diff options
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, 827 insertions, 0 deletions
diff --git a/ml/dlib/tools/python/src/sequence_segmenter.cpp b/ml/dlib/tools/python/src/sequence_segmenter.cpp new file mode 100644 index 000000000..9fde1e771 --- /dev/null +++ b/ml/dlib/tools/python/src/sequence_segmenter.cpp @@ -0,0 +1,827 @@ +// 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()); +} + + + + |