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// Copyright 2008 Google Inc.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
/*
An example running of this program:
./lda \
--num_topics 2 \
--alpha 0.1 \
--beta 0.01 \
--training_data_file ./testdata/test_data.txt \
--model_file /tmp/lda_model.txt \
--burn_in_iterations 100 \
--total_iterations 150
*/
#include <fstream>
#include <set>
#include <sstream>
#include <string>
#include <map>
#include <time.h>
#include "common.h"
#include "document.h"
#include "model.h"
#include "accumulative_model.h"
#include "sampler.h"
#include "cmd_flags.h"
namespace learning_lda {
using std::ifstream;
using std::ofstream;
using std::ostream;
using std::istringstream;
using std::set;
using std::map;
void OutputAssignments(LDACorpus* corpus,vector<string>& index_word_map,string file_out1,string file_out2) {
ofstream out1(file_out1.c_str());
ofstream out2(file_out2.c_str());
for (list<LDADocument*>::iterator iter = corpus->begin();
iter != corpus->end();
++iter) {
const vector<int64> topic_distributions_vec = (*iter)->topic_distribution();
out1 << (*iter)->document_name();
for (int topic = 0; topic < topic_distributions_vec.size(); ++topic) {
out1 << " " << topic_distributions_vec[topic];
}
out1 << "\n";
out2 << (*iter)->document_name();
vector<pair<int,int> > word_assign;
(*iter)->word_assignments(word_assign);
for (int index = 0; index < word_assign.size(); ++index) {
out2 << " " << index_word_map[word_assign[index].first]<< ":" << word_assign[index].second;
}
out2 << "\n";
}
}
int LoadAndInitTrainingCorpus(const string& corpus_file,
int file_type,
int num_topics,
LDACorpus* corpus,
map<string, int>* word_index_map) {
corpus->clear();
ifstream fin(corpus_file.c_str());
string line;
while (getline(fin, line)) { // Each line is a training document.
if (line.size() > 0 && // Skip empty lines.
line[0] != '\r' && // Skip empty lines.
line[0] != '\n' && // Skip empty lines.
line[0] != '#') { // Skip comment lines.
istringstream ss(line);
string doc_name;
ss >> doc_name;
DocumentWordTopicsPB document;
string word;
int count;
while (ss >> word ) { // Load and init a document.
if (0==file_type)
{
ss >> count;
}
else
{
count = 1;
}
vector<int32> topics;
for (int i = 0; i < count; ++i) {
topics.push_back(RandInt(num_topics));
}
int word_index;
map<string, int>::const_iterator iter = word_index_map->find(word);
if (iter == word_index_map->end()) {
// word_index = word_index_map->size();
// (*word_index_map)[word] = word_index;
continue;
} else {
word_index = iter->second;
}
document.add_wordtopics(word, word_index, topics, true);
}
corpus->push_back(new LDADocument(doc_name,document, num_topics));
}
}
return corpus->size();
}
void FreeCorpus(LDACorpus* corpus) {
for (list<LDADocument*>::iterator iter = corpus->begin();
iter != corpus->end();
++iter) {
if (*iter != NULL) {
delete *iter;
*iter = NULL;
}
}
}
} // namespace learning_lda
int main(int argc, char** argv) {
using learning_lda::LDACorpus;
using learning_lda::LDAModel;
using learning_lda::LDAAccumulativeModel;
using learning_lda::LDASampler;
using learning_lda::LDATopicSplitRule;
using learning_lda::LDADocument;
using learning_lda::LoadWordIndex;
using learning_lda::LoadWordSet;
using learning_lda::LoadWordLex;
using learning_lda::LoadAndInitTrainingCorpus;
using learning_lda::OutputAssignments;
using learning_lda::generate_model_name;
using learning_lda::LDACmdLineFlags;
using std::ifstream;
using std::ofstream;
using std::list;
LDACmdLineFlags flags;
flags.ParseCmdFlags(argc, argv);
// if (!flags.CheckTrainingValidity()) {
// return -1;
// }
srand(time(NULL));
map<string, int> word_index_map;
ifstream model_fin(flags.model_file_.c_str());
LDAModel model(model_fin, &word_index_map);
set<int> new_words;
if (flags.new_words_file_.length() > 0 )
{
ifstream newwords_fin(flags.new_words_file_.c_str());
LoadWordSet(newwords_fin,word_index_map,new_words);
}
vector<string> index_word_map(word_index_map.size());
CHECK_GT(LoadWordLex(word_index_map,index_word_map), 0);
LDAModel new_model(flags.num_topics_, word_index_map);
LDASampler new_sampler(flags.alpha_, flags.beta_, &new_model, NULL);
LDATopicSplitRule rule;
ifstream rule_fin(flags.rule_file_.c_str());
rule.LoadSplitRule(rule_fin ,word_index_map);
LDACorpus corpus;
CHECK_GT(LoadAndInitTrainingCorpus(flags.training_data_file_,
flags.file_type_,
model.num_topics(),
&corpus, &word_index_map), 0);
LDASampler sampler(flags.alpha_, flags.beta_, &model, NULL);
for (int iter = 0; iter < flags.burn_in_iterations_; ++iter) {
std::cout << "Iteration " << iter << " ...\n";
sampler.DoIteration(&corpus, false, iter < flags.burn_in_iterations_);
}
sampler.AdjustCorpusWithRule(corpus,flags.num_topics_ ,new_words,rule);
new_sampler.InitModelGivenTopics(corpus);
for (int iter = 0; iter < flags.total_iterations_; ++iter) {
std::cout << "Iteration " << iter << " ...\n";
if (flags.compute_likelihood_ == "true") {
double loglikelihood = 0;
for (list<LDADocument*>::const_iterator iterator = corpus.begin();
iterator != corpus.end();
++iterator) {
loglikelihood += new_sampler.LogLikelihood(*iterator);
}
std::cout << "Loglikelihood: " << loglikelihood << std::endl;
}
new_sampler.DoIteration2(&corpus, true, iter < flags.burn_in_iterations_);
if (flags.save_step_ > 0) {
if (iter % flags.save_step_ == 0) {
// saving the model
printf("Saving the Assignments File at iteration %d ...\n", iter);
string file_out1 = generate_model_name(flags.topic_distribution_file_, 0, iter);
string file_out2 = generate_model_name(flags.topic_assignments_file_, 0, iter);
OutputAssignments(&corpus,index_word_map,file_out1,file_out2);
}
}
}
string file_out1 = generate_model_name(flags.topic_distribution_file_, 0, -1);
string file_out2 = generate_model_name(flags.topic_assignments_file_, 0, -1);
OutputAssignments(&corpus,index_word_map,file_out1,file_out2);
FreeCorpus(&corpus);
string file_out3 = generate_model_name(flags.model_file_, 0, -1);
std::ofstream fout(file_out3.c_str());
new_model.AppendAsString(fout);
return 0;
}