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Attempt at fixing sparse features for hgmira
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@ -180,7 +180,7 @@ HypergraphHopeFearDecoder::HypergraphHopeFearDecoder
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references_.Load(referenceFiles, vocab_);
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SparseVector weights;
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wv.ToSparse(&weights);
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wv.ToSparse(&weights,num_dense_);
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scorer_ = scorer;
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static const string kWeights = "weights";
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@ -243,7 +243,7 @@ void HypergraphHopeFearDecoder::HopeFear(
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{
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size_t sentenceId = *sentenceIdIter_;
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SparseVector weights;
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wv.ToSparse(&weights);
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wv.ToSparse(&weights, num_dense_);
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const Graph& graph = *(graphs_[sentenceId]);
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// ValType hope_scale = 1.0;
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@ -338,7 +338,7 @@ void HypergraphHopeFearDecoder::MaxModel(const AvgWeightVector& wv, vector<ValTy
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HgHypothesis bestHypo;
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size_t sentenceId = *sentenceIdIter_;
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SparseVector weights;
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wv.ToSparse(&weights);
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wv.ToSparse(&weights, num_dense_);
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vector<ValType> bg(scorer_->NumberOfScores());
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//cerr << "Calculating bleu on " << sentenceId << endl;
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Viterbi(*(graphs_[sentenceId]), weights, 0, references_, sentenceId, bg, &bestHypo);
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@ -77,6 +77,7 @@ unit-test feature_data_test : FeatureDataTest.cpp mert_lib ..//boost_unit_test_f
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unit-test data_test : DataTest.cpp mert_lib ..//boost_unit_test_framework ;
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unit-test forest_rescore_test : ForestRescoreTest.cpp mert_lib ..//boost_unit_test_framework ;
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unit-test hypergraph_test : HypergraphTest.cpp mert_lib ..//boost_unit_test_framework ;
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unit-test mira_feature_vector_test : MiraFeatureVectorTest.cpp mert_lib ..//boost_unit_test_framework ;
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unit-test ngram_test : NgramTest.cpp mert_lib ..//boost_unit_test_framework ;
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unit-test optimizer_factory_test : OptimizerFactoryTest.cpp mert_lib ..//boost_unit_test_framework ;
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unit-test point_test : PointTest.cpp mert_lib ..//boost_unit_test_framework ;
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49
mert/MiraFeatureVectorTest.cpp
Normal file
49
mert/MiraFeatureVectorTest.cpp
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@ -0,0 +1,49 @@
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#include "MiraFeatureVector.h"
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#include "MiraWeightVector.h"
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#define BOOST_TEST_MODULE MiraFeatureVector
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#include <boost/test/unit_test.hpp>
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using namespace MosesTuning;
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/* Note that the conversion to and from SparseVector needs to know
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how many of the features are really "dense". This is because in hg mira
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all features (sparse and dense) are to get rolled in to SparseVector
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*/
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BOOST_AUTO_TEST_CASE(from_sparse) {
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SparseVector sp;
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sp.set("dense0", 0.2);
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sp.set("dense1", 0.3);
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sp.set("sparse0", 0.7);
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sp.set("sparse1", 0.9);
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sp.set("sparse2", 0.1);
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MiraFeatureVector mfv(sp,2);
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BOOST_CHECK_EQUAL(mfv.size(),5);
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BOOST_CHECK_EQUAL(mfv.feat(0),0);
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BOOST_CHECK_EQUAL(mfv.feat(1),1);
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BOOST_CHECK_EQUAL(mfv.feat(2),4);
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BOOST_CHECK_EQUAL(mfv.feat(3),5);
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BOOST_CHECK_EQUAL(mfv.feat(4),6);
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BOOST_CHECK_CLOSE(mfv.val(0), 0.2,1e-5);
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BOOST_CHECK_CLOSE(mfv.val(1), 0.3,1e-5);
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BOOST_CHECK_CLOSE(mfv.val(2), 0.7,1e-5);
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BOOST_CHECK_CLOSE(mfv.val(3), 0.9,1e-5);
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BOOST_CHECK_CLOSE(mfv.val(4), 0.1,1e-5);
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MiraWeightVector mwv;
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mwv.update(mfv,1.0);
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SparseVector sp2;
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mwv.ToSparse(&sp2,2);
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//check we get back what we started with
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BOOST_CHECK_CLOSE(sp2.get("dense0"), 0.2,1e-5);
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BOOST_CHECK_CLOSE(sp2.get("dense1"), 0.3,1e-5);
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BOOST_CHECK_CLOSE(sp2.get("sparse0"), 0.7,1e-5);
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BOOST_CHECK_CLOSE(sp2.get("sparse1"), 0.9,1e-5);
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BOOST_CHECK_CLOSE(sp2.get("sparse2"), 0.1,1e-5);
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}
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@ -93,11 +93,17 @@ void MiraWeightVector::update(size_t index, ValType delta)
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m_lastUpdated[index] = m_numUpdates;
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}
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void MiraWeightVector::ToSparse(SparseVector* sparse) const
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void MiraWeightVector::ToSparse(SparseVector* sparse, size_t denseSize) const
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{
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for (size_t i = 0; i < m_weights.size(); ++i) {
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if(abs(m_weights[i])>1e-8) {
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sparse->set(i,m_weights[i]);
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if (i < denseSize) {
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sparse->set(i,m_weights[i]);
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} else {
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//The ids in MiraFeatureVector/MiraWeightVector for sparse features
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//need to be translated when converting back to SparseVector.
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sparse->set(i-denseSize, m_weights[i]);
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}
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}
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}
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}
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@ -172,12 +178,18 @@ size_t AvgWeightVector::size() const
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return m_wv.m_weights.size();
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}
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void AvgWeightVector::ToSparse(SparseVector* sparse) const
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void AvgWeightVector::ToSparse(SparseVector* sparse, size_t denseSize) const
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{
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for (size_t i = 0; i < size(); ++i) {
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ValType w = weight(i);
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if(abs(w)>1e-8) {
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sparse->set(i,w);
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if (i < denseSize) {
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sparse->set(i,w);
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} else {
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//The ids in MiraFeatureVector/MiraWeightVector for sparse features
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//need to be translated when converting back to SparseVector.
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sparse->set(i-denseSize, w);
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}
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}
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}
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}
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@ -64,9 +64,9 @@ public:
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AvgWeightVector avg();
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/**
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* Convert to sparse vector, interpreting all features as sparse.
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* Convert to sparse vector, interpreting all features as sparse. Only used by hgmira.
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**/
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void ToSparse(SparseVector* sparse) const;
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void ToSparse(SparseVector* sparse, size_t denseSize) const;
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friend class AvgWeightVector;
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@ -104,7 +104,7 @@ public:
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ValType score(const MiraFeatureVector& fv) const;
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ValType weight(std::size_t index) const;
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std::size_t size() const;
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void ToSparse(SparseVector* sparse) const;
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void ToSparse(SparseVector* sparse, size_t num_dense) const;
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private:
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const MiraWeightVector& m_wv;
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};
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