namespace std;
using namespace LEARNER;
namespace BS {
struct bs{
uint32_t B; //number of bootstrap rounds
size_t bs_type;
float lb;
float ub;
vector<double> pred_vec;
vw* all;
};
float weight_gen()//sampling from Poisson with rate 1
{
float temp = frand48();
if(temp<=0.3678794411714423215955) return 0.;
if(temp<=0.735758882342884643191) return 1.;
if(temp<=0.919698602928605803989) return 2.;
if(temp<=0.9810118431238461909214) return 3.;
if(temp<=0.9963401531726562876545) return 4.;
if(temp<=0.9994058151824183070012) return 5.;
if(temp<=0.9999167588507119768923) return 6.;
if(temp<=0.9999897508033253583053) return 7.;
if(temp<=0.9999988747974020309819) return 8.;
if(temp<=0.9999998885745216612793) return 9.;
if(temp<=0.9999999899522336243091) return 10.;
if(temp<=0.9999999991683892573118) return 11.;
if(temp<=0.9999999999364022267287) return 12;
if(temp<=0.999999999995480147453) return 13.;
if(temp<=0.9999999999996999989333) return 14.;
if(temp<=0.9999999999999813223654) return 15.;
if(temp<=0.9999999999999989050799) return 16.;
if(temp<=0.9999999999999999393572) return 17.;
if(temp<=0.999999999999999996817) return 18.;
if(temp<=0.9999999999999999998412) return 19.;
return 20.;
}
void bs_predict_mean(vw& all, example* ec, vector<double> &pred_vec)
{
ec->final_prediction = (float)accumulate(pred_vec.begin(), pred_vec.end(), 0.0)/pred_vec.size();
ec->loss = all.loss->getLoss(all.sd, ec->final_prediction, ((label_data*)ec->ld)->label) * ((label_data*)ec->ld)->weight;
}
void bs_predict_vote(vw& all, example* ec, vector<double> &pred_vec)
{ //majority vote in linear time
unsigned int counter = 0;
float current_label = 1.;
for(unsigned int i=0; i<pred_vec.size(); i++)
{
if(pred_vec[i] == current_label)
counter++;
else
{
if (counter == 0)
{
counter = 1;
current_label = (float)pred_vec[i];
}
else
counter--;
}
if(counter==0)
current_label = -1;
}
if(counter == 0)//no majority exists
{
ec->final_prediction = -1;
ec->loss = 1.;
return;
}
//will output majority if it exists
ec->final_prediction = current_label;
if (ec->final_prediction == ((label_data*)ec->ld)->label)
ec->loss = 0.;
else
ec->loss = 1.;
}
void print_result(int f, float res, float weight, v_array<char> tag, float lb, float ub)
{
if (f >= 0)
{
char temp[30];
sprintf(temp, "%f", res);
std::stringstream ss;
ss << temp;
print_tag(ss, tag);
ss << ' ';
sprintf(temp, "%f", lb);
ss << temp;
ss << ' ';
sprintf(temp, "%f", ub);
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_example(vw& all, bs* d, example* ec)
{
label_data* ld = (label_data*)ec->ld;
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
{
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++;
}
if(all.final_prediction_sink.size() != 0)//get confidence interval only when printing out predictions
{
d->lb = FLT_MAX;
d->ub = -FLT_MAX;
for (unsigned i = 0; i < d->pred_vec.size(); i++)
{
if(d->pred_vec[i] > d->ub)
d->ub = (float)d->pred_vec[i];
if(d->pred_vec[i] < d->lb)
d->lb = (float)d->pred_vec[i];
}
}
for (int* sink = all.final_prediction_sink.begin; sink != all.final_prediction_sink.end; sink++)
BS::print_result(*sink, ec->final_prediction, 0, ec->tag, d->lb, d->ub);
print_update(all, ec);
}
template <bool is_learn>
void predict_or_learn(bs* d, learner& base, example* ec)
{
vw* all = d->all;
bool shouldOutput = all->raw_prediction > 0;
float weight_temp = ((label_data*)ec->ld)->weight;
string outputString;
stringstream outputStringStream(outputString);
d->pred_vec.clear();
for (size_t i = 1; i <= d->B; i++)
{
((label_data*)ec->ld)->weight = weight_temp * weight_gen();
if (is_learn)
base.learn(ec, i-1);
else
base.predict(ec, i-1);
d->pred_vec.push_back(ec->final_prediction);
if (shouldOutput) {
if (i > 1) outputStringStream << ' ';
outputStringStream << i << ':' << ec->partial_prediction;
}
}
((label_data*)ec->ld)->weight = weight_temp;
switch(d->bs_type)
{
case BS_TYPE_MEAN:
bs_predict_mean(*all, ec, d->pred_vec);
break;
case BS_TYPE_VOTE:
bs_predict_vote(*all, ec, d->pred_vec);
break;
default:
std::cerr << "Unknown bs_type specified: " << d->bs_type << ". Exiting." << endl;
throw exception();
}
if (shouldOutput)
all->print_text(all->raw_prediction, outputStringStream.str(), ec->tag);
}
void finish_example(vw& all, bs* d, example* ec)
{
BS::output_example(all, d, ec);
VW::finish_example(all, ec);
}
void finish(bs* d)
{
d->pred_vec.~vector();
}
learner* setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
{
bs* data = (bs*)calloc(1, sizeof(bs));
data->ub = FLT_MAX;
data->lb = -FLT_MAX;
po::options_description desc("BS options");
desc.add_options()
("bs_type", po::value<string>(), "prediction type {mean,vote}");
po::parsed_options parsed = po::command_line_parser(opts).
style(po::command_line_style::default_style ^ po::command_line_style::allow_guessing).
options(desc).allow_unregistered().run();
opts = po::collect_unrecognized(parsed.options, po::include_positional);
po::store(parsed, vm);
po::notify(vm);
po::parsed_options parsed_file = po::command_line_parser(all.options_from_file_argc,all.options_from_file_argv).
style(po::command_line_style::default_style ^ po::command_line_style::allow_guessing).
options(desc).allow_unregistered().run();
po::store(parsed_file, vm_file);
po::notify(vm_file);
if( vm_file.count("bs") ) {
data->B = (uint32_t)vm_file["bs"].as<size_t>();
if( vm.count("bs") && (uint32_t)vm["bs"].as<size_t>() != data->B )
std::cerr << "warning: you specified a different number of samples through --bs than the one loaded from predictor. Pursuing with loaded value of: " << data->B << endl;
}
else {
data->B = (uint32_t)vm["bs"].as<size_t>();
//append bs with number of samples to options_from_file so it is saved to regressor later
std::stringstream ss;
ss << " --bs " << data->B;
all.options_from_file.append(ss.str());
}
if (vm.count("bs_type") || vm_file.count("bs_type"))
{
std::string type_string;
if(vm_file.count("bs_type")) {
type_string = vm_file["bs_type"].as<std::string>();
if( vm.count("bs_type") && type_string.compare(vm["bs_type"].as<string>()) != 0)
cerr << "You specified a different --bs_type than the one loaded from regressor file. Pursuing with loaded value of: " << type_string << endl;
}
else {
type_string = vm["bs_type"].as<std::string>();
all.options_from_file.append(" --bs_type ");
all.options_from_file.append(type_string);
}
if (type_string.compare("mean") == 0) {
data->bs_type = BS_TYPE_MEAN;
}
else if (type_string.compare("vote") == 0) {
data->bs_type = BS_TYPE_VOTE;
}
else {
std::cerr << "warning: bs_type must be in {'mean','vote'}; resetting to mean." << std::endl;
data->bs_type = BS_TYPE_MEAN;
}
}
else {
//by default use mean
data->bs_type = BS_TYPE_MEAN;
all.options_from_file.append(" --bs_type mean");
}
data->pred_vec.reserve(data->B);
data->all = &all;
learner* l = new learner(data, all.l, data->B);
l->set_learn<bs, predict_or_learn<true> >();
l->set_predict<bs, predict_or_learn<false> >();
l->set_finish_example<bs,finish_example>();
l->set_finish<bs,finish>();
return l;
}
}
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