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
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
|
#include <sstream>
#include <iostream>
#include <fstream>
#include <vector>
#include <cassert>
#include <cmath>
#include <boost/shared_ptr.hpp>
#include <boost/program_options.hpp>
#include <boost/program_options/variables_map.hpp>
#include "optimize.h"
#include "fdict.h"
#include "weights.h"
#include "sparse_vector.h"
using namespace std;
using boost::shared_ptr;
namespace po = boost::program_options;
void SanityCheck(const vector<double>& w) {
for (int i = 0; i < w.size(); ++i) {
assert(!isnan(w[i]));
assert(!isinf(w[i]));
}
}
struct FComp {
const vector<double>& w_;
FComp(const vector<double>& w) : w_(w) {}
bool operator()(int a, int b) const {
return fabs(w_[a]) > fabs(w_[b]);
}
};
void ShowLargestFeatures(const vector<double>& w) {
vector<int> fnums(w.size());
for (int i = 0; i < w.size(); ++i)
fnums[i] = i;
vector<int>::iterator mid = fnums.begin();
mid += (w.size() > 10 ? 10 : w.size());
partial_sort(fnums.begin(), mid, fnums.end(), FComp(w));
cerr << "TOP FEATURES:";
for (vector<int>::iterator i = fnums.begin(); i != mid; ++i) {
cerr << ' ' << FD::Convert(*i) << '=' << w[*i];
}
cerr << endl;
}
void InitCommandLine(int argc, char** argv, po::variables_map* conf) {
po::options_description opts("Configuration options");
opts.add_options()
("input_weights,i",po::value<string>(),"Input feature weights file")
("output_weights,o",po::value<string>()->default_value("-"),"Output feature weights file")
("optimization_method,m", po::value<string>()->default_value("lbfgs"), "Optimization method (sgd, lbfgs, rprop)")
("state,s",po::value<string>(),"Read (and write if output_state is not set) optimizer state from this state file. In the first iteration, the file should not exist.")
("input_format,f",po::value<string>()->default_value("b64"),"Encoding of the input (b64 or text)")
("output_state,S", po::value<string>(), "Output state file (optional override)")
("correction_buffers,M", po::value<int>()->default_value(10), "Number of gradients for LBFGS to maintain in memory")
("eta,e", po::value<double>()->default_value(0.1), "Learning rate for SGD (eta)")
("gaussian_prior,p","Use a Gaussian prior on the weights")
("means,u", po::value<string>(), "File containing the means for Gaussian prior")
("sigma_squared", po::value<double>()->default_value(1.0), "Sigma squared term for spherical Gaussian prior");
po::options_description clo("Command line options");
clo.add_options()
("config", po::value<string>(), "Configuration file")
("help,h", "Print this help message and exit");
po::options_description dconfig_options, dcmdline_options;
dconfig_options.add(opts);
dcmdline_options.add(opts).add(clo);
po::store(parse_command_line(argc, argv, dcmdline_options), *conf);
if (conf->count("config")) {
ifstream config((*conf)["config"].as<string>().c_str());
po::store(po::parse_config_file(config, dconfig_options), *conf);
}
po::notify(*conf);
if (conf->count("help") || !conf->count("input_weights") || !conf->count("state")) {
cerr << dcmdline_options << endl;
exit(1);
}
}
int main(int argc, char** argv) {
po::variables_map conf;
InitCommandLine(argc, argv, &conf);
const bool use_b64 = conf["input_format"].as<string>() == "b64";
Weights weights;
weights.InitFromFile(conf["input_weights"].as<string>());
const string s_obj = "**OBJ**";
int num_feats = FD::NumFeats();
cerr << "Number of features: " << num_feats << endl;
const bool gaussian_prior = conf.count("gaussian_prior");
vector<double> means(num_feats, 0);
if (conf.count("means")) {
if (!gaussian_prior) {
cerr << "Don't use --means without --gaussian_prior!\n";
exit(1);
}
Weights wm;
wm.InitFromFile(conf["means"].as<string>());
if (num_feats != FD::NumFeats()) {
cerr << "[ERROR] Means file had unexpected features!\n";
exit(1);
}
wm.InitVector(&means);
}
shared_ptr<Optimizer> o;
const string omethod = conf["optimization_method"].as<string>();
if (omethod == "sgd")
o.reset(new SGDOptimizer(conf["eta"].as<double>()));
else if (omethod == "rprop")
o.reset(new RPropOptimizer(num_feats)); // TODO add configuration
else
o.reset(new LBFGSOptimizer(num_feats, conf["correction_buffers"].as<int>()));
cerr << "Optimizer: " << o->Name() << endl;
string state_file = conf["state"].as<string>();
{
ifstream in(state_file.c_str(), ios::binary);
if (in)
o->Load(&in);
else
cerr << "No state file found, assuming ITERATION 1\n";
}
vector<double> lambdas(num_feats, 0);
weights.InitVector(&lambdas);
double objective = 0;
vector<double> gradient(num_feats, 0);
// 0<TAB>**OBJ**=12.2;Feat1=2.3;Feat2=-0.2;
// 0<TAB>**OBJ**=1.1;Feat1=1.0;
int total_lines = 0; // TODO - this should be a count of the
// training instances!!
while(cin) {
string line;
getline(cin, line);
if (line.empty()) continue;
++total_lines;
int feat;
double val;
size_t i = line.find("\t");
assert(i != string::npos);
++i;
if (use_b64) {
SparseVector<double> g;
double obj;
if (!B64::Decode(&obj, &g, &line[i], line.size() - i)) {
cerr << "B64 decoder returned error, skipping gradient!\n";
cerr << " START: " << line.substr(0,min(200ul, line.size())) << endl;
if (line.size() > 200)
cerr << " END: " << line.substr(line.size() - 200, 200) << endl;
cout << "-1\tRESTART\n";
exit(99);
}
objective += obj;
const SparseVector<double>& cg = g;
for (SparseVector<double>::const_iterator it = cg.begin(); it != cg.end(); ++it) {
if (it->first >= num_feats) {
cerr << "Unexpected feature in gradient: " << FD::Convert(it->first) << endl;
abort();
}
gradient[it->first] -= it->second;
}
} else { // text encoding - your gradients will not be accurate!
while (i < line.size()) {
size_t start = i;
while (line[i] != '=' && i < line.size()) ++i;
if (i == line.size()) { cerr << "FORMAT ERROR\n"; break; }
string fname = line.substr(start, i - start);
if (fname == s_obj) {
feat = -1;
} else {
feat = FD::Convert(line.substr(start, i - start));
if (feat >= num_feats) {
cerr << "Unexpected feature in gradient: " << line.substr(start, i - start) << endl;
abort();
}
}
++i;
start = i;
while (line[i] != ';' && i < line.size()) ++i;
if (i - start == 0) continue;
val = atof(line.substr(start, i - start).c_str());
++i;
if (feat == -1) {
objective += val;
} else {
gradient[feat] -= val;
}
}
}
}
if (gaussian_prior) {
const double sigsq = conf["sigma_squared"].as<double>();
double norm = 0;
for (int k = 1; k < lambdas.size(); ++k) {
const double& lambda_k = lambdas[k];
if (lambda_k) {
const double param = (lambda_k - means[k]);
norm += param * param;
gradient[k] += param / sigsq;
}
}
const double reg = norm / (2.0 * sigsq);
cerr << "REGULARIZATION TERM: " << reg << endl;
objective += reg;
}
cerr << "EVALUATION #" << o->EvaluationCount() << " OBJECTIVE: " << objective << endl;
double gnorm = 0;
for (int i = 0; i < gradient.size(); ++i)
gnorm += gradient[i] * gradient[i];
cerr << " GNORM=" << sqrt(gnorm) << endl;
vector<double> old = lambdas;
int c = 0;
while (old == lambdas) {
++c;
if (c > 1) { cerr << "Same lambdas, repeating optimization\n"; }
o->Optimize(objective, gradient, &lambdas);
assert(c < 5);
}
old.clear();
SanityCheck(lambdas);
ShowLargestFeatures(lambdas);
weights.InitFromVector(lambdas);
weights.WriteToFile(conf["output_weights"].as<string>(), false);
const bool conv = o->HasConverged();
if (conv) { cerr << "OPTIMIZER REPORTS CONVERGENCE!\n"; }
if (conf.count("output_state"))
state_file = conf["output_state"].as<string>();
ofstream out(state_file.c_str(), ios::binary);
cerr << "Writing state to: " << state_file << endl;
o->Save(&out);
out.close();
cout << o->EvaluationCount() << "\t" << conv << endl;
return 0;
}
|
