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Diffstat (limited to 'ml/dlib/tools/python/src/sequence_segmenter.cpp')
| -rw-r--r-- | ml/dlib/tools/python/src/sequence_segmenter.cpp | 827 |
1 files changed, 0 insertions, 827 deletions
diff --git a/ml/dlib/tools/python/src/sequence_segmenter.cpp b/ml/dlib/tools/python/src/sequence_segmenter.cpp deleted file mode 100644 index 9fde1e771..000000000 --- a/ml/dlib/tools/python/src/sequence_segmenter.cpp +++ /dev/null @@ -1,827 +0,0 @@ -// Copyright (C) 2013 Davis E. King (davis@dlib.net) -// License: Boost Software License See LICENSE.txt for the full license. - -#include "opaque_types.h" -#include <dlib/python.h> -#include <dlib/matrix.h> -#include <dlib/svm_threaded.h> - -using namespace dlib; -using namespace std; -namespace py = pybind11; - -typedef matrix<double,0,1> dense_vect; -typedef std::vector<std::pair<unsigned long,double> > sparse_vect; -typedef std::vector<std::pair<unsigned long, unsigned long> > ranges; - -// ---------------------------------------------------------------------------------------- - -template <typename samp_type, bool BIO, bool high_order, bool nonnegative> -class segmenter_feature_extractor -{ - -public: - typedef std::vector<samp_type> sequence_type; - const static bool use_BIO_model = BIO; - const static bool use_high_order_features = high_order; - const static bool allow_negative_weights = nonnegative; - - - unsigned long _num_features; - unsigned long _window_size; - - segmenter_feature_extractor( - ) : _num_features(1), _window_size(1) {} - - segmenter_feature_extractor( - unsigned long _num_features_, - unsigned long _window_size_ - ) : _num_features(_num_features_), _window_size(_window_size_) {} - - unsigned long num_features( - ) const { return _num_features; } - - unsigned long window_size( - ) const {return _window_size; } - - template <typename feature_setter> - void get_features ( - feature_setter& set_feature, - const std::vector<dense_vect>& x, - unsigned long position - ) const - { - for (long i = 0; i < x[position].size(); ++i) - { - set_feature(i, x[position](i)); - } - } - - template <typename feature_setter> - void get_features ( - feature_setter& set_feature, - const std::vector<sparse_vect>& x, - unsigned long position - ) const - { - for (unsigned long i = 0; i < x[position].size(); ++i) - { - set_feature(x[position][i].first, x[position][i].second); - } - } - - friend void serialize(const segmenter_feature_extractor& item, std::ostream& out) - { - dlib::serialize(item._num_features, out); - dlib::serialize(item._window_size, out); - } - friend void deserialize(segmenter_feature_extractor& item, std::istream& in) - { - dlib::deserialize(item._num_features, in); - dlib::deserialize(item._window_size, in); - } -}; - -// ---------------------------------------------------------------------------------------- - -struct segmenter_type -{ - /*! - WHAT THIS OBJECT REPRESENTS - This the object that python will use directly to represent a - sequence_segmenter. All it does is contain all the possible template - instantiations of a sequence_segmenter and invoke the right one depending on - the mode variable. - !*/ - - segmenter_type() : mode(-1) - { } - - ranges segment_sequence_dense ( - const std::vector<dense_vect>& x - ) const - { - switch (mode) - { - case 0: return segmenter0(x); - case 1: return segmenter1(x); - case 2: return segmenter2(x); - case 3: return segmenter3(x); - case 4: return segmenter4(x); - case 5: return segmenter5(x); - case 6: return segmenter6(x); - case 7: return segmenter7(x); - default: throw dlib::error("Invalid mode"); - } - } - - ranges segment_sequence_sparse ( - const std::vector<sparse_vect>& x - ) const - { - switch (mode) - { - case 8: return segmenter8(x); - case 9: return segmenter9(x); - case 10: return segmenter10(x); - case 11: return segmenter11(x); - case 12: return segmenter12(x); - case 13: return segmenter13(x); - case 14: return segmenter14(x); - case 15: return segmenter15(x); - default: throw dlib::error("Invalid mode"); - } - } - - const matrix<double,0,1> get_weights() - { - switch(mode) - { - case 0: return segmenter0.get_weights(); - case 1: return segmenter1.get_weights(); - case 2: return segmenter2.get_weights(); - case 3: return segmenter3.get_weights(); - case 4: return segmenter4.get_weights(); - case 5: return segmenter5.get_weights(); - case 6: return segmenter6.get_weights(); - case 7: return segmenter7.get_weights(); - - case 8: return segmenter8.get_weights(); - case 9: return segmenter9.get_weights(); - case 10: return segmenter10.get_weights(); - case 11: return segmenter11.get_weights(); - case 12: return segmenter12.get_weights(); - case 13: return segmenter13.get_weights(); - case 14: return segmenter14.get_weights(); - case 15: return segmenter15.get_weights(); - - default: throw dlib::error("Invalid mode"); - } - } - - friend void serialize (const segmenter_type& item, std::ostream& out) - { - serialize(item.mode, out); - switch(item.mode) - { - case 0: serialize(item.segmenter0, out); break; - case 1: serialize(item.segmenter1, out); break; - case 2: serialize(item.segmenter2, out); break; - case 3: serialize(item.segmenter3, out); break; - case 4: serialize(item.segmenter4, out); break; - case 5: serialize(item.segmenter5, out); break; - case 6: serialize(item.segmenter6, out); break; - case 7: serialize(item.segmenter7, out); break; - - case 8: serialize(item.segmenter8, out); break; - case 9: serialize(item.segmenter9, out); break; - case 10: serialize(item.segmenter10, out); break; - case 11: serialize(item.segmenter11, out); break; - case 12: serialize(item.segmenter12, out); break; - case 13: serialize(item.segmenter13, out); break; - case 14: serialize(item.segmenter14, out); break; - case 15: serialize(item.segmenter15, out); break; - default: throw dlib::error("Invalid mode"); - } - } - friend void deserialize (segmenter_type& item, std::istream& in) - { - deserialize(item.mode, in); - switch(item.mode) - { - case 0: deserialize(item.segmenter0, in); break; - case 1: deserialize(item.segmenter1, in); break; - case 2: deserialize(item.segmenter2, in); break; - case 3: deserialize(item.segmenter3, in); break; - case 4: deserialize(item.segmenter4, in); break; - case 5: deserialize(item.segmenter5, in); break; - case 6: deserialize(item.segmenter6, in); break; - case 7: deserialize(item.segmenter7, in); break; - - case 8: deserialize(item.segmenter8, in); break; - case 9: deserialize(item.segmenter9, in); break; - case 10: deserialize(item.segmenter10, in); break; - case 11: deserialize(item.segmenter11, in); break; - case 12: deserialize(item.segmenter12, in); break; - case 13: deserialize(item.segmenter13, in); break; - case 14: deserialize(item.segmenter14, in); break; - case 15: deserialize(item.segmenter15, in); break; - default: throw dlib::error("Invalid mode"); - } - } - - int mode; - - typedef segmenter_feature_extractor<dense_vect, false,false,false> fe0; - typedef segmenter_feature_extractor<dense_vect, false,false,true> fe1; - typedef segmenter_feature_extractor<dense_vect, false,true, false> fe2; - typedef segmenter_feature_extractor<dense_vect, false,true, true> fe3; - typedef segmenter_feature_extractor<dense_vect, true, false,false> fe4; - typedef segmenter_feature_extractor<dense_vect, true, false,true> fe5; - typedef segmenter_feature_extractor<dense_vect, true, true, false> fe6; - typedef segmenter_feature_extractor<dense_vect, true, true, true> fe7; - sequence_segmenter<fe0> segmenter0; - sequence_segmenter<fe1> segmenter1; - sequence_segmenter<fe2> segmenter2; - sequence_segmenter<fe3> segmenter3; - sequence_segmenter<fe4> segmenter4; - sequence_segmenter<fe5> segmenter5; - sequence_segmenter<fe6> segmenter6; - sequence_segmenter<fe7> segmenter7; - - typedef segmenter_feature_extractor<sparse_vect, false,false,false> fe8; - typedef segmenter_feature_extractor<sparse_vect, false,false,true> fe9; - typedef segmenter_feature_extractor<sparse_vect, false,true, false> fe10; - typedef segmenter_feature_extractor<sparse_vect, false,true, true> fe11; - typedef segmenter_feature_extractor<sparse_vect, true, false,false> fe12; - typedef segmenter_feature_extractor<sparse_vect, true, false,true> fe13; - typedef segmenter_feature_extractor<sparse_vect, true, true, false> fe14; - typedef segmenter_feature_extractor<sparse_vect, true, true, true> fe15; - sequence_segmenter<fe8> segmenter8; - sequence_segmenter<fe9> segmenter9; - sequence_segmenter<fe10> segmenter10; - sequence_segmenter<fe11> segmenter11; - sequence_segmenter<fe12> segmenter12; - sequence_segmenter<fe13> segmenter13; - sequence_segmenter<fe14> segmenter14; - sequence_segmenter<fe15> segmenter15; -}; - - -// ---------------------------------------------------------------------------------------- - -struct segmenter_params -{ - segmenter_params() - { - use_BIO_model = true; - use_high_order_features = true; - allow_negative_weights = true; - window_size = 5; - num_threads = 4; - epsilon = 0.1; - max_cache_size = 40; - be_verbose = false; - C = 100; - } - - bool use_BIO_model; - bool use_high_order_features; - bool allow_negative_weights; - unsigned long window_size; - unsigned long num_threads; - double epsilon; - unsigned long max_cache_size; - bool be_verbose; - double C; -}; - - -string segmenter_params__str__(const segmenter_params& p) -{ - ostringstream sout; - if (p.use_BIO_model) - sout << "BIO,"; - else - sout << "BILOU,"; - - if (p.use_high_order_features) - sout << "highFeats,"; - else - sout << "lowFeats,"; - - if (p.allow_negative_weights) - sout << "signed,"; - else - sout << "non-negative,"; - - sout << "win="<<p.window_size << ","; - sout << "threads="<<p.num_threads << ","; - sout << "eps="<<p.epsilon << ","; - sout << "cache="<<p.max_cache_size << ","; - if (p.be_verbose) - sout << "verbose,"; - else - sout << "non-verbose,"; - sout << "C="<<p.C; - return trim(sout.str()); -} - -string segmenter_params__repr__(const segmenter_params& p) -{ - ostringstream sout; - sout << "<"; - sout << segmenter_params__str__(p); - sout << ">"; - return sout.str(); -} - -void serialize ( const segmenter_params& item, std::ostream& out) -{ - serialize(item.use_BIO_model, out); - serialize(item.use_high_order_features, out); - serialize(item.allow_negative_weights, out); - serialize(item.window_size, out); - serialize(item.num_threads, out); - serialize(item.epsilon, out); - serialize(item.max_cache_size, out); - serialize(item.be_verbose, out); - serialize(item.C, out); -} - -void deserialize (segmenter_params& item, std::istream& in) -{ - deserialize(item.use_BIO_model, in); - deserialize(item.use_high_order_features, in); - deserialize(item.allow_negative_weights, in); - deserialize(item.window_size, in); - deserialize(item.num_threads, in); - deserialize(item.epsilon, in); - deserialize(item.max_cache_size, in); - deserialize(item.be_verbose, in); - deserialize(item.C, in); -} - -// ---------------------------------------------------------------------------------------- - -template <typename T> -void configure_trainer ( - const std::vector<std::vector<dense_vect> >& samples, - structural_sequence_segmentation_trainer<T>& trainer, - const segmenter_params& params -) -{ - pyassert(samples.size() != 0, "Invalid arguments. You must give some training sequences."); - pyassert(samples[0].size() != 0, "Invalid arguments. You can't have zero length training sequences."); - pyassert(params.window_size != 0, "Invalid window_size parameter, it must be > 0."); - pyassert(params.epsilon > 0, "Invalid epsilon parameter, it must be > 0."); - pyassert(params.C > 0, "Invalid C parameter, it must be > 0."); - const long dims = samples[0][0].size(); - - trainer = structural_sequence_segmentation_trainer<T>(T(dims, params.window_size)); - trainer.set_num_threads(params.num_threads); - trainer.set_epsilon(params.epsilon); - trainer.set_max_cache_size(params.max_cache_size); - trainer.set_c(params.C); - if (params.be_verbose) - trainer.be_verbose(); -} - -// ---------------------------------------------------------------------------------------- - -template <typename T> -void configure_trainer ( - const std::vector<std::vector<sparse_vect> >& samples, - structural_sequence_segmentation_trainer<T>& trainer, - const segmenter_params& params -) -{ - pyassert(samples.size() != 0, "Invalid arguments. You must give some training sequences."); - pyassert(samples[0].size() != 0, "Invalid arguments. You can't have zero length training sequences."); - - unsigned long dims = 0; - for (unsigned long i = 0; i < samples.size(); ++i) - { - dims = std::max(dims, max_index_plus_one(samples[i])); - } - - trainer = structural_sequence_segmentation_trainer<T>(T(dims, params.window_size)); - trainer.set_num_threads(params.num_threads); - trainer.set_epsilon(params.epsilon); - trainer.set_max_cache_size(params.max_cache_size); - trainer.set_c(params.C); - if (params.be_verbose) - trainer.be_verbose(); -} - -// ---------------------------------------------------------------------------------------- - -segmenter_type train_dense ( - const std::vector<std::vector<dense_vect> >& samples, - const std::vector<ranges>& segments, - segmenter_params params -) -{ - pyassert(is_sequence_segmentation_problem(samples, segments), "Invalid inputs"); - - int mode = 0; - if (params.use_BIO_model) - mode = mode*2 + 1; - else - mode = mode*2; - if (params.use_high_order_features) - mode = mode*2 + 1; - else - mode = mode*2; - if (params.allow_negative_weights) - mode = mode*2 + 1; - else - mode = mode*2; - - - segmenter_type res; - res.mode = mode; - switch(mode) - { - case 0: { structural_sequence_segmentation_trainer<segmenter_type::fe0> trainer; - configure_trainer(samples, trainer, params); - res.segmenter0 = trainer.train(samples, segments); - } break; - case 1: { structural_sequence_segmentation_trainer<segmenter_type::fe1> trainer; - configure_trainer(samples, trainer, params); - res.segmenter1 = trainer.train(samples, segments); - } break; - case 2: { structural_sequence_segmentation_trainer<segmenter_type::fe2> trainer; - configure_trainer(samples, trainer, params); - res.segmenter2 = trainer.train(samples, segments); - } break; - case 3: { structural_sequence_segmentation_trainer<segmenter_type::fe3> trainer; - configure_trainer(samples, trainer, params); - res.segmenter3 = trainer.train(samples, segments); - } break; - case 4: { structural_sequence_segmentation_trainer<segmenter_type::fe4> trainer; - configure_trainer(samples, trainer, params); - res.segmenter4 = trainer.train(samples, segments); - } break; - case 5: { structural_sequence_segmentation_trainer<segmenter_type::fe5> trainer; - configure_trainer(samples, trainer, params); - res.segmenter5 = trainer.train(samples, segments); - } break; - case 6: { structural_sequence_segmentation_trainer<segmenter_type::fe6> trainer; - configure_trainer(samples, trainer, params); - res.segmenter6 = trainer.train(samples, segments); - } break; - case 7: { structural_sequence_segmentation_trainer<segmenter_type::fe7> trainer; - configure_trainer(samples, trainer, params); - res.segmenter7 = trainer.train(samples, segments); - } break; - default: throw dlib::error("Invalid mode"); - } - - - return res; -} - -// ---------------------------------------------------------------------------------------- - -segmenter_type train_sparse ( - const std::vector<std::vector<sparse_vect> >& samples, - const std::vector<ranges>& segments, - segmenter_params params -) -{ - pyassert(is_sequence_segmentation_problem(samples, segments), "Invalid inputs"); - - int mode = 0; - if (params.use_BIO_model) - mode = mode*2 + 1; - else - mode = mode*2; - if (params.use_high_order_features) - mode = mode*2 + 1; - else - mode = mode*2; - if (params.allow_negative_weights) - mode = mode*2 + 1; - else - mode = mode*2; - - mode += 8; - - segmenter_type res; - res.mode = mode; - switch(mode) - { - case 8: { structural_sequence_segmentation_trainer<segmenter_type::fe8> trainer; - configure_trainer(samples, trainer, params); - res.segmenter8 = trainer.train(samples, segments); - } break; - case 9: { structural_sequence_segmentation_trainer<segmenter_type::fe9> trainer; - configure_trainer(samples, trainer, params); - res.segmenter9 = trainer.train(samples, segments); - } break; - case 10: { structural_sequence_segmentation_trainer<segmenter_type::fe10> trainer; - configure_trainer(samples, trainer, params); - res.segmenter10 = trainer.train(samples, segments); - } break; - case 11: { structural_sequence_segmentation_trainer<segmenter_type::fe11> trainer; - configure_trainer(samples, trainer, params); - res.segmenter11 = trainer.train(samples, segments); - } break; - case 12: { structural_sequence_segmentation_trainer<segmenter_type::fe12> trainer; - configure_trainer(samples, trainer, params); - res.segmenter12 = trainer.train(samples, segments); - } break; - case 13: { structural_sequence_segmentation_trainer<segmenter_type::fe13> trainer; - configure_trainer(samples, trainer, params); - res.segmenter13 = trainer.train(samples, segments); - } break; - case 14: { structural_sequence_segmentation_trainer<segmenter_type::fe14> trainer; - configure_trainer(samples, trainer, params); - res.segmenter14 = trainer.train(samples, segments); - } break; - case 15: { structural_sequence_segmentation_trainer<segmenter_type::fe15> trainer; - configure_trainer(samples, trainer, params); - res.segmenter15 = trainer.train(samples, segments); - } break; - default: throw dlib::error("Invalid mode"); - } - - - return res; -} - -// ---------------------------------------------------------------------------------------- - - -struct segmenter_test -{ - double precision; - double recall; - double f1; -}; - -void serialize(const segmenter_test& item, std::ostream& out) -{ - serialize(item.precision, out); - serialize(item.recall, out); - serialize(item.f1, out); -} - -void deserialize(segmenter_test& item, std::istream& in) -{ - deserialize(item.precision, in); - deserialize(item.recall, in); - deserialize(item.f1, in); -} - -std::string segmenter_test__str__(const segmenter_test& item) -{ - std::ostringstream sout; - sout << "precision: "<< item.precision << " recall: "<< item.recall << " f1-score: " << item.f1; - return sout.str(); -} -std::string segmenter_test__repr__(const segmenter_test& item) { return "< " + segmenter_test__str__(item) + " >";} - -// ---------------------------------------------------------------------------------------- - -const segmenter_test test_sequence_segmenter1 ( - const segmenter_type& segmenter, - const std::vector<std::vector<dense_vect> >& samples, - const std::vector<ranges>& segments -) -{ - pyassert(is_sequence_segmentation_problem(samples, segments), "Invalid inputs"); - matrix<double,1,3> res; - - switch(segmenter.mode) - { - case 0: res = test_sequence_segmenter(segmenter.segmenter0, samples, segments); break; - case 1: res = test_sequence_segmenter(segmenter.segmenter1, samples, segments); break; - case 2: res = test_sequence_segmenter(segmenter.segmenter2, samples, segments); break; - case 3: res = test_sequence_segmenter(segmenter.segmenter3, samples, segments); break; - case 4: res = test_sequence_segmenter(segmenter.segmenter4, samples, segments); break; - case 5: res = test_sequence_segmenter(segmenter.segmenter5, samples, segments); break; - case 6: res = test_sequence_segmenter(segmenter.segmenter6, samples, segments); break; - case 7: res = test_sequence_segmenter(segmenter.segmenter7, samples, segments); break; - default: throw dlib::error("Invalid mode"); - } - - - segmenter_test temp; - temp.precision = res(0); - temp.recall = res(1); - temp.f1 = res(2); - return temp; -} - -const segmenter_test test_sequence_segmenter2 ( - const segmenter_type& segmenter, - const std::vector<std::vector<sparse_vect> >& samples, - const std::vector<ranges>& segments -) -{ - pyassert(is_sequence_segmentation_problem(samples, segments), "Invalid inputs"); - matrix<double,1,3> res; - - switch(segmenter.mode) - { - case 8: res = test_sequence_segmenter(segmenter.segmenter8, samples, segments); break; - case 9: res = test_sequence_segmenter(segmenter.segmenter9, samples, segments); break; - case 10: res = test_sequence_segmenter(segmenter.segmenter10, samples, segments); break; - case 11: res = test_sequence_segmenter(segmenter.segmenter11, samples, segments); break; - case 12: res = test_sequence_segmenter(segmenter.segmenter12, samples, segments); break; - case 13: res = test_sequence_segmenter(segmenter.segmenter13, samples, segments); break; - case 14: res = test_sequence_segmenter(segmenter.segmenter14, samples, segments); break; - case 15: res = test_sequence_segmenter(segmenter.segmenter15, samples, segments); break; - default: throw dlib::error("Invalid mode"); - } - - - segmenter_test temp; - temp.precision = res(0); - temp.recall = res(1); - temp.f1 = res(2); - return temp; -} - -// ---------------------------------------------------------------------------------------- - -const segmenter_test cross_validate_sequence_segmenter1 ( - const std::vector<std::vector<dense_vect> >& samples, - const std::vector<ranges>& segments, - long folds, - segmenter_params params -) -{ - pyassert(is_sequence_segmentation_problem(samples, segments), "Invalid inputs"); - pyassert(1 < folds && folds <= static_cast<long>(samples.size()), "folds argument is outside the valid range."); - - matrix<double,1,3> res; - - int mode = 0; - if (params.use_BIO_model) - mode = mode*2 + 1; - else - mode = mode*2; - if (params.use_high_order_features) - mode = mode*2 + 1; - else - mode = mode*2; - if (params.allow_negative_weights) - mode = mode*2 + 1; - else - mode = mode*2; - - - switch(mode) - { - case 0: { structural_sequence_segmentation_trainer<segmenter_type::fe0> trainer; - configure_trainer(samples, trainer, params); - res = cross_validate_sequence_segmenter(trainer, samples, segments, folds); - } break; - case 1: { structural_sequence_segmentation_trainer<segmenter_type::fe1> trainer; - configure_trainer(samples, trainer, params); - res = cross_validate_sequence_segmenter(trainer, samples, segments, folds); - } break; - case 2: { structural_sequence_segmentation_trainer<segmenter_type::fe2> trainer; - configure_trainer(samples, trainer, params); - res = cross_validate_sequence_segmenter(trainer, samples, segments, folds); - } break; - case 3: { structural_sequence_segmentation_trainer<segmenter_type::fe3> trainer; - configure_trainer(samples, trainer, params); - res = cross_validate_sequence_segmenter(trainer, samples, segments, folds); - } break; - case 4: { structural_sequence_segmentation_trainer<segmenter_type::fe4> trainer; - configure_trainer(samples, trainer, params); - res = cross_validate_sequence_segmenter(trainer, samples, segments, folds); - } break; - case 5: { structural_sequence_segmentation_trainer<segmenter_type::fe5> trainer; - configure_trainer(samples, trainer, params); - res = cross_validate_sequence_segmenter(trainer, samples, segments, folds); - } break; - case 6: { structural_sequence_segmentation_trainer<segmenter_type::fe6> trainer; - configure_trainer(samples, trainer, params); - res = cross_validate_sequence_segmenter(trainer, samples, segments, folds); - } break; - case 7: { structural_sequence_segmentation_trainer<segmenter_type::fe7> trainer; - configure_trainer(samples, trainer, params); - res = cross_validate_sequence_segmenter(trainer, samples, segments, folds); - } break; - default: throw dlib::error("Invalid mode"); - } - - - segmenter_test temp; - temp.precision = res(0); - temp.recall = res(1); - temp.f1 = res(2); - return temp; -} - -const segmenter_test cross_validate_sequence_segmenter2 ( - const std::vector<std::vector<sparse_vect> >& samples, - const std::vector<ranges>& segments, - long folds, - segmenter_params params -) -{ - pyassert(is_sequence_segmentation_problem(samples, segments), "Invalid inputs"); - pyassert(1 < folds && folds <= static_cast<long>(samples.size()), "folds argument is outside the valid range."); - - matrix<double,1,3> res; - - int mode = 0; - if (params.use_BIO_model) - mode = mode*2 + 1; - else - mode = mode*2; - if (params.use_high_order_features) - mode = mode*2 + 1; - else - mode = mode*2; - if (params.allow_negative_weights) - mode = mode*2 + 1; - else - mode = mode*2; - - mode += 8; - - switch(mode) - { - case 8: { structural_sequence_segmentation_trainer<segmenter_type::fe8> trainer; - configure_trainer(samples, trainer, params); - res = cross_validate_sequence_segmenter(trainer, samples, segments, folds); - } break; - case 9: { structural_sequence_segmentation_trainer<segmenter_type::fe9> trainer; - configure_trainer(samples, trainer, params); - res = cross_validate_sequence_segmenter(trainer, samples, segments, folds); - } break; - case 10: { structural_sequence_segmentation_trainer<segmenter_type::fe10> trainer; - configure_trainer(samples, trainer, params); - res = cross_validate_sequence_segmenter(trainer, samples, segments, folds); - } break; - case 11: { structural_sequence_segmentation_trainer<segmenter_type::fe11> trainer; - configure_trainer(samples, trainer, params); - res = cross_validate_sequence_segmenter(trainer, samples, segments, folds); - } break; - case 12: { structural_sequence_segmentation_trainer<segmenter_type::fe12> trainer; - configure_trainer(samples, trainer, params); - res = cross_validate_sequence_segmenter(trainer, samples, segments, folds); - } break; - case 13: { structural_sequence_segmentation_trainer<segmenter_type::fe13> trainer; - configure_trainer(samples, trainer, params); - res = cross_validate_sequence_segmenter(trainer, samples, segments, folds); - } break; - case 14: { structural_sequence_segmentation_trainer<segmenter_type::fe14> trainer; - configure_trainer(samples, trainer, params); - res = cross_validate_sequence_segmenter(trainer, samples, segments, folds); - } break; - case 15: { structural_sequence_segmentation_trainer<segmenter_type::fe15> trainer; - configure_trainer(samples, trainer, params); - res = cross_validate_sequence_segmenter(trainer, samples, segments, folds); - } break; - default: throw dlib::error("Invalid mode"); - } - - - segmenter_test temp; - temp.precision = res(0); - temp.recall = res(1); - temp.f1 = res(2); - return temp; -} - -// ---------------------------------------------------------------------------------------- - -void bind_sequence_segmenter(py::module& m) -{ - py::class_<segmenter_params>(m, "segmenter_params", -"This class is used to define all the optional parameters to the \n\ -train_sequence_segmenter() and cross_validate_sequence_segmenter() routines. ") - .def(py::init<>()) - .def_readwrite("use_BIO_model", &segmenter_params::use_BIO_model) - .def_readwrite("use_high_order_features", &segmenter_params::use_high_order_features) - .def_readwrite("allow_negative_weights", &segmenter_params::allow_negative_weights) - .def_readwrite("window_size", &segmenter_params::window_size) - .def_readwrite("num_threads", &segmenter_params::num_threads) - .def_readwrite("epsilon", &segmenter_params::epsilon) - .def_readwrite("max_cache_size", &segmenter_params::max_cache_size) - .def_readwrite("C", &segmenter_params::C, "SVM C parameter") - .def_readwrite("be_verbose", &segmenter_params::be_verbose) - .def("__repr__",&segmenter_params__repr__) - .def("__str__",&segmenter_params__str__) - .def(py::pickle(&getstate<segmenter_params>, &setstate<segmenter_params>)); - - py::class_<segmenter_type> (m, "segmenter_type", "This object represents a sequence segmenter and is the type of object " - "returned by the dlib.train_sequence_segmenter() routine.") - .def("__call__", &segmenter_type::segment_sequence_dense) - .def("__call__", &segmenter_type::segment_sequence_sparse) - .def_property_readonly("weights", &segmenter_type::get_weights) - .def(py::pickle(&getstate<segmenter_type>, &setstate<segmenter_type>)); - - py::class_<segmenter_test> (m, "segmenter_test", "This object is the output of the dlib.test_sequence_segmenter() and " - "dlib.cross_validate_sequence_segmenter() routines.") - .def_readwrite("precision", &segmenter_test::precision) - .def_readwrite("recall", &segmenter_test::recall) - .def_readwrite("f1", &segmenter_test::f1) - .def("__repr__",&segmenter_test__repr__) - .def("__str__",&segmenter_test__str__) - .def(py::pickle(&getstate<segmenter_test>, &setstate<segmenter_test>)); - - m.def("train_sequence_segmenter", train_dense, py::arg("samples"), py::arg("segments"), py::arg("params")=segmenter_params()); - m.def("train_sequence_segmenter", train_sparse, py::arg("samples"), py::arg("segments"), py::arg("params")=segmenter_params()); - - - m.def("test_sequence_segmenter", test_sequence_segmenter1); - m.def("test_sequence_segmenter", test_sequence_segmenter2); - - m.def("cross_validate_sequence_segmenter", cross_validate_sequence_segmenter1, - py::arg("samples"), py::arg("segments"), py::arg("folds"), py::arg("params")=segmenter_params()); - m.def("cross_validate_sequence_segmenter", cross_validate_sequence_segmenter2, - py::arg("samples"), py::arg("segments"), py::arg("folds"), py::arg("params")=segmenter_params()); -} - - - - |