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
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
|
#include "arc_factored.h"
#include <vector>
#include <iostream>
#include <boost/program_options.hpp>
#include <boost/program_options/variables_map.hpp>
#include "arc_ff.h"
#include "arc_ff_factory.h"
#include "stringlib.h"
#include "filelib.h"
#include "tdict.h"
#include "picojson.h"
#include "optimize.h"
#include "weights.h"
#include "rst.h"
using namespace std;
namespace po = boost::program_options;
void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
po::options_description opts("Configuration options");
string cfg_file;
opts.add_options()
("training_data,t",po::value<string>()->default_value("-"), "File containing training data (jsent format)")
("feature_function,F",po::value<vector<string> >()->composing(), "feature function")
("regularization_strength,C",po::value<double>()->default_value(1.0), "Regularization strength")
("correction_buffers,m", po::value<int>()->default_value(10), "LBFGS correction buffers");
po::options_description clo("Command line options");
clo.add_options()
("config,c", po::value<string>(&cfg_file), "Configuration file")
("help,?", "Print this help message and exit");
po::options_description dconfig_options, dcmdline_options;
dconfig_options.add(opts);
dcmdline_options.add(dconfig_options).add(clo);
po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
if (cfg_file.size() > 0) {
ReadFile rf(cfg_file);
po::store(po::parse_config_file(*rf.stream(), dconfig_options), *conf);
}
if (conf->count("help")) {
cerr << dcmdline_options << endl;
exit(1);
}
}
struct TrainingInstance {
TaggedSentence ts;
EdgeSubset tree;
SparseVector<weight_t> features;
};
void ReadTraining(const string& fname, vector<TrainingInstance>* corpus, int rank = 0, int size = 1) {
ReadFile rf(fname);
istream& in = *rf.stream();
string line;
string err;
int lc = 0;
bool flag = false;
while(getline(in, line)) {
++lc;
if ((lc-1) % size != rank) continue;
if (rank == 0 && lc % 10 == 0) { cerr << '.' << flush; flag = true; }
if (rank == 0 && lc % 400 == 0) { cerr << " [" << lc << "]\n"; flag = false; }
size_t pos = line.rfind('\t');
assert(pos != string::npos);
picojson::value obj;
picojson::parse(obj, line.begin() + pos, line.end(), &err);
if (err.size() > 0) { cerr << "JSON parse error in " << lc << ": " << err << endl; abort(); }
corpus->push_back(TrainingInstance());
TrainingInstance& cur = corpus->back();
TaggedSentence& ts = cur.ts;
EdgeSubset& tree = cur.tree;
assert(obj.is<picojson::object>());
const picojson::object& d = obj.get<picojson::object>();
const picojson::array& ta = d.find("tokens")->second.get<picojson::array>();
for (unsigned i = 0; i < ta.size(); ++i) {
ts.words.push_back(TD::Convert(ta[i].get<picojson::array>()[0].get<string>()));
ts.pos.push_back(TD::Convert(ta[i].get<picojson::array>()[1].get<string>()));
}
const picojson::array& da = d.find("deps")->second.get<picojson::array>();
for (unsigned i = 0; i < da.size(); ++i) {
const picojson::array& thm = da[i].get<picojson::array>();
// get dep type here
short h = thm[2].get<double>();
short m = thm[1].get<double>();
if (h < 0)
tree.roots.push_back(m);
else
tree.h_m_pairs.push_back(make_pair(h,m));
}
//cerr << TD::GetString(ts.words) << endl << TD::GetString(ts.pos) << endl << tree << endl;
}
if (flag) cerr << "\nRead " << lc << " training instances\n";
}
void AddFeatures(double prob, const SparseVector<double>& fmap, vector<double>* g) {
for (SparseVector<double>::const_iterator it = fmap.begin(); it != fmap.end(); ++it)
(*g)[it->first] += it->second * prob;
}
double ApplyRegularizationTerms(const double C,
const vector<double>& weights,
vector<double>* g) {
assert(weights.size() == g->size());
double reg = 0;
for (size_t i = 0; i < weights.size(); ++i) {
// const double prev_w_i = (i < prev_weights.size() ? prev_weights[i] : 0.0);
const double& w_i = weights[i];
double& g_i = (*g)[i];
reg += C * w_i * w_i;
g_i += 2 * C * w_i;
// reg += T * (w_i - prev_w_i) * (w_i - prev_w_i);
// g_i += 2 * T * (w_i - prev_w_i);
}
return reg;
}
int main(int argc, char** argv) {
int rank = 0;
int size = 1;
po::variables_map conf;
InitCommandLine(argc, argv, &conf);
ArcFactoredForest af(5);
ArcFFRegistry reg;
reg.Register("DistancePenalty", new ArcFFFactory<DistancePenalty>);
vector<TrainingInstance> corpus;
vector<boost::shared_ptr<ArcFeatureFunction> > ffs;
ffs.push_back(boost::shared_ptr<ArcFeatureFunction>(new DistancePenalty("")));
ReadTraining(conf["training_data"].as<string>(), &corpus, rank, size);
vector<ArcFactoredForest> forests(corpus.size());
SparseVector<double> empirical;
bool flag = false;
for (int i = 0; i < corpus.size(); ++i) {
TrainingInstance& cur = corpus[i];
if (rank == 0 && (i+1) % 10 == 0) { cerr << '.' << flush; flag = true; }
if (rank == 0 && (i+1) % 400 == 0) { cerr << " [" << (i+1) << "]\n"; flag = false; }
for (int fi = 0; fi < ffs.size(); ++fi) {
ArcFeatureFunction& ff = *ffs[fi];
ff.PrepareForInput(cur.ts);
SparseVector<weight_t> efmap;
for (int j = 0; j < cur.tree.h_m_pairs.size(); ++j) {
efmap.clear();
ff.EgdeFeatures(cur.ts, cur.tree.h_m_pairs[j].first,
cur.tree.h_m_pairs[j].second,
&efmap);
cur.features += efmap;
}
for (int j = 0; j < cur.tree.roots.size(); ++j) {
efmap.clear();
ff.EgdeFeatures(cur.ts, -1, cur.tree.roots[j], &efmap);
cur.features += efmap;
}
}
empirical += cur.features;
forests[i].resize(cur.ts.words.size());
forests[i].ExtractFeatures(cur.ts, ffs);
}
if (flag) cerr << endl;
//cerr << "EMP: " << empirical << endl; //DE
vector<weight_t> weights(FD::NumFeats(), 0.0);
vector<weight_t> g(FD::NumFeats(), 0.0);
cerr << "features initialized\noptimizing...\n";
boost::shared_ptr<BatchOptimizer> o;
o.reset(new LBFGSOptimizer(g.size(), conf["correction_buffers"].as<int>()));
int iterations = 1000;
for (int iter = 0; iter < iterations; ++iter) {
cerr << "ITERATION " << iter << " " << flush;
fill(g.begin(), g.end(), 0.0);
for (SparseVector<double>::const_iterator it = empirical.begin(); it != empirical.end(); ++it)
g[it->first] = -it->second;
double obj = -empirical.dot(weights);
// SparseVector<double> mfm; //DE
for (int i = 0; i < corpus.size(); ++i) {
const int num_words = corpus[i].ts.words.size();
forests[i].Reweight(weights);
double lz;
forests[i].EdgeMarginals(&lz);
obj -= lz;
//cerr << " O = " << (-corpus[i].features.dot(weights)) << " D=" << -lz << " OO= " << (-corpus[i].features.dot(weights) - lz) << endl;
//cerr << " ZZ = " << zz << endl;
for (int h = -1; h < num_words; ++h) {
for (int m = 0; m < num_words; ++m) {
if (h == m) continue;
const ArcFactoredForest::Edge& edge = forests[i](h,m);
const SparseVector<weight_t>& fmap = edge.features;
double prob = edge.edge_prob.as_float();
if (prob < -0.000001) { cerr << "Prob < 0: " << prob << endl; prob = 0; }
if (prob > 1.000001) { cerr << "Prob > 1: " << prob << endl; prob = 1; }
AddFeatures(prob, fmap, &g);
//mfm += fmap * prob; // DE
}
}
}
//cerr << endl << "E: " << empirical << endl; // DE
//cerr << "M: " << mfm << endl; // DE
double r = ApplyRegularizationTerms(conf["regularization_strength"].as<double>(), weights, &g);
double gnorm = 0;
for (int i = 0; i < g.size(); ++i)
gnorm += g[i]*g[i];
ostringstream ll;
ll << "ITER=" << (iter+1) << "\tOBJ=" << (obj+r) << "\t[F=" << obj << " R=" << r << "]\tGnorm=" << sqrt(gnorm);
cerr << endl << ll.str() << endl;
obj += r;
assert(obj >= 0);
o->Optimize(obj, g, &weights);
Weights::ShowLargestFeatures(weights);
string sl = ll.str();
Weights::WriteToFile(o->HasConverged() ? "weights.final.gz" : "weights.cur.gz", weights, true, &sl);
if (o->HasConverged()) { cerr << "CONVERGED\n"; break; }
}
forests[0].Reweight(weights);
TreeSampler ts(forests[0]);
EdgeSubset tt; ts.SampleRandomSpanningTree(&tt);
return 0;
}
|
