From 94e233126283d18a474260e9791401e8b3b486d5 Mon Sep 17 00:00:00 2001 From: philblunsom Date: Mon, 23 Aug 2010 18:19:53 +0000 Subject: fixed llh and changed to random initialiser. git-svn-id: https://ws10smt.googlecode.com/svn/trunk@614 ec762483-ff6d-05da-a07a-a48fb63a330f --- gi/pyp-topics/src/pyp-topics.cc | 103 ++++++++++++++++++++++++++++++++-------- 1 file changed, 82 insertions(+), 21 deletions(-) (limited to 'gi/pyp-topics/src/pyp-topics.cc') diff --git a/gi/pyp-topics/src/pyp-topics.cc b/gi/pyp-topics/src/pyp-topics.cc index 4c777f0c..16cc9588 100644 --- a/gi/pyp-topics/src/pyp-topics.cc +++ b/gi/pyp-topics/src/pyp-topics.cc @@ -1,12 +1,17 @@ #include "timing.h" #include "pyp-topics.hh" +#include "contexts_corpus.hh" + +//Dict const *dict; //#include 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) { + 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(); @@ -21,16 +26,17 @@ void PYPTopics::sample_corpus(const Corpus& corpus, int samples, { m_word_pyps.at(i).reserve(m_num_topics); for (int j=0; j(0.5, 1.0, m_seed)); + m_word_pyps.at(i).push_back(new PYP(0.01, 1.0, m_seed)); } std::cerr << std::endl; m_document_pyps.reserve(corpus.num_documents()); for (int j=0; j(0.5, 1.0, m_seed)); + m_document_pyps.push_back(new PYP(0.01, 1.0, m_seed)); m_topic_p0 = 1.0/m_num_topics; - m_term_p0 = 1.0/corpus.num_types(); + 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: " @@ -58,8 +64,9 @@ void PYPTopics::sample_corpus(const Corpus& corpus, int samples, 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 = 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); @@ -95,11 +102,13 @@ void PYPTopics::sample_corpus(const Corpus& corpus, int samples, std::cerr << "\n Context frequency cutoff set to " << frequency_cutoff << std::endl; } - std::cerr << "\n -- Sample " << curr_sample << " "; std::cerr.flush(); + 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) @@ -109,6 +118,7 @@ void PYPTopics::sample_corpus(const Corpus& corpus, int samples, randomDocIndices[i] = randomDocIndices[j]; randomDocIndices[j] = tmp; } + */ // for each document in the corpus int document_id; @@ -124,6 +134,7 @@ void PYPTopics::sample_corpus(const Corpus& corpus, int samples, break; Term term = *docIt; + int freq = corpus.context_count(term); if (freq < frequency_cutoff) continue; @@ -142,7 +153,9 @@ void PYPTopics::sample_corpus(const Corpus& corpus, int samples, } // sample a new_topic - int new_topic = sample(document_id, term); + 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; @@ -160,9 +173,10 @@ void PYPTopics::sample_corpus(const Corpus& corpus, int samples, std::cerr << "."; std::cerr.flush(); } } - std::cerr << " ||| sampled " << processed_terms << " terms."; + 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 ("; @@ -201,12 +215,12 @@ void PYPTopics::sample_corpus(const Corpus& corpus, int samples, } if (m_use_topic_pyp) { - m_topic_pyp.resample_prior(rnd); + //m_topic_pyp.resample_prior(rnd); log_p += m_topic_pyp.log_restaurant_prob(); } std::cerr.precision(10); - std::cerr << " ||| LLH=" << log_p << " ||| resampling time=" << timer.Elapsed() << " sec" << std::endl; + std::cerr << " ||| LLH=" << log_likelihood() << " ||| resampling time=" << timer.Elapsed() << " sec" << std::endl; timer.Reset(); int k=0; @@ -218,7 +232,7 @@ void PYPTopics::sample_corpus(const Corpus& corpus, int samples, std::cerr << "<" << k << ":" << pypIt->num_customers() << "," << pypIt->num_types() << "," << m_topic_pyp.prob(k, m_topic_p0) << "> "; } - std::cerr.precision(4); + std::cerr.precision(10); std::cerr << std::endl; } } @@ -234,7 +248,7 @@ PYPTopics::F PYPTopics::hresample_docs(int start, int end) assert(start <= end); for (int i=start; i < end; ++i) { - m_document_pyps[i].resample_prior(rnd); + //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(); @@ -251,13 +265,47 @@ PYPTopics::F PYPTopics::hresample_topics() for (PYPs::iterator pypIt=levelIt->begin(); pypIt != levelIt->end(); ++pypIt) { - pypIt->resample_prior(rnd); + //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::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); @@ -279,7 +327,7 @@ void PYPTopics::increment(const Term& term, int topic, int level) { } } -int PYPTopics::sample(const DocumentId& doc, const Term& term) { +int PYPTopics::sample(const DocumentId& doc, const Term& term, F inv_temp) { // First pass: collect probs F sum=0.0; std::vector sums; @@ -292,7 +340,14 @@ int PYPTopics::sample(const DocumentId& doc, const Term& term) { //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); - sum += (p_w_k*p_k_d); + 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 @@ -313,13 +368,16 @@ PYPTopics::F PYPTopics::word_pyps_p0(const Term& term, int topic, int level) con //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/(double)m_backoff->terms_at_level(level))*prob(backoff_term, topic, level+1); + //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 = m_term_p0; + p0 = (1.0/(F) m_backoff->terms_at_level(level)); + //p0 = m_term_p0; } //for (int i=0; iConvert(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; iConvert(term) << "," << topic << "," << level << ") = " << p_w_k << std::endl; + for (int i=0; i