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
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
|
/**
* \description This is the main for the new version of the mert algorithm developed during the 2nd MT marathon
*/
#include <limits>
#include <unistd.h>
#include <cstdlib>
#include <iostream>
#include <fstream>
#include <cmath>
#include <ctime>
#include <getopt.h>
#include "Data.h"
#include "Point.h"
#include "Scorer.h"
#include "ScorerFactory.h"
#include "ScoreData.h"
#include "FeatureData.h"
#include "Optimizer.h"
#include "Types.h"
#include "Timer.h"
#include "Util.h"
#include "../moses/src/ThreadPool.h"
using namespace std;
namespace {
const char kDefaultOptimizer[] = "powell";
const char kDefaultScorer[] = "BLEU";
const char kDefaultScorerFile[] = "statscore.data";
const char kDefaultFeatureFile[] = "features.data";
const char kDefaultInitFile[] = "init.opt";
// Used when saving optimized weights.
const char kOutputFile[] = "weights.txt";
/**
* Runs an optimisation, or a random restart.
*/
class OptimizationTask : public Moses::Task {
public:
OptimizationTask(Optimizer* optimizer, const Point& point)
: m_optimizer(optimizer), m_point(point) {}
~OptimizationTask() {}
virtual void Run() {
m_score = m_optimizer->Run(m_point);
}
virtual bool DeleteAfterExecution() {
return false;
}
void resetOptimizer() {
if (m_optimizer) {
delete m_optimizer;
m_optimizer = NULL;
}
}
statscore_t getScore() const {
return m_score;
}
const Point& getPoint() const {
return m_point;
}
private:
// Do not allow the user to instanciate without arguments.
OptimizationTask() {}
Optimizer* m_optimizer;
Point m_point;
statscore_t m_score;
};
bool WriteFinalWeights(const char* filename, const Point& point) {
ofstream ofs(filename);
if (!ofs) {
cerr << "Cannot open " << filename << endl;
return false;
}
ofs << point << endl;
return true;
}
void usage(int ret)
{
cerr << "usage: mert -d <dimensions> (mandatory)" << endl;
cerr << "[-n] retry ntimes (default 1)" << endl;
cerr << "[-m] number of random directions in powell (default 0)"<< endl;
cerr << "[-o] the indexes to optimize(default all)" << endl;
cerr << "[-t] the optimizer(default " << kDefaultOptimizer << ")" << endl;
cerr << "[-r] the random seed (defaults to system clock)" << endl;
cerr << "[--sctype|-s] the scorer type (default " << kDefaultScorer << ")" << endl;
cerr << "[--scconfig|-c] configuration string passed to scorer" << endl;
cerr << "[--scfile|-S] comma separated list of scorer data files (default " << kDefaultScorerFile << ")" << endl;
cerr << "[--ffile|-F] comma separated list of feature data files (default " << kDefaultFeatureFile << ")" << endl;
cerr << "[--ifile|-i] the starting point data file (default " << kDefaultInitFile << ")" << endl;
#ifdef WITH_THREADS
cerr << "[--threads|-T] use multiple threads (default 1)" << endl;
#endif
cerr << "[--shard-count] Split data into shards, optimize for each shard and average" << endl;
cerr << "[--shard-size] Shard size as proportion of data. If 0, use non-overlapping shards" << endl;
cerr << "[-v] verbose level" << endl;
cerr << "[--help|-h] print this message and exit" << endl;
exit(ret);
}
static struct option long_options[] = {
{"pdim", 1, 0, 'd'},
{"ntry", 1, 0, 'n'},
{"nrandom", 1, 0, 'm'},
{"rseed", required_argument, 0, 'r'},
{"optimize", 1, 0, 'o'},
{"pro", required_argument, 0, 'p'},
{"type", 1, 0, 't'},
{"sctype", 1, 0, 's'},
{"scconfig", required_argument, 0, 'c'},
{"scfile", 1, 0, 'S'},
{"ffile", 1, 0, 'F'},
{"ifile", 1, 0, 'i'},
#ifdef WITH_THREADS
{"threads", required_argument, 0, 'T'},
#endif
{"shard-count", required_argument, 0, 'a'},
{"shard-size", required_argument, 0, 'b'},
{"verbose", 1, 0, 'v'},
{"help", no_argument, 0, 'h'},
{0, 0, 0, 0}
};
struct ProgramOption {
string to_optimize_str;
int pdim;
int ntry;
int nrandom;
int seed;
bool has_seed;
string optimize_type;
string scorer_type;
string scorer_config;
string scorer_file;
string feature_file;
string init_file;
size_t num_threads;
float shard_size;
size_t shard_count;
ProgramOption()
: to_optimize_str(""),
pdim(-1),
ntry(1),
nrandom(0),
seed(0),
has_seed(false),
optimize_type(kDefaultOptimizer),
scorer_type(kDefaultScorer),
scorer_config(""),
scorer_file(kDefaultScorerFile),
feature_file(kDefaultFeatureFile),
init_file(kDefaultInitFile),
num_threads(1),
shard_size(0),
shard_count(0) { }
};
void ParseCommandOptions(int argc, char** argv, ProgramOption* opt) {
int c;
int option_index;
while ((c = getopt_long(argc, argv, "o:r:d:n:m:t:s:S:F:v:p:", long_options, &option_index)) != -1) {
switch (c) {
case 'o':
opt->to_optimize_str = string(optarg);
break;
case 'd':
opt->pdim = strtol(optarg, NULL, 10);
break;
case 'n':
opt->ntry = strtol(optarg, NULL, 10);
break;
case 'm':
opt->nrandom = strtol(optarg, NULL, 10);
break;
case 'r':
opt->seed = strtol(optarg, NULL, 10);
opt->has_seed = true;
break;
case 't':
opt->optimize_type = string(optarg);
break;
case's':
opt->scorer_type = string(optarg);
break;
case 'c':
opt->scorer_config = string(optarg);
break;
case 'S':
opt->scorer_file = string(optarg);
break;
case 'F':
opt->feature_file = string(optarg);
break;
case 'i':
opt->init_file = string(optarg);
break;
case 'v':
setverboselevel(strtol(optarg, NULL, 10));
break;
#ifdef WITH_THREADS
case 'T':
opt->num_threads = strtol(optarg, NULL, 10);
if (opt->num_threads < 1) opt->num_threads = 1;
break;
#endif
case 'a':
opt->shard_count = strtof(optarg, NULL);
break;
case 'b':
opt->shard_size = strtof(optarg, NULL);
break;
case 'h':
usage(0);
break;
default:
usage(1);
}
}
}
} // anonymous namespace
int main(int argc, char **argv)
{
ResetUserTime();
ProgramOption option;
ParseCommandOptions(argc, argv, &option);
vector<unsigned> to_optimize;
vector<vector<parameter_t> > start_list;
vector<parameter_t> min;
vector<parameter_t> max;
// NOTE: those mins and max are the bound for the starting points of the algorithm, not strict bound on the result!
if (option.pdim < 0)
usage(1);
cerr << "shard_size = " << option.shard_size << " shard_count = " << option.shard_count << endl;
if (option.shard_size && !option.shard_count) {
cerr << "Error: shard-size provided without shard-count" << endl;
exit(1);
}
if (option.shard_size > 1 || option.shard_size < 0) {
cerr << "Error: shard-size should be between 0 and 1" << endl;
exit(1);
}
if (option.has_seed) {
cerr << "Seeding random numbers with " << option.seed << endl;
srandom(option.seed);
} else {
cerr << "Seeding random numbers with system clock " << endl;
srandom(time(NULL));
}
// read in starting points
string onefile;
while (!option.init_file.empty()) {
getNextPound(option.init_file, onefile, ",");
vector<parameter_t> start;
ifstream opt(onefile.c_str());
if (opt.fail()) {
cerr << "could not open initfile: " << option.init_file << endl;
exit(3);
}
start.resize(option.pdim);//to do:read from file
int j;
for (j = 0; j < option.pdim && !opt.fail(); j++) {
opt >> start[j];
}
if (j < option.pdim) {
cerr << option.init_file << ":Too few starting weights." << endl;
exit(3);
}
start_list.push_back(start);
// for the first time, also read in the min/max values for scores
if (start_list.size() == 1) {
min.resize(option.pdim);
for (j = 0; j < option.pdim && !opt.fail(); j++) {
opt >> min[j];
}
if (j < option.pdim) {
cerr << option.init_file << ":Too few minimum weights." << endl;
cerr << "error could not initialize start point with " << option.init_file << endl;
cerr << "j: " << j << ", pdim: " << option.pdim << endl;
exit(3);
}
max.resize(option.pdim);
for (j = 0; j < option.pdim && !opt.fail(); j++) {
opt >> max[j];
}
if (j < option.pdim) {
cerr << option.init_file << ":Too few maximum weights." << endl;
exit(3);
}
}
opt.close();
}
vector<string> ScoreDataFiles;
if (option.scorer_file.length() > 0) {
Tokenize(option.scorer_file.c_str(), ',', &ScoreDataFiles);
}
vector<string> FeatureDataFiles;
if (option.feature_file.length() > 0) {
Tokenize(option.feature_file.c_str(), ',', &FeatureDataFiles);
}
if (ScoreDataFiles.size() != FeatureDataFiles.size()) {
throw runtime_error("Error: there is a different number of previous score and feature files");
}
// it make sense to know what parameter set were used to generate the nbest
Scorer *TheScorer = ScorerFactory::getScorer(option.scorer_type, option.scorer_config);
//load data
Data data(*TheScorer);
for (size_t i = 0; i < ScoreDataFiles.size(); i++) {
cerr<<"Loading Data from: "<< ScoreDataFiles.at(i) << " and " << FeatureDataFiles.at(i) << endl;
data.load(FeatureDataFiles.at(i), ScoreDataFiles.at(i));
}
//ADDED_BY_TS
data.remove_duplicates();
//END_ADDED
PrintUserTime("Data loaded");
// starting point score over latest n-best, accumulative n-best
//vector<unsigned> bests;
//compute bests with sparse features needs to be implemented
//currently sparse weights are not even loaded
//statscore_t score = TheScorer->score(bests);
if (option.to_optimize_str.length() > 0) {
cerr << "Weights to optimize: " << option.to_optimize_str << endl;
// Parse string to get weights to optimize, and set them as active
string substring;
int index;
while (!option.to_optimize_str.empty()) {
getNextPound(option.to_optimize_str, substring, ",");
index = data.getFeatureIndex(substring);
cerr << "FeatNameIndex:" << index << " to insert" << endl;
//index = strtol(substring.c_str(), NULL, 10);
if (index >= 0 && index < option.pdim) {
to_optimize.push_back(index);
} else {
cerr << "Index " << index << " is out of bounds. Allowed indexes are [0," << option.pdim - 1 << "]." << endl;
}
}
} else {
//set all weights as active
to_optimize.resize(option.pdim);//We'll optimize on everything
for (int i = 0; i < option.pdim; i++) {
to_optimize[i] = 1;
}
}
// treat sparse features just like regular features
if (data.hasSparseFeatures()) {
data.mergeSparseFeatures();
}
#ifdef WITH_THREADS
cerr << "Creating a pool of " << option.num_threads << " threads" << endl;
Moses::ThreadPool pool(option.num_threads);
#endif
Point::setpdim(option.pdim);
Point::setdim(to_optimize.size());
//starting points consist of specified points and random restarts
vector<Point> startingPoints;
for (size_t i = 0; i < start_list.size(); ++i) {
startingPoints.push_back(Point(start_list[i], min, max));
}
for (int i = 0; i < option.ntry; ++i) {
startingPoints.push_back(Point(start_list[0], min, max));
startingPoints.back().Randomize();
}
vector<vector<OptimizationTask*> > allTasks(1);
//optional sharding
vector<Data> shards;
if (option.shard_count) {
data.createShards(option.shard_count, option.shard_size, option.scorer_config, shards);
allTasks.resize(option.shard_count);
}
// launch tasks
for (size_t i = 0; i < allTasks.size(); ++i) {
Data& data_ref = data;
if (option.shard_count)
data_ref = shards[i]; //use the sharded data if it exists
vector<OptimizationTask*>& tasks = allTasks[i];
Optimizer *optimizer = OptimizerFactory::BuildOptimizer(option.pdim, to_optimize, start_list[0], option.optimize_type, option.nrandom);
optimizer->SetScorer(data_ref.getScorer());
optimizer->SetFData(data_ref.getFeatureData());
// A task for each start point
for (size_t j = 0; j < startingPoints.size(); ++j) {
OptimizationTask* task = new OptimizationTask(optimizer, startingPoints[j]);
tasks.push_back(task);
#ifdef WITH_THREADS
pool.Submit(task);
#else
task->Run();
#endif
}
}
// wait for all threads to finish
#ifdef WITH_THREADS
pool.Stop(true);
#endif
statscore_t total = 0;
Point totalP;
// collect results
for (size_t i = 0; i < allTasks.size(); ++i) {
statscore_t best = 0, mean = 0, var = 0;
Point bestP;
for (size_t j = 0; j < allTasks[i].size(); ++j) {
statscore_t score = allTasks[i][j]->getScore();
mean += score;
var += score * score;
if (score > best) {
bestP = allTasks[i][j]->getPoint();
best = score;
}
}
mean /= (float)option.ntry;
var /= (float)option.ntry;
var = sqrt(abs(var - mean * mean));
if (verboselevel() > 1) {
cerr << "shard " << i << " best score: " << best << " variance of the score (for " << option.ntry << " try): " << var << endl;
}
totalP += bestP;
total += best;
if (verboselevel() > 1)
cerr << "bestP " << bestP << endl;
}
//cerr << "totalP: " << totalP << endl;
Point finalP = totalP * (1.0 / allTasks.size());
statscore_t final = total / allTasks.size();
if (verboselevel() > 1)
cerr << "bestP: " << finalP << endl;
// L1-Normalization of the best Point
if ((int)to_optimize.size() == option.pdim) {
finalP.NormalizeL1();
}
cerr << "Best point: " << finalP << " => " << final << endl;
if (!WriteFinalWeights(kOutputFile, finalP)) {
cerr << "Warning: Failed to write the final point" << endl;
}
for (size_t i = 0; i < allTasks.size(); ++i) {
allTasks[i][0]->resetOptimizer();
for (size_t j = 0; j < allTasks[i].size(); ++j) {
delete allTasks[i][j];
}
}
delete TheScorer;
PrintUserTime("Stopping...");
return 0;
}
|