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parser.cpp
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#include "parser.h"
#include "options.h"
#include "decoder1o.h"
#include "decoder2o.h"
#include "conllreader.h"
#include "conllwriter.h"
#include "treeutils.hpp"
#if _WIN32
#include <Windows.h>
#define sleep Sleep
#endif // end for _WIN32
namespace ltp {
namespace parser {
Parser::Parser() {
init_opt();
}
Parser::Parser(ConfigParser & cfg) {
init_opt();
parse_cfg(cfg);
}
Parser::~Parser() {
if (decoder) {
delete decoder;
}
if (model) {
delete model;
}
}
void Parser::init_opt() {
model_opt.labeled = false;
model_opt.decoder_name = "1o";
model_opt.display_interval = 1000;
train_opt.train_file = "";
train_opt.holdout_file = "";
train_opt.max_iter = 10;
train_opt.algorithm = "pa";
train_opt.model_name = "";
test_opt.test_file = "";
test_opt.model_file = "";
feat_opt.use_postag = false;
feat_opt.use_postag_unigram = false;
feat_opt.use_postag_bigram = false;
feat_opt.use_dependency = false;
feat_opt.use_dependency_unigram = false;
feat_opt.use_dependency_bigram = false;
feat_opt.use_dependency_surrounding = false;
feat_opt.use_dependency_between = false;
feat_opt.use_sibling = false;
feat_opt.use_sibling_basic = false;
feat_opt.use_sibling_linear = false;
feat_opt.use_grand = false;
feat_opt.use_grand_basic = false;
feat_opt.use_grand_linear = false;
feat_opt.use_last_sibling = false;
feat_opt.use_no_grand = false;
feat_opt.use_distance_in_features = true;
}
bool Parser::parse_cfg(utility::ConfigParser & cfg) {
string strbuf;
int intbuf;
if (cfg.has_section("model")) {
if (cfg.get_integer("model", "labeled", intbuf)) {
model_opt.labeled = (intbuf == 1);
}
if (cfg.get("model", "decoder-name", strbuf)) {
model_opt.decoder_name = strbuf;
} else {
WARNING_LOG("decoder-name is not configed, [1o] is set as default.");
}
}
__TRAIN__ = false;
if (cfg.has_section("train")) {
TRACE_LOG("train model specified.");
__TRAIN__ = true;
if (cfg.get("train", "train-file", strbuf)) {
train_opt.train_file = strbuf;
} else {
ERROR_LOG("train-file config item is not set.");
return false;
} // end for if (cfg.get("train", "train-file", strbuf))
if (cfg.get("train", "holdout-file", strbuf)) {
train_opt.holdout_file = strbuf;
} else {
ERROR_LOG("holdout-file config item is not set.");
return false;
}
if (cfg.get("train", "algorithm", strbuf)) {
train_opt.algorithm = strbuf;
} else {
WARNING_LOG("algorithm is not configed, [PA] is set as default.");
}
train_opt.model_name = train_opt.train_file + "." + train_opt.algorithm + ".model";
if (cfg.get("train", "model-name", strbuf)) {
train_opt.model_name = strbuf;
} else {
WARNING_LOG("model name is not configed, [%s] is set as default",
train_opt.model_name.c_str());
}
if (cfg.get_integer("train", "max-iter", intbuf)) {
train_opt.max_iter = intbuf;
} else {
WARNING_LOG("max-iter is not configed, [10] is set as default.");
}
} // end for cfg.has_section("train")
__TEST__ = false;
if (cfg.has_section("test")) {
__TEST__ = true;
if (cfg.get("test", "test-file", strbuf)) {
test_opt.test_file = strbuf;
} else {
ERROR_LOG("test-file config item is not set.");
return false;
} // end for if (cfg.get("train", "train-file", strbuf))
if (cfg.get("test", "model-file", strbuf)) {
test_opt.model_file = strbuf;
} else {
ERROR_LOG("model-file config item is not set.");
return false;
}
}
if (cfg.has_section("feature")) {
if (cfg.get_integer("feature", "use-postag", intbuf)) {
feat_opt.use_postag = (intbuf == 1);
}
if (cfg.get_integer("feature", "use-postag-unigram", intbuf)) {
feat_opt.use_postag_unigram = (intbuf == 1);
}
if (cfg.get_integer("feature", "use-postag-bigram", intbuf)) {
feat_opt.use_postag_bigram = (intbuf == 1);
}
if (cfg.get_integer("feature", "use-dependency", intbuf)) {
feat_opt.use_dependency = (intbuf == 1);
}
if (cfg.get_integer("feature", "use-dependency-unigram", intbuf)) {
feat_opt.use_dependency_unigram = (intbuf == 1);
}
if (cfg.get_integer("feature", "use-dependency-bigram", intbuf)) {
feat_opt.use_dependency_bigram = (intbuf == 1);
}
if (cfg.get_integer("feature", "use-dependency-surrounding", intbuf)) {
feat_opt.use_dependency_surrounding = (intbuf == 1);
}
if (cfg.get_integer("feature", "use-dependency-between", intbuf)) {
feat_opt.use_dependency_between = (intbuf == 1);
}
if (cfg.get_integer("feature", "use-sibling", intbuf)) {
feat_opt.use_sibling = (intbuf == 1);
}
if (cfg.get_integer("feature", "use-sibling-basic", intbuf)) {
feat_opt.use_sibling_basic = (intbuf == 1);
}
if (cfg.get_integer("feature", "use-sibling-linear", intbuf)) {
feat_opt.use_sibling_linear = (intbuf == 1);
}
if (cfg.get_integer("feature", "use-grand", intbuf)) {
feat_opt.use_grand = (intbuf == 1);
}
if (cfg.get_integer("feature", "use-grand-basic", intbuf)) {
feat_opt.use_grand_basic = (intbuf == 1);
}
if (cfg.get_integer("feature", "use-grand-linear", intbuf)) {
feat_opt.use_grand_linear = (intbuf == 1);
}
}
if (model_opt.decoder_name == "1o") {
if (feat_opt.use_sibling) {
WARNING_LOG("Sibling features should not be configed "
"with 1st-order decoder.");
TRACE_LOG("Sibling features is inactived.");
feat_opt.use_sibling = false;
}
if (feat_opt.use_grand) {
WARNING_LOG("Grandchild features should not be configed "
"with 1st-order decoder.");
TRACE_LOG("Grandchild features is inactived.");
feat_opt.use_grand = false;
}
} else if (model_opt.decoder_name == "2o-sib") {
if (feat_opt.use_grand) {
WARNING_LOG("Grandchild features should not be configed "
"with 2nd-order-sibling decoder.");
TRACE_LOG("Grandchild features is inactived.");
feat_opt.use_grand = false;
}
}
// detrieve dependency type from configuration
feat_opt.use_unlabeled_dependency = (model_opt.labeled == false &&
feat_opt.use_dependency);
feat_opt.use_labeled_dependency = (model_opt.labeled == true &&
feat_opt.use_dependency);
// detrieve sibling type from configuration
feat_opt.use_unlabeled_sibling = (model_opt.labeled == false &&
feat_opt.use_sibling);
feat_opt.use_labeled_sibling = (model_opt.labeled == true &&
feat_opt.use_sibling);
feat_opt.use_unlabeled_grand = (model_opt.labeled == false &&
feat_opt.use_grand);
feat_opt.use_labeled_grand = (model_opt.labeled == true &&
feat_opt.use_grand);
return true;
}
void Parser::build_configuration(void) {
// build postags set, deprels set.
// map deprels from string to int when model_opt.labeled is configed.
// need to check if the model is initialized
for (int i = 0; i < train_dat.size(); ++ i) {
int len = train_dat[i]->size();
// if labeled is configured, init the deprelsidxs
if (model_opt.labeled) {
train_dat[i]->deprelsidx.resize(len);
train_dat[i]->predicted_deprelsidx.resize(len);
}
for (int j = 1; j < len; ++ j) {
model->postags.push(train_dat[i]->postags[j].c_str());
if (model_opt.labeled) {
int idx = -1;
idx = model->deprels.push(train_dat[i]->deprels[j].c_str());
train_dat[i]->deprelsidx[j] = idx;
}
}
}
}
void Parser::build_feature_space(void) {
model->space.build_feature_space(model->num_deprels(), train_dat);
} // end for build_feature_space
void Parser::collect_unlabeled_features_of_one_instance(Instance * inst,
const vector<int> & heads,
SparseVec & vec ) {
vec.zero();
if (feat_opt.use_dependency) {
for (treeutils::DEPIterator itx(heads); !itx.end(); ++ itx) {
int hid = itx.hid();
int cid = itx.cid();
const FeatureVector * fv = inst->depu_features[hid][cid];
if (NULL == fv) {
continue;
}
vec.add(fv->idx, fv->val, fv->n, 1.);
}
}
if (feat_opt.use_sibling) {
for (treeutils::SIBIterator itx(heads, feat_opt.use_last_sibling); !itx.end(); ++ itx) {
int hid = itx.hid();
int cid = itx.cid();
int sid = itx.sid();
const FeatureVector * fv = inst->sibu_features[hid][cid][sid];
if (NULL == fv) {
continue;
}
vec.add(fv->idx, fv->val, fv->n, 1.);
}
}
if (feat_opt.use_grand) {
for (treeutils::GRDIterator itx(heads, feat_opt.use_no_grand); !itx.end(); ++ itx) {
int hid = itx.hid();
int cid = itx.cid();
int gid = itx.gid();
const FeatureVector * fv = inst->grdu_features[hid][cid][gid];
if (NULL == fv) {
continue;
}
vec.add(fv->idx, fv->val, fv->n, 1.);
}
}
}
void Parser::collect_labeled_features_of_one_instance(Instance * inst,
const vector<int> & heads,
const vector<int> & deprelsidx,
SparseVec & vec) {
vec.zero();
if (feat_opt.use_dependency) {
for (treeutils::DEPIterator itx(heads); !itx.end(); ++ itx) {
int hid = itx.hid();
int cid = itx.cid();
int relidx = deprelsidx[cid];
const FeatureVector * fv = inst->depl_features[hid][cid][relidx];
if (NULL == fv) {
continue;
}
vec.add(fv->idx, fv->val, fv->n, fv->loff, 1.);
}
}
if (feat_opt.use_sibling) {
for (treeutils::SIBIterator itx(heads, feat_opt.use_last_sibling); !itx.end(); ++ itx) {
int hid = itx.hid();
int cid = itx.cid();
int sid = itx.sid();
int relidx = deprelsidx[cid];
const FeatureVector * fv = inst->sibl_features[hid][cid][sid][relidx];
if (NULL == fv) {
continue;
}
vec.add(fv->idx, fv->val, fv->n, fv->loff, 1.);
}
}
if (feat_opt.use_grand) {
for (treeutils::GRDIterator itx(heads, feat_opt.use_no_grand); !itx.end(); ++ itx) {
int hid = itx.hid();
int cid = itx.cid();
int gid = itx.gid();
int relidx = deprelsidx[cid];
const FeatureVector * fv = inst->grdl_features[hid][cid][gid][relidx];
if (NULL == fv) {
continue;
}
vec.add(fv->idx, fv->val, fv->n, fv->loff, 1.);
}
}
}
void Parser::collect_features_of_one_instance(Instance * inst, bool gold) {
if (gold) {
if (!model_opt.labeled) {
collect_unlabeled_features_of_one_instance(inst,
inst->heads,
inst->features);
} else {
collect_labeled_features_of_one_instance(inst,
inst->heads,
inst->deprelsidx,
inst->features);
}
} else {
if (!model_opt.labeled) {
collect_unlabeled_features_of_one_instance(inst,
inst->predicted_heads,
inst->predicted_features);
} else {
collect_labeled_features_of_one_instance(inst,
inst->predicted_heads,
inst->predicted_deprelsidx,
inst->predicted_features);
}
}
}
bool Parser::read_instances(const char * filename, vector<Instance *> & dat) {
Instance * inst = NULL;
ifstream f(filename);
if (!f) {
return false;
}
CoNLLReader reader(f);
int num_inst = 0;
while ((inst = reader.next())) {
dat.push_back(inst);
++ num_inst;
if (num_inst % model_opt.display_interval == 0) {
TRACE_LOG("Reading in [%d] instances.", num_inst);
}
}
return true;
}
Decoder * Parser::build_decoder(void) {
Decoder * deco;
if (model_opt.decoder_name == "1o") {
if (!model_opt.labeled) {
deco = new Decoder1O();
} else {
deco = new Decoder1O(model->num_deprels());
}
} else if (model_opt.decoder_name == "2o-sib") {
if (!model_opt.labeled) {
deco = new Decoder2O();
} else {
deco = new Decoder2O(model->num_deprels());
}
} else if (model_opt.decoder_name == "2o-carreras") {
if (!model_opt.labeled) {
deco = new Decoder2OCarreras();
} else {
deco = new Decoder2OCarreras(model->num_deprels());
}
}
return deco;
}
void Parser::extract_features(Instance * inst) {
int len = inst->size();
int L = model->num_deprels();
FeatureSpace& space = model->space;
if (feat_opt.use_dependency) {
if (!model_opt.labeled) {
inst->depu_features.resize(len, len);
inst->depu_scores.resize(len, len);
inst->depu_features = 0;
inst->depu_scores = DOUBLE_NEG_INF;
} else {
inst->depl_features.resize(len, len, L);
inst->depl_scores.resize(len, len, L);
inst->depl_features = 0;
inst->depl_scores = DOUBLE_NEG_INF;
}
vector< StringVec > cache;
vector< int > cache_again;
int N = DEPExtractor::num_templates();
cache.resize( N );
for (treeutils::DEPTreeSpaceIterator itx(len); !itx.end(); ++ itx) {
int hid = itx.hid();
int cid = itx.cid();
// here the self-implementated String Vector is little
// fasteer than the list<string>
for (int i = 0; i < N; ++ i) {
cache[i].clear();
}
DEPExtractor::extract2o(inst, hid, cid, cache);
cache_again.clear();
for (int tid = 0; tid < cache.size(); ++ tid) {
for (int itx = 0; itx < cache[tid].size(); ++ itx) {
int idx = model->space.index(FeatureSpace::DEP, tid, cache[tid][itx], 0);
// std::cout << "idx: " << idx << std::endl;
if (idx >= 0) {
cache_again.push_back(idx);
}
}
}
int num_feat = cache_again.size();
if (num_feat > 0) {
if (!model_opt.labeled) {
inst->depu_features[hid][cid] = new FeatureVector;
inst->depu_features[hid][cid]->n = num_feat;
inst->depu_features[hid][cid]->idx = 0;
inst->depu_features[hid][cid]->val = 0;
inst->depu_features[hid][cid]->idx = new int[num_feat];
for (int j = 0; j < num_feat; ++ j) {
inst->depu_features[hid][cid]->idx[j] = cache_again[j];
}
} else {
int l = 0;
int * idx = new int[num_feat];
for (int j = 0; j < num_feat; ++ j) {
idx[j] = cache_again[j];
}
inst->depl_features[hid][cid][l] = new FeatureVector;
inst->depl_features[hid][cid][l]->n = num_feat;
inst->depl_features[hid][cid][l]->val = 0;
inst->depl_features[hid][cid][l]->loff = 0;
inst->depl_features[hid][cid][l]->idx = idx;
for (l = 1; l < L; ++ l) {
inst->depl_features[hid][cid][l] = new FeatureVector;
inst->depl_features[hid][cid][l]->n = num_feat;
inst->depl_features[hid][cid][l]->idx = idx;
inst->depl_features[hid][cid][l]->val = 0;
inst->depl_features[hid][cid][l]->loff = l;
}
}
}
} // end for DEPTreeSpaceIterator itx
} // end for feat_opt.use_dependency
if (feat_opt.use_sibling) {
if (!model_opt.labeled) {
inst->sibu_features.resize(len, len, len);
inst->sibu_scores.resize(len, len, len);
inst->sibu_features = 0;
inst->sibu_scores = DOUBLE_NEG_INF;
} else {
inst->sibl_features.resize(len, len, len, L);
inst->sibl_scores.resize(len, len, len, L);
inst->sibl_features = 0;
inst->sibl_scores = DOUBLE_NEG_INF;
}
int N = SIBExtractor::num_templates();
vector< StringVec > cache;
vector< int > cache_again;
cache.resize(N);
for (treeutils::SIBTreeSpaceIterator itx(len, feat_opt.use_last_sibling); !itx.end(); ++ itx) {
int hid = itx.hid();
int cid = itx.cid();
int sid = itx.sid();
for (int i = 0; i < N; ++ i) {
cache[i].clear();
}
SIBExtractor::extract3o(inst, hid, cid, sid, cache);
cache_again.clear();
//
for (int tid = 0; tid < cache.size(); ++ tid) {
for (int itx = 0; itx < cache[tid].size(); ++ itx) {
int idx = model->space.index(FeatureSpace::SIB, tid, cache[tid][itx]);
if (idx >= 0) {
cache_again.push_back(idx);
}
}
}
int num_feat = cache_again.size();
if (num_feat > 0) {
if (!model_opt.labeled) {
inst->sibu_features[hid][cid][sid] = new FeatureVector;
inst->sibu_features[hid][cid][sid]->n = num_feat;
inst->sibu_features[hid][cid][sid]->idx = 0;
inst->sibu_features[hid][cid][sid]->val = 0;
inst->sibu_features[hid][cid][sid]->idx = new int[num_feat];
for (int j = 0; j < num_feat; ++ j) {
inst->sibu_features[hid][cid][sid]->idx[j] = cache_again[j];
}
} else {
int l = 0;
int * idx = new int[num_feat];
for (int j = 0; j < num_feat; ++ j) {
idx[j] = cache_again[j];
}
inst->sibl_features[hid][cid][sid][l] = new FeatureVector;
inst->sibl_features[hid][cid][sid][l]->n = num_feat;
inst->sibl_features[hid][cid][sid][l]->val = 0;
inst->sibl_features[hid][cid][sid][l]->idx = idx;
inst->sibl_features[hid][cid][sid][l]->loff = 0;
for (l = 1; l < L; ++ l) {
inst->sibl_features[hid][cid][sid][l] = new FeatureVector;
inst->sibl_features[hid][cid][sid][l]->n = num_feat;
inst->sibl_features[hid][cid][sid][l]->val = 0;
inst->sibl_features[hid][cid][sid][l]->idx = idx;
inst->sibl_features[hid][cid][sid][l]->loff = l;
} // end for if model_opt.labeled
}
}
} // end for SIBTreeSpaceIterator itx
} // end for feat_opt.use_sibling
if (feat_opt.use_grand) {
if (!model_opt.labeled) {
inst->grdu_features.resize(len, len, len);
inst->grdu_scores.resize(len, len, len);
inst->grdu_features = 0;
inst->grdu_scores = DOUBLE_NEG_INF;
} else {
inst->grdl_features.resize(len, len, len, L);
inst->grdl_scores.resize(len, len, len, L);
inst->grdl_features = 0;
inst->grdl_scores = DOUBLE_NEG_INF;
}
int N = GRDExtractor::num_templates();
vector< StringVec > cache;
vector< int > cache_again;
cache.resize(N);
for (treeutils::GRDTreeSpaceIterator itx(len, feat_opt.use_no_grand); !itx.end(); ++ itx) {
int hid = itx.hid();
int cid = itx.cid();
int gid = itx.gid();
for (int i = 0; i < N; ++ i) {
cache[i].clear();
}
GRDExtractor::extract3o(inst, hid, cid, gid, cache);
cache_again.clear();
for (int tid = 0; tid < cache.size(); ++ tid) {
for (int itx = 0; itx < cache[tid].size(); ++ itx) {
int idx = model->space.index(FeatureSpace::GRD, tid, cache[tid][itx]);
if (idx >= 0) {
cache_again.push_back(idx);
}
}
}
int num_feat = cache_again.size();
if (num_feat > 0) {
if (!model_opt.labeled) {
inst->grdu_features[hid][cid][gid] = new FeatureVector;
inst->grdu_features[hid][cid][gid]->n = num_feat;
inst->grdu_features[hid][cid][gid]->idx = 0;
inst->grdu_features[hid][cid][gid]->val = 0;
inst->grdu_features[hid][cid][gid]->idx = new int[num_feat];
for (int j = 0; j < num_feat; ++ j) {
inst->grdu_features[hid][cid][gid]->idx[j] = cache_again[j];
}
} else {
int l = 0;
int * idx = new int[num_feat];
for (int j = 0; j < num_feat; ++ j) {
idx[j] = cache_again[j];
}
inst->grdl_features[hid][cid][gid][l] = new FeatureVector;
inst->grdl_features[hid][cid][gid][l]->n = num_feat;
inst->grdl_features[hid][cid][gid][l]->val = 0;
inst->grdl_features[hid][cid][gid][l]->idx = idx;
inst->grdl_features[hid][cid][gid][l]->loff = 0;
for (l = 1; l < L; ++ l) {
inst->grdl_features[hid][cid][gid][l] = new FeatureVector;
inst->grdl_features[hid][cid][gid][l]->n = num_feat;
inst->grdl_features[hid][cid][gid][l]->val = 0;
inst->grdl_features[hid][cid][gid][l]->idx = idx;
inst->grdl_features[hid][cid][gid][l]->loff = l;
} // end for if model_opt.labeled
}
}
}
} // end for feat_opt.use_grand
}
void Parser::extract_features(vector<Instance *>& dat) {
// ofstream fout("lgdpj.fv.tmp", std::ofstream::binary);
// DependencyExtractor
for (int i = 0; i < dat.size(); ++ i) {
extract_features(dat[i]);
// dat[i]->dump_all_featurevec(fout);
if ((i + 1) % model_opt.display_interval == 0) {
TRACE_LOG("[%d] instance is extracted.", i + 1);
}
} // end for i = 0; i < dat.size(); ++ i
// fout.close();
}
void Parser::build_gold_features() {
// ifstream fin("lgdpj.fv.tmp", std::ifstream::binary);
for (int i = 0; i < train_dat.size(); ++ i) {
// train_dat[i]->load_all_featurevec(fin);
collect_features_of_one_instance(train_dat[i], true);
// train_dat[i]->nice_all_featurevec();
}
// fin.close();
}
void Parser::train(void) {
const char * train_file = train_opt.train_file.c_str();
const char * holdout_file = train_opt.holdout_file.c_str();
if (!read_instances(train_file, train_dat)) {
ERROR_LOG("Failed to read train data from [%s].", train_file);
return;
} else {
TRACE_LOG("Read in [%d] train instances.", train_dat.size());
}
model = new Model;
TRACE_LOG("Start building configuration.");
build_configuration();
TRACE_LOG("Building configuration is done.");
TRACE_LOG("Number of postags: [%d]", model->num_postags());
TRACE_LOG("Number of deprels: [%d]", model->num_deprels());
TRACE_LOG("Start building feature space.");
build_feature_space();
TRACE_LOG("Building feature space is done.");
TRACE_LOG("Number of features: [%d]", model->space.num_features());
model->param.realloc(model->dim());
TRACE_LOG("Allocate a parameter vector of [%d] dimension.", model->dim());
decoder= build_decoder();
for (int iter = 0; iter < train_opt.max_iter; ++ iter) {
TRACE_LOG("Start training epoch #%d.", (iter + 1));
// random_shuffle(train_dat.begin(), train_dat.end());
for (int i = 0; i < train_dat.size(); ++ i) {
extract_features(train_dat[i]);
calculate_score(train_dat[i], model->param);
decoder->decode(train_dat[i]);
collect_features_of_one_instance(train_dat[i], true);
collect_features_of_one_instance(train_dat[i], false);
// instance_verify(train_dat[i], cout, true);
if (train_opt.algorithm == "pa") {
SparseVec update_features;
update_features.zero();
update_features.add(train_dat[i]->features, 1.);
update_features.add(train_dat[i]->predicted_features, -1.);
double error = train_dat[i]->num_errors();
double score = model->param.dot(update_features, false);
double norm = update_features.L2();
double step = 0.;
if (norm < EPS) {
step = 0;
} else {
step = (error - score) / norm;
}
model->param.add(update_features,
iter * train_dat.size() + i + 1,
step);
} else if (train_opt.algorithm == "ap") {
SparseVec update_features;
update_features.add(train_dat[i]->features, 1.);
update_features.add(train_dat[i]->predicted_features, -1.);
model->param.add(update_features,
iter * train_dat.size() + i + 1,
1.);
}
if ((i + 1) % model_opt.display_interval == 0) {
TRACE_LOG("[%d] instances is trained.", i + 1);
}
train_dat[i]->cleanup();
}
model->param.flush( train_dat.size() * (iter + 1) );
evaluate();
string saved_model_file = (train_opt.model_name + "." + to_str(iter) + ".model");
ofstream fout(saved_model_file.c_str(), std::ofstream::binary);
model->save(fout);
TRACE_LOG("Model for iteration [%d] is saved to [%s]",
iter + 1,
saved_model_file.c_str());
}
delete model;
model = 0;
}
void Parser::evaluate(void) {
const char * holdout_file = train_opt.holdout_file.c_str();
int head_correct = 0;
int label_correct = 0;
int total_rels = 0;
ifstream f(holdout_file);
CoNLLReader reader(f);
Instance * inst = NULL;
double before = get_time();
while ((inst = reader.next())) {
if (model_opt.labeled) {
inst->deprelsidx.resize(inst->size());
for (int i = 1; i < inst->size(); ++ i) {
inst->deprelsidx[i] = model->deprels.index(inst->deprels[i].c_str());
}
}
extract_features(inst);
calculate_score(inst, model->param, true);
decoder->decode(inst);
total_rels += inst->num_rels();
head_correct += inst->num_correct_heads();
label_correct += inst->num_correct_heads_and_labels();
delete inst;
}
TRACE_LOG("UAS: %.4lf ( %d / %d )",
(double)head_correct / total_rels,
head_correct,
total_rels);
if (model_opt.labeled) {
TRACE_LOG("LAS: %.4lf ( %d / %d )",
(double)label_correct / total_rels,
label_correct,
total_rels);
}
double after = get_time();
TRACE_LOG("consuming time: %.2lf", after - before);
// holdout_dat.clear();
}
void Parser::test() {
double before = get_time();
const char * model_file = test_opt.model_file.c_str();
ifstream mfs(model_file, std::ifstream::binary);
if (!mfs) {
ERROR_LOG("Failed to open file [%s].", model_file);
return;
}
model = new Model;
if (!model->load(mfs)) {
ERROR_LOG("Failed to load model");
return;
}
TRACE_LOG("Number of postags [%d]", model->num_postags());
TRACE_LOG("Number of deprels [%d]", model->num_deprels());
TRACE_LOG("Number of features [%d]", model->num_features());
TRACE_LOG("Number of dimension [%d]", model->dim());
TRACE_LOG("Labeled: %s",
(model_opt.labeled ? "true" : "fales"));
TRACE_LOG("Decoder: %s",
model_opt.decoder_name.c_str());
TRACE_LOG("Dependency features: %s",
(feat_opt.use_dependency ? "true" : "false"));
TRACE_LOG("Dependency features unigram: %s",
(feat_opt.use_dependency_unigram ? "true" : "false"));
TRACE_LOG("Dependency features bigram: %s",
(feat_opt.use_dependency_bigram ? "true" : "false"));
TRACE_LOG("Dependency features surrounding: %s",
(feat_opt.use_dependency_surrounding ? "true" : "false"));
TRACE_LOG("Dependency features between: %s",
(feat_opt.use_dependency_between ? "true" : "false"));
TRACE_LOG("Sibling features: %s",
(feat_opt.use_sibling ? "true" : "false"));
TRACE_LOG("Sibling basic features: %s",
(feat_opt.use_sibling_basic ? "true" : "false"));
TRACE_LOG("Sibling linear features: %s",
(feat_opt.use_sibling_linear ? "true" : "false"));
TRACE_LOG("Grandchild features: %s",
(feat_opt.use_grand ? "true" : "false"));
TRACE_LOG("Grandchild basic features: %s",
(feat_opt.use_grand_basic ? "true" : "false"));
TRACE_LOG("Grandchild linear features: %s",
(feat_opt.use_grand_linear ? "true" : "false"));
const char * test_file = test_opt.test_file.c_str();
ifstream f(test_file);
if (!f) {
ERROR_LOG("Failed to load test file %s", test_file);
return;
}
CoNLLReader reader(f);
CoNLLWriter writer(cout);
Instance * inst = NULL;
build_decoder();
cerr << get_time() - before << endl;
before = get_time();
int head_correct = 0;
int label_correct = 0;
int total_rels = 0;
while ((inst = reader.next())) {
int len = inst->size();
if (model_opt.labeled) {
inst->deprelsidx.resize(len);
for (int i = 1; i < len; ++ i) {
inst->deprelsidx[i] = model->deprels.index(inst->deprels[i].c_str());
}
}
extract_features(inst);
calculate_score(inst, model->param, true);
decoder->decode(inst);
if (model_opt.labeled) {
inst->predicted_deprels.resize(len);
for (int i = 1; i < len; ++ i) {
inst->predicted_deprels[i] = model->deprels.at(inst->predicted_deprelsidx[i]);
}
}
writer.write(inst);
total_rels += inst->num_rels();
head_correct += inst->num_correct_heads();
label_correct += inst->num_correct_heads_and_labels();
delete inst;
}
double after = get_time();
cerr << after - before << endl;
TRACE_LOG("UAS: %.4lf ( %d / %d )",
(double)head_correct / total_rels,
head_correct,
total_rels);
if (model_opt.labeled) {
TRACE_LOG("LAS: %.4lf ( %d / %d )",
(double)label_correct / total_rels,
label_correct,