path: root/gi/pyp-topics/src/pyp-topics.cc

#include "timing.h"
#include "pyp-topics.hh"
#include "contexts_corpus.hh"

//Dict const *dict;

//#include <boost/date_time/posix_time/posix_time_types.hpp>
void PYPTopics::sample_corpus(const Corpus& corpus, int samples,
                              int freq_cutoff_start, int freq_cutoff_end,
                              int freq_cutoff_interval,
                              int max_contexts_per_document,
                              F temp_start, F temp_end) {
  Timer timer;
  //dict = &((ContextsCorpus*) &corpus)->dict();

  if (!m_backoff.get()) {
    m_word_pyps.clear();
    m_word_pyps.push_back(PYPs());
  }

  std::cerr << "\n Training with " << m_word_pyps.size()-1 << " backoff level"
    << (m_word_pyps.size()==2 ? ":" : "s:") << std::endl;


  for (int i=0; i<(int)m_word_pyps.size(); ++i)
  {
    m_word_pyps.at(i).reserve(m_num_topics);
    for (int j=0; j<m_num_topics; ++j)
      m_word_pyps.at(i).push_back(new PYP<int>(0.01, 1.0, m_seed));
  }
  std::cerr << std::endl;

  m_document_pyps.reserve(corpus.num_documents());
  for (int j=0; j<corpus.num_documents(); ++j)
    m_document_pyps.push_back(new PYP<int>(0.01, 1.0, m_seed));

  m_topic_p0 = 1.0/m_num_topics;
  m_term_p0 = 1.0/(F)m_backoff->terms_at_level(m_word_pyps.size()-1);
  //m_term_p0 = 1.0/corpus.num_types();
  m_backoff_p0 = 1.0/corpus.num_documents();

  std::cerr << " Documents: " << corpus.num_documents() << " Terms: "
    << corpus.num_types() << std::endl;

  int frequency_cutoff = freq_cutoff_start;
  std::cerr << " Context frequency cutoff set to " << frequency_cutoff << std::endl;

  timer.Reset();
  // Initialisation pass
  int document_id=0, topic_counter=0;
  for (Corpus::const_iterator corpusIt=corpus.begin();
       corpusIt != corpus.end(); ++corpusIt, ++document_id) {
    m_corpus_topics.push_back(DocumentTopics(corpusIt->size(), 0));

    int term_index=0;
    for (Document::const_iterator docIt=corpusIt->begin();
         docIt != corpusIt->end(); ++docIt, ++term_index) {
      topic_counter++;
      Term term = *docIt;

      // sample a new_topic
      //int new_topic = (topic_counter % m_num_topics);
      int freq = corpus.context_count(term);
      int new_topic = -1;
      if (freq > frequency_cutoff
          && (!max_contexts_per_document || term_index < max_contexts_per_document)) {
        //new_topic = sample(document_id, term);
        //new_topic = document_id % m_num_topics;
        new_topic = (int) (rnd() * m_num_topics);

        // add the new topic to the PYPs
        increment(term, new_topic);

        if (m_use_topic_pyp) {
          F p0 = m_topic_pyp.prob(new_topic, m_topic_p0);
          int table_delta = m_document_pyps[document_id].increment(new_topic, p0);
          if (table_delta)
            m_topic_pyp.increment(new_topic, m_topic_p0);
        }
        else m_document_pyps[document_id].increment(new_topic, m_topic_p0);
      }

      m_corpus_topics[document_id][term_index] = new_topic;
    }
  }
  std::cerr << "  Initialized in " << timer.Elapsed() << " seconds\n";

  int* randomDocIndices = new int[corpus.num_documents()];
  for (int i = 0; i < corpus.num_documents(); ++i)
	  randomDocIndices[i] = i;

  if (num_jobs < max_threads)
    num_jobs = max_threads;
  int job_incr = (int) ( (float)m_document_pyps.size() / float(num_jobs) );

  // Sampling phase
  for (int curr_sample=0; curr_sample < samples; ++curr_sample) {
    if (freq_cutoff_interval > 0 && curr_sample != 1
        && curr_sample % freq_cutoff_interval == 1
        && frequency_cutoff > freq_cutoff_end) {
      frequency_cutoff--;
      std::cerr << "\n Context frequency cutoff set to " << frequency_cutoff << std::endl;
    }

    F temp = 1.0 / (temp_start - curr_sample*(temp_start-temp_end)/samples);
    std::cerr << "\n  -- Sample " << curr_sample << " (T=" << temp << ") "; std::cerr.flush();

    // Randomize the corpus indexing array
    int tmp;
    int processed_terms=0;
    /*
    for (int i = corpus.num_documents()-1; i > 0; --i)
    {
        //i+1 since j \in [0,i] but rnd() \in [0,1)
    	int j = (int)(rnd() * (i+1));
      assert(j >= 0 && j <= i);
     	tmp = randomDocIndices[i];
    	randomDocIndices[i] = randomDocIndices[j];
    	randomDocIndices[j] = tmp;
    }
    */

    // for each document in the corpus
    int document_id;
    for (int i=0; i<corpus.num_documents(); ++i) {
    	document_id = randomDocIndices[i];

      // for each term in the document
      int term_index=0;
      Document::const_iterator docEnd = corpus.at(document_id).end();
      for (Document::const_iterator docIt=corpus.at(document_id).begin();
           docIt != docEnd; ++docIt, ++term_index) {
        if (max_contexts_per_document && term_index > max_contexts_per_document)
          break;
        
        Term term = *docIt;

        int freq = corpus.context_count(term);
        if (freq < frequency_cutoff)
          continue;

        processed_terms++;

        // remove the prevous topic from the PYPs
        int current_topic = m_corpus_topics[document_id][term_index];
        // a negative label mean that term hasn't been sampled yet
        if (current_topic >= 0) {
          decrement(term, current_topic);

          int table_delta = m_document_pyps[document_id].decrement(current_topic);
          if (m_use_topic_pyp && table_delta < 0)
            m_topic_pyp.decrement(current_topic);
        }

        // sample a new_topic
        int new_topic = sample(document_id, term, temp);
        //std::cerr << "TERM: " << dict->Convert(term) << " (" << term << ") " << " Old Topic: " 
        //  << current_topic << " New Topic: " << new_topic << "\n" << std::endl;

        // add the new topic to the PYPs
        m_corpus_topics[document_id][term_index] = new_topic;
        increment(term, new_topic);

        if (m_use_topic_pyp) {
          F p0 = m_topic_pyp.prob(new_topic, m_topic_p0);
          int table_delta = m_document_pyps[document_id].increment(new_topic, p0);
          if (table_delta)
            m_topic_pyp.increment(new_topic, m_topic_p0);
        }
        else m_document_pyps[document_id].increment(new_topic, m_topic_p0);
      }
      if (document_id && document_id % 10000 == 0) {
        std::cerr << "."; std::cerr.flush();
      }
    }
    std::cerr << " ||| LLH= " << log_likelihood();

    if (curr_sample != 0 && curr_sample % 10 == 0) {
    //if (true) {
      std::cerr << " ||| time=" << (timer.Elapsed() / 10.0) << " sec/sample" << std::endl;
      timer.Reset();
      std::cerr << "     ... Resampling hyperparameters (";
      
      // resample the hyperparamters
      F log_p=0.0;
      if (max_threads == 1)
      { 
        std::cerr << "1 thread)" << std::endl; std::cerr.flush();
        log_p += hresample_topics();
        log_p += hresample_docs(0, m_document_pyps.size());
      }
      else
      { //parallelize
        std::cerr << max_threads << " threads, " << num_jobs << " jobs)" << std::endl; std::cerr.flush();
        
        WorkerPool<JobReturnsF, F> pool(max_threads); 
        int i=0, sz = m_document_pyps.size();
        //documents...
        while (i <= sz - 2*job_incr)
        {    
          JobReturnsF job = boost::bind(&PYPTopics::hresample_docs, this, i, i+job_incr);
          pool.addJob(job);
          i += job_incr;
        }
        //  do all remaining documents
        JobReturnsF job = boost::bind(&PYPTopics::hresample_docs, this, i,sz);
        pool.addJob(job);
        
        //topics...
        JobReturnsF topics_job = boost::bind(&PYPTopics::hresample_topics, this);
        pool.addJob(topics_job);

        log_p += pool.get_result(); //blocks

      }

      if (m_use_topic_pyp) {
        m_topic_pyp.resample_prior(rnd);
        log_p += m_topic_pyp.log_restaurant_prob();
      }

      std::cerr.precision(10);
      std::cerr << " ||| LLH=" << log_likelihood() << " ||| resampling time=" << timer.Elapsed() << " sec" << std::endl;
      timer.Reset();

      int k=0;
      std::cerr << "Topics distribution: ";
      std::cerr.precision(2);
      for (PYPs::iterator pypIt=m_word_pyps.front().begin();
           pypIt != m_word_pyps.front().end(); ++pypIt, ++k) {
        if (k % 5 == 0) std::cerr << std::endl << '\t';
        std::cerr << "<" << k << ":" << pypIt->num_customers() << ","
          << pypIt->num_types() << "," << m_topic_pyp.prob(k, m_topic_p0) << "> ";
      }
      std::cerr.precision(10);
      std::cerr << std::endl;
    }
  }
  delete [] randomDocIndices;
}

PYPTopics::F PYPTopics::hresample_docs(int start, int end)
{
  int resample_counter=0;
  F log_p = 0.0;
  assert(start >= 0);
  assert(end >= 0);
  assert(start <= end);
  for (int i=start; i < end; ++i)
  {
    m_document_pyps[i].resample_prior(rnd);
    log_p += m_document_pyps[i].log_restaurant_prob();
    if (resample_counter++ % 5000 == 0) {
      std::cerr << "."; std::cerr.flush();
    }
  }
  return log_p;
}

PYPTopics::F PYPTopics::hresample_topics()
{
  F log_p = 0.0;
  for (std::vector<PYPs>::iterator levelIt=m_word_pyps.begin();
      levelIt != m_word_pyps.end(); ++levelIt) {
    for (PYPs::iterator pypIt=levelIt->begin();
        pypIt != levelIt->end(); ++pypIt) {

      pypIt->resample_prior(rnd);
      log_p += pypIt->log_restaurant_prob();
    }
    std::cerr << log_p << std::endl;
  }
  return log_p;
}

PYPTopics::F PYPTopics::log_likelihood() const 
{
  F log_p = 0.0;

  // LLH of topic term distribution
  size_t i=0;
  for (std::vector<PYPs>::const_iterator levelIt=m_word_pyps.begin();
      levelIt != m_word_pyps.end(); ++levelIt, ++i) {
    for (PYPs::const_iterator pypIt=levelIt->begin();
        pypIt != levelIt->end(); ++pypIt, ++i) {
      log_p += pypIt->log_restaurant_prob();

      if (i == m_word_pyps.size()-1)
        log_p += (pypIt->num_tables() * -log(m_backoff->terms_at_level(i)));
      else
        log_p += (pypIt->num_tables() * log(m_term_p0));
    }
  }
  std::cerr << " TERM LLH: " << log_p << " "; //std::endl;

  // LLH of document topic distribution
  for (size_t i=0; i < m_document_pyps.size(); ++i) {
    log_p += m_document_pyps[i].log_restaurant_prob();
    if (!m_use_topic_pyp) log_p += (m_document_pyps[i].num_tables() * m_topic_p0);
  }
  if (m_use_topic_pyp) {
    log_p += m_topic_pyp.log_restaurant_prob();
    log_p += (m_topic_pyp.num_tables() * log(m_topic_p0));
  }

  return log_p;
}

void PYPTopics::decrement(const Term& term, int topic, int level) {
  //std::cerr << "PYPTopics::decrement(" << term << "," << topic << "," << level << ")" << std::endl;
  int table_delta = m_word_pyps.at(level).at(topic).decrement(term);
  if (table_delta && m_backoff.get()) {
    Term backoff_term = (*m_backoff)[term];
    if (!m_backoff->is_null(backoff_term))
      decrement(backoff_term, topic, level+1);
  }
}

void PYPTopics::increment(const Term& term, int topic, int level) {
  //std::cerr << "PYPTopics::increment(" << term << "," << topic << "," << level << ")" << std::endl;
  int table_delta = m_word_pyps.at(level).at(topic).increment(term, word_pyps_p0(term, topic, level));

  if (table_delta && m_backoff.get()) {
    Term backoff_term = (*m_backoff)[term];
    if (!m_backoff->is_null(backoff_term))
      increment(backoff_term, topic, level+1);
  }
}

int PYPTopics::sample(const DocumentId& doc, const Term& term, F inv_temp) {
  // First pass: collect probs
  F sum=0.0;
  std::vector<F> sums;
  for (int k=0; k<m_num_topics; ++k) {
    F p_w_k = prob(term, k);

    F topic_prob = m_topic_p0;
    if (m_use_topic_pyp) topic_prob = m_topic_pyp.prob(k, m_topic_p0);

    //F p_k_d = m_document_pyps[doc].prob(k, topic_prob);
    F p_k_d = m_document_pyps[doc].unnormalised_prob(k, topic_prob);

    F prob = p_w_k*p_k_d;
    /*
    if (prob < 0.0) { std::cerr << "\n\n" << prob << " " << p_w_k << " " << p_k_d << std::endl; assert(false); }
    if (prob > 1.0) { std::cerr << "\n\n" << prob << " " << p_w_k << " " << p_k_d << std::endl; assert(false); }
    assert (pow(prob, inv_temp) >= 0.0);
    assert (pow(prob, inv_temp) <= 1.0);
    */
    sum += pow(prob, inv_temp);
    sums.push_back(sum);
  }
  // Second pass: sample a topic
  F cutoff = rnd() * sum;
  for (int k=0; k<m_num_topics; ++k) {
    if (cutoff <= sums[k])
      return k;
  }
  assert(false);
}

PYPTopics::F PYPTopics::word_pyps_p0(const Term& term, int topic, int level) const {
  //for (int i=0; i<level+1; ++i) std::cerr << "  ";
  //std::cerr << "PYPTopics::word_pyps_p0(" << term << "," << topic << "," << level << ")" << std::endl;

  F p0 = m_term_p0;
  if (m_backoff.get()) {
    //static F fudge=m_backoff_p0; // TODO

    Term backoff_term = (*m_backoff)[term];
    //std::cerr << "T: " << term << " BO: " << backoff_term << std::endl;
    if (!m_backoff->is_null(backoff_term)) {
      assert (level < m_backoff->order());
      //p0 = (1.0/(F)m_backoff->terms_at_level(level))*prob(backoff_term, topic, level+1);
      p0 = m_term_p0*prob(backoff_term, topic, level+1);
      p0 = prob(backoff_term, topic, level+1);
    }
    else
      p0 = (1.0/(F) m_backoff->terms_at_level(level));
      //p0 = m_term_p0;
  }
  //for (int i=0; i<level+1; ++i) std::cerr << "  ";
  //std::cerr << "PYPTopics::word_pyps_p0(" << term << "," << topic << "," << level << ") = " << p0 << std::endl;
  return p0;
}

PYPTopics::F PYPTopics::prob(const Term& term, int topic, int level) const {
  //for (int i=0; i<level+1; ++i) std::cerr << "  ";
  //std::cerr << "PYPTopics::prob(" << dict->Convert(term) << "," << topic << "," << level << ")" << std::endl;

  F p0 = word_pyps_p0(term, topic, level);
  F p_w_k = m_word_pyps.at(level).at(topic).prob(term, p0);

  /*
  for (int i=0; i<level+1; ++i) std::cerr << "  ";
  std::cerr << "PYPTopics::prob(" << dict->Convert(term) << "," << topic << "," << level << ") = " << p_w_k << std::endl;
  for (int i=0; i<level+1; ++i) std::cerr << "  ";
  m_word_pyps.at(level).at(topic).debug_info(std::cerr);
  */
  return p_w_k;
}

int PYPTopics::max_topic() const {
  if (!m_use_topic_pyp)
    return -1;

  F current_max=0.0;
  int current_topic=-1;
  for (int k=0; k<m_num_topics; ++k) {
    F prob = m_topic_pyp.prob(k, m_topic_p0);
    if (prob > current_max) {
      current_max = prob;
      current_topic = k;
    }
  }
  assert(current_topic >= 0);
  return current_topic;
}

std::pair<int,PYPTopics::F> PYPTopics::max(const DocumentId& doc) const {
  //std::cerr << "PYPTopics::max(" << doc << "," << term << ")" << std::endl;
  // collect probs
  F current_max=0.0;
  int current_topic=-1;
  for (int k=0; k<m_num_topics; ++k) {
    //F p_w_k = prob(term, k);

    F topic_prob = m_topic_p0;
    if (m_use_topic_pyp)
      topic_prob = m_topic_pyp.prob(k, m_topic_p0);

    F prob = 0;
    if (doc < 0) prob = topic_prob;
    else         prob = m_document_pyps[doc].prob(k, topic_prob);

    if (prob > current_max) {
      current_max = prob;
      current_topic = k;
    }
  }
  assert(current_topic >= 0);
  assert(current_max >= 0);
  return std::make_pair(current_topic, current_max);
}

std::pair<int,PYPTopics::F> PYPTopics::max(const DocumentId& doc, const Term& term) const {
  //std::cerr << "PYPTopics::max(" << doc << "," << term << ")" << std::endl;
  // collect probs
  F current_max=0.0;
  int current_topic=-1;
  for (int k=0; k<m_num_topics; ++k) {
    F p_w_k = prob(term, k);

    F topic_prob = m_topic_p0;
    if (m_use_topic_pyp)
      topic_prob = m_topic_pyp.prob(k, m_topic_p0);

    F p_k_d = 0;
    if (doc < 0) p_k_d = topic_prob;
    else         p_k_d = m_document_pyps[doc].prob(k, topic_prob);

    F prob = (p_w_k*p_k_d);
    if (prob > current_max) {
      current_max = prob;
      current_topic = k;
    }
  }
  assert(current_topic >= 0);
  assert(current_max >= 0);
  return std::make_pair(current_topic,current_max);
}

std::ostream& PYPTopics::print_document_topics(std::ostream& out) const {
  for (CorpusTopics::const_iterator corpusIt=m_corpus_topics.begin();
       corpusIt != m_corpus_topics.end(); ++corpusIt) {
    int term_index=0;
    for (DocumentTopics::const_iterator docIt=corpusIt->begin();
         docIt != corpusIt->end(); ++docIt, ++term_index) {
      if (term_index) out << " ";
      out << *docIt;
    }
    out << std::endl;
  }
  return out;
}

std::ostream& PYPTopics::print_topic_terms(std::ostream& out) const {
  for (PYPs::const_iterator pypsIt=m_word_pyps.front().begin();
       pypsIt != m_word_pyps.front().end(); ++pypsIt) {
    int term_index=0;
    for (PYP<int>::const_iterator termIt=pypsIt->begin();
         termIt != pypsIt->end(); ++termIt, ++term_index) {
      if (term_index) out << " ";
      out << termIt->first << ":" << termIt->second;
    }
    out << std::endl;
  }
  return out;
}