1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
|
/*
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 <queue>
#include "topk.h"
#include "cache.h"
#include "v_hashmap.h"
#include "vw.h"
using namespace std;
using namespace LEARNER;
typedef pair<float, v_array<char> > scored_example;
struct compare_scored_examples
{
bool operator()(scored_example const& a, scored_example const& b) const
{
return a.first > b.first;
}
};
namespace TOPK {
struct topk{
uint32_t B; //rec number
priority_queue<scored_example, vector<scored_example>, compare_scored_examples > pr_queue;
vw* all;
};
void print_result(int f, priority_queue<scored_example, vector<scored_example>, compare_scored_examples > &pr_queue)
{
if (f >= 0)
{
char temp[30];
std::stringstream ss;
scored_example tmp_example;
while(!pr_queue.empty())
{
tmp_example = pr_queue.top();
pr_queue.pop();
sprintf(temp, "%f", tmp_example.first);
ss << temp;
ss << ' ';
print_tag(ss, tmp_example.second);
ss << ' ';
ss << '\n';
}
ss << '\n';
ssize_t len = ss.str().size();
#ifdef _WIN32
ssize_t t = _write(f, ss.str().c_str(), (unsigned int)len);
#else
ssize_t t = write(f, ss.str().c_str(), (unsigned int)len);
#endif
if (t != len)
cerr << "write error" << endl;
}
}
void output_example(vw& all, topk* d, example* ec)
{
label_data* ld = (label_data*)ec->ld;
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 (example_is_newline(ec))
for (int* sink = all.final_prediction_sink.begin; sink != all.final_prediction_sink.end; sink++)
TOPK::print_result(*sink, d->pr_queue);
print_update(all, ec);
}
template <bool is_learn>
void predict_or_learn(topk* d, learner& base, example* ec)
{
if (example_is_newline(ec)) return;//do not predict newline
if (is_learn)
base.learn(ec);
else
base.predict(ec);
if(d->pr_queue.size() < d->B)
d->pr_queue.push(make_pair(ec->final_prediction, ec->tag));
else if(d->pr_queue.top().first < ec->final_prediction)
{
d->pr_queue.pop();
d->pr_queue.push(make_pair(ec->final_prediction, ec->tag));
}
}
void finish_example(vw& all, topk* d, example* ec)
{
TOPK::output_example(all, d, ec);
VW::finish_example(all, ec);
}
learner* setup(vw& all, std::vector<std::string>&opts, po::variables_map& vm, po::variables_map& vm_file)
{
topk* data = (topk*)calloc(1, sizeof(topk));
data->B = (uint32_t)vm["top"].as<size_t>();
data->all = &all;
learner* l = new learner(data, all.l);
l->set_learn<topk, predict_or_learn<true> >();
l->set_predict<topk, predict_or_learn<false> >();
l->set_finish_example<topk,finish_example>();
return l;
}
}
|