Fully setup the functional python interface to the sequence segmenter tool.

Need to add documentation next.

tools/python/src/sequence_segmenter.cpp

@@ -12,7 +12,7 @@ using namespace dlib;
 using namespace std;
 using namespace boost::python;
 
-typedef matrix<double,0,1> sample_type; 
+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;
 
@@ -33,7 +33,7 @@ public:
     unsigned long _window_size;
 
     segmenter_feature_extractor(
-    ) : _num_features(0), _window_size(0) {}
+    ) : _num_features(1), _window_size(1) {}
 
     segmenter_feature_extractor(
         unsigned long _num_features_,
@@ -49,7 +49,7 @@ public:
     template <typename feature_setter>
     void get_features (
         feature_setter& set_feature,
-        const std::vector<sample_type>& x,
+        const std::vector<dense_vect>& x,
         unsigned long position
     ) const
     {
@@ -88,17 +88,54 @@ public:
 
 struct segmenter_type
 {
-    segmenter_type() : mode(0)
+    /*!
+        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 (
+    ranges segment_sequence_dense (
-        const std::vector<sample_type>& x
+        const std::vector<dense_vect>& x
     ) const 
     {
-        return ranges();
+        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");
+        }
     }
 
-    const matrix<double,0,1>& get_weights() 
+    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)
         {
@@ -110,6 +147,17 @@ struct segmenter_type
             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");
         }
     }
 
@@ -126,6 +174,16 @@ struct segmenter_type
             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)
@@ -141,19 +199,29 @@ struct segmenter_type
             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<sample_type, true, true, true>  fe0;
+    typedef segmenter_feature_extractor<dense_vect, false,false,false> fe0;
-    typedef segmenter_feature_extractor<sample_type, true, true, false> fe1;
+    typedef segmenter_feature_extractor<dense_vect, false,false,true>  fe1;
-    typedef segmenter_feature_extractor<sample_type, true, false,true>  fe2;
+    typedef segmenter_feature_extractor<dense_vect, false,true, false> fe2;
-    typedef segmenter_feature_extractor<sample_type, true, false,false> fe3;
+    typedef segmenter_feature_extractor<dense_vect, false,true, true>  fe3;
-    typedef segmenter_feature_extractor<sample_type, false,true, true>  fe4;
+    typedef segmenter_feature_extractor<dense_vect, true, false,false> fe4;
-    typedef segmenter_feature_extractor<sample_type, false,true, false> fe5;
+    typedef segmenter_feature_extractor<dense_vect, true, false,true>  fe5;
-    typedef segmenter_feature_extractor<sample_type, false,false,true>  fe6;
+    typedef segmenter_feature_extractor<dense_vect, true, true, false> fe6;
-    typedef segmenter_feature_extractor<sample_type, false,false,false> fe7;
+    typedef segmenter_feature_extractor<dense_vect, true, true, true>  fe7;
     sequence_segmenter<fe0> segmenter0;
     sequence_segmenter<fe1> segmenter1;
     sequence_segmenter<fe2> segmenter2;
@@ -163,14 +231,14 @@ struct segmenter_type
     sequence_segmenter<fe6> segmenter6;
     sequence_segmenter<fe7> segmenter7;
 
-    typedef segmenter_feature_extractor<sparse_vect, true, true, true>  fe8;
+    typedef segmenter_feature_extractor<sparse_vect, false,false,false> fe8;
-    typedef segmenter_feature_extractor<sparse_vect, true, true, false> fe9;
+    typedef segmenter_feature_extractor<sparse_vect, false,false,true>  fe9;
-    typedef segmenter_feature_extractor<sparse_vect, true, false,true>  fe10;
+    typedef segmenter_feature_extractor<sparse_vect, false,true, false> fe10;
-    typedef segmenter_feature_extractor<sparse_vect, true, false,false> fe11;
+    typedef segmenter_feature_extractor<sparse_vect, false,true, true>  fe11;
-    typedef segmenter_feature_extractor<sparse_vect, false,true, true>  fe12;
+    typedef segmenter_feature_extractor<sparse_vect, true, false,false> fe12;
-    typedef segmenter_feature_extractor<sparse_vect, false,true, false> fe13;
+    typedef segmenter_feature_extractor<sparse_vect, true, false,true>  fe13;
-    typedef segmenter_feature_extractor<sparse_vect, false,false,true>  fe14;
+    typedef segmenter_feature_extractor<sparse_vect, true, true, false> fe14;
-    typedef segmenter_feature_extractor<sparse_vect, false,false,false> fe15;
+    typedef segmenter_feature_extractor<sparse_vect, true, true, true>  fe15;
     sequence_segmenter<fe8> segmenter8;
     sequence_segmenter<fe9> segmenter9;
     sequence_segmenter<fe10> segmenter10;
@@ -195,6 +263,7 @@ struct segmenter_params
         num_threads = 4;
         epsilon = 0.1;
         max_cache_size = 40;
+        be_verbose = false;
         C = 100;
     }
 
@@ -209,11 +278,77 @@ struct segmenter_params
     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<sample_type> >& samples,
+    const std::vector<std::vector<dense_vect> >& samples,
     structural_sequence_segmentation_trainer<T>& trainer,
     const segmenter_params& params
 )
@@ -233,8 +368,35 @@ void configure_trainer (
 
 // ----------------------------------------------------------------------------------------
 
+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<sample_type> >& samples,
+    const std::vector<std::vector<dense_vect> >& samples,
     const std::vector<ranges>& segments,
     segmenter_params params
 )
@@ -255,6 +417,7 @@ segmenter_type train_dense (
     else 
         mode = mode*2;
 
+
     segmenter_type res;
     res.mode = mode;
     switch(mode)
@@ -291,6 +454,76 @@ segmenter_type train_dense (
                   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");
     }
 
 
@@ -304,21 +537,27 @@ void bind_sequence_segmenter()
     class_<segmenter_params>("segmenter_params",
 "This class is used to define all the optional parameters to the    \n\
 train_sequence_segmenter() routine.   ")
-        .add_property("use_BIO_model", &segmenter_params::use_BIO_model)
+        .def_readwrite("use_BIO_model", &segmenter_params::use_BIO_model)
-        .add_property("use_high_order_features", &segmenter_params::use_high_order_features)
+        .def_readwrite("use_high_order_features", &segmenter_params::use_high_order_features)
-        .add_property("allow_negative_weights", &segmenter_params::allow_negative_weights)
+        .def_readwrite("allow_negative_weights", &segmenter_params::allow_negative_weights)
-        .add_property("window_size", &segmenter_params::window_size)
+        .def_readwrite("window_size", &segmenter_params::window_size)
-        .add_property("num_threads", &segmenter_params::num_threads)
+        .def_readwrite("num_threads", &segmenter_params::num_threads)
-        .add_property("epsilon", &segmenter_params::epsilon)
+        .def_readwrite("epsilon", &segmenter_params::epsilon)
-        .add_property("max_cache_size", &segmenter_params::max_cache_size)
+        .def_readwrite("max_cache_size", &segmenter_params::max_cache_size)
-        .add_property("C", &segmenter_params::C);
+        .def_readwrite("C", &segmenter_params::C, "SVM C parameter")
+        .def("__repr__",&segmenter_params__repr__)
+        .def("__str__",&segmenter_params__str__)
+        .def_pickle(serialize_pickle<segmenter_params>());
 
     class_<segmenter_type> ("segmenter_type")
-        .def("segment_sequence", &segmenter_type::segment_sequence)
+        .def("segment_sequence", &segmenter_type::segment_sequence_dense)
+        .def("segment_sequence", &segmenter_type::segment_sequence_sparse)
+        .def_readonly("weights", &segmenter_type::get_weights)
         .def_pickle(serialize_pickle<segmenter_type>());
 
     using boost::python::arg;
     def("train_sequence_segmenter", train_dense, (arg("samples"), arg("segments"), arg("params")=segmenter_params()));
+    def("train_sequence_segmenter", train_sparse, (arg("samples"), arg("segments"), arg("params")=segmenter_params()));
 }