coco
462 lines
15 KiB
C++
462 lines
15 KiB
C++
// Copyright (C) 2011 Davis E. King (davis@dlib.net)
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// License: Boost Software License See LICENSE.txt for the full license.
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#include <sstream>
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#include <string>
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#include <cstdlib>
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#include <ctime>
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#include "tester.h"
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#include <dlib/svm_threaded.h>
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#include <dlib/rand.h>
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namespace
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{
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using namespace test;
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using namespace dlib;
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using namespace std;
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logger dlog("test.sequence_labeler");
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// ----------------------------------------------------------------------------------------
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const unsigned long num_label_states = 3; // the "hidden" states
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const unsigned long num_sample_states = 3;
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// ----------------------------------------------------------------------------------------
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struct funny_sequence
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{
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std::vector<unsigned long> item;
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unsigned long size() const { return item.size(); }
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};
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funny_sequence make_funny_sequence(const std::vector<unsigned long>& item)
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{
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funny_sequence temp;
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temp.item = item;
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return temp;
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}
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// ----------------------------------------------------------------------------------------
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class feature_extractor
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{
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public:
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typedef funny_sequence sequence_type;
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unsigned long num_features() const
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{
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return num_label_states*num_label_states + num_label_states*num_sample_states;
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}
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unsigned long order() const
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{
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return 1;
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}
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unsigned long num_labels() const
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{
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return num_label_states;
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}
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template <typename feature_setter, typename EXP>
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void get_features (
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feature_setter& set_feature,
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const sequence_type& x,
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const matrix_exp<EXP>& y,
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unsigned long position
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) const
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{
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if (y.size() > 1)
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set_feature(y(1)*num_label_states + y(0));
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set_feature(num_label_states*num_label_states +
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y(0)*num_sample_states + x.item[position]);
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}
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};
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class feature_extractor_partial
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{
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public:
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typedef funny_sequence sequence_type;
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unsigned long num_features() const
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{
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return num_label_states*num_label_states + num_label_states*num_sample_states;
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}
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unsigned long order() const
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{
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return 1;
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}
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unsigned long num_labels() const
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{
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return num_label_states;
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}
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template <typename feature_setter, typename EXP>
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void get_features (
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feature_setter& set_feature,
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const sequence_type& x,
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const matrix_exp<EXP>& y,
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unsigned long position
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) const
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{
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if (y.size() > 1)
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{
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set_feature(y(1)*num_label_states + y(0), 0.5);
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set_feature(y(1)*num_label_states + y(0), 0.5);
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}
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set_feature(num_label_states*num_label_states +
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y(0)*num_sample_states + x.item[position],0.25);
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set_feature(num_label_states*num_label_states +
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y(0)*num_sample_states + x.item[position],0.75);
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}
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};
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bool called_rejct_labeling = false;
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class feature_extractor2
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{
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public:
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typedef funny_sequence sequence_type;
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unsigned long num_features() const
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{
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return num_label_states*num_label_states + num_label_states*num_sample_states;
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}
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unsigned long order() const
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{
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return 1;
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}
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unsigned long num_labels() const
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{
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return num_label_states;
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}
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template <typename EXP>
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bool reject_labeling (
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const sequence_type& ,
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const matrix_exp<EXP>& ,
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unsigned long
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) const
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{
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called_rejct_labeling = true;
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return false;
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}
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template <typename feature_setter, typename EXP>
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void get_features (
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feature_setter& set_feature,
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const sequence_type& x,
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const matrix_exp<EXP>& y,
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unsigned long position
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) const
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{
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if (y.size() > 1)
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set_feature(y(1)*num_label_states + y(0));
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set_feature(num_label_states*num_label_states +
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y(0)*num_sample_states + x.item[position]);
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}
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};
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void serialize(const feature_extractor&, std::ostream&) {}
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void deserialize(feature_extractor&, std::istream&) {}
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void serialize(const feature_extractor2&, std::ostream&) {}
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void deserialize(feature_extractor2&, std::istream&) {}
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// ----------------------------------------------------------------------------------------
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void sample_hmm (
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dlib::rand& rnd,
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const matrix<double>& transition_probabilities,
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const matrix<double>& emission_probabilities,
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unsigned long previous_label,
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unsigned long& next_label,
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unsigned long& next_sample
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)
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/*!
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requires
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- previous_label < transition_probabilities.nr()
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- transition_probabilities.nr() == transition_probabilities.nc()
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- transition_probabilities.nr() == emission_probabilities.nr()
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- The rows of transition_probabilities and emission_probabilities must sum to 1.
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(i.e. sum_cols(transition_probabilities) and sum_cols(emission_probabilities)
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must evaluate to vectors of all 1s.)
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ensures
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- This function randomly samples the HMM defined by transition_probabilities
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and emission_probabilities assuming that the previous hidden state
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was previous_label.
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- The HMM is defined by:
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- P(next_label |previous_label) == transition_probabilities(previous_label, next_label)
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- P(next_sample|next_label) == emission_probabilities (next_label, next_sample)
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- #next_label == the sampled value of the hidden state
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- #next_sample == the sampled value of the observed state
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!*/
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{
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// sample next_label
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double p = rnd.get_random_double();
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for (long c = 0; p >= 0 && c < transition_probabilities.nc(); ++c)
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{
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next_label = c;
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p -= transition_probabilities(previous_label, c);
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}
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// now sample next_sample
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p = rnd.get_random_double();
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for (long c = 0; p >= 0 && c < emission_probabilities.nc(); ++c)
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{
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next_sample = c;
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p -= emission_probabilities(next_label, c);
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}
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}
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// ----------------------------------------------------------------------------------------
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void make_dataset (
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const matrix<double>& transition_probabilities,
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const matrix<double>& emission_probabilities,
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std::vector<funny_sequence>& samples,
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std::vector<std::vector<unsigned long> >& labels,
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unsigned long dataset_size
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)
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/*!
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requires
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- transition_probabilities.nr() == transition_probabilities.nc()
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- transition_probabilities.nr() == emission_probabilities.nr()
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- The rows of transition_probabilities and emission_probabilities must sum to 1.
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(i.e. sum_cols(transition_probabilities) and sum_cols(emission_probabilities)
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must evaluate to vectors of all 1s.)
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ensures
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- This function randomly samples a bunch of sequences from the HMM defined by
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transition_probabilities and emission_probabilities.
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- The HMM is defined by:
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- The probability of transitioning from hidden state H1 to H2
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is given by transition_probabilities(H1,H2).
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- The probability of a hidden state H producing an observed state
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O is given by emission_probabilities(H,O).
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- #samples.size() == labels.size() == dataset_size
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- for all valid i:
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- #labels[i] is a randomly sampled sequence of hidden states from the
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given HMM. #samples[i] is its corresponding randomly sampled sequence
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of observed states.
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!*/
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{
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samples.clear();
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labels.clear();
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dlib::rand rnd;
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// now randomly sample some labeled sequences from our Hidden Markov Model
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for (unsigned long iter = 0; iter < dataset_size; ++iter)
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{
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const unsigned long sequence_size = rnd.get_random_32bit_number()%20+3;
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std::vector<unsigned long> sample(sequence_size);
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std::vector<unsigned long> label(sequence_size);
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unsigned long previous_label = rnd.get_random_32bit_number()%num_label_states;
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for (unsigned long i = 0; i < sample.size(); ++i)
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{
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unsigned long next_label=0, next_sample=0;
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sample_hmm(rnd, transition_probabilities, emission_probabilities,
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previous_label, next_label, next_sample);
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label[i] = next_label;
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sample[i] = next_sample;
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previous_label = next_label;
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}
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samples.push_back(make_funny_sequence(sample));
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labels.push_back(label);
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}
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}
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// ----------------------------------------------------------------------------------------
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template <typename fe_type>
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void do_test()
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{
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called_rejct_labeling = false;
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matrix<double> transition_probabilities(num_label_states, num_label_states);
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transition_probabilities = 0.05, 0.90, 0.05,
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0.05, 0.05, 0.90,
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0.90, 0.05, 0.05;
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matrix<double> emission_probabilities(num_label_states,num_sample_states);
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emission_probabilities = 0.5, 0.5, 0.0,
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0.0, 0.5, 0.5,
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0.5, 0.0, 0.5;
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print_spinner();
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std::vector<funny_sequence> samples;
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std::vector<std::vector<unsigned long> > labels;
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make_dataset(transition_probabilities,emission_probabilities,
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samples, labels, 1000);
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dlog << LINFO << "samples.size(): "<< samples.size();
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// print out some of the randomly sampled sequences
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for (int i = 0; i < 10; ++i)
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{
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dlog << LINFO << "hidden states: " << trans(mat(labels[i]));
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dlog << LINFO << "observed states: " << trans(mat(samples[i].item));
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dlog << LINFO << "******************************";
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}
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print_spinner();
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structural_sequence_labeling_trainer<fe_type> trainer;
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trainer.set_c(4);
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DLIB_TEST(trainer.get_c() == 4);
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trainer.set_num_threads(4);
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DLIB_TEST(trainer.get_num_threads() == 4);
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// Learn to do sequence labeling from the dataset
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sequence_labeler<fe_type> labeler = trainer.train(samples, labels);
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std::vector<unsigned long> predicted_labels = labeler(samples[0]);
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dlog << LINFO << "true hidden states: "<< trans(mat(labels[0]));
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dlog << LINFO << "predicted hidden states: "<< trans(mat(predicted_labels));
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DLIB_TEST(mat(labels[0]) == mat(predicted_labels));
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print_spinner();
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// We can also do cross-validation
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matrix<double> confusion_matrix;
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confusion_matrix = cross_validate_sequence_labeler(trainer, samples, labels, 4);
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dlog << LINFO << "cross-validation: ";
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dlog << LINFO << confusion_matrix;
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double accuracy = sum(diag(confusion_matrix))/sum(confusion_matrix);
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dlog << LINFO << "label accuracy: "<< accuracy;
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DLIB_TEST(std::abs(accuracy - 0.882) < 0.01);
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print_spinner();
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matrix<double,0,1> true_hmm_model_weights = log(join_cols(reshape_to_column_vector(transition_probabilities),
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reshape_to_column_vector(emission_probabilities)));
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sequence_labeler<fe_type> labeler_true(true_hmm_model_weights);
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confusion_matrix = test_sequence_labeler(labeler_true, samples, labels);
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dlog << LINFO << "True HMM model: ";
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dlog << LINFO << confusion_matrix;
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accuracy = sum(diag(confusion_matrix))/sum(confusion_matrix);
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dlog << LINFO << "label accuracy: "<< accuracy;
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DLIB_TEST(std::abs(accuracy - 0.882) < 0.01);
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print_spinner();
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// Finally, the labeler can be serialized to disk just like most dlib objects.
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ostringstream sout;
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serialize(labeler, sout);
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sequence_labeler<fe_type> labeler2;
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// recall from disk
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istringstream sin(sout.str());
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deserialize(labeler2, sin);
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confusion_matrix = test_sequence_labeler(labeler2, samples, labels);
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dlog << LINFO << "deserialized labeler: ";
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dlog << LINFO << confusion_matrix;
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accuracy = sum(diag(confusion_matrix))/sum(confusion_matrix);
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dlog << LINFO << "label accuracy: "<< accuracy;
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DLIB_TEST(std::abs(accuracy - 0.882) < 0.01);
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}
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// ----------------------------------------------------------------------------------------
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void test2()
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{
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/*
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The point of this test is to make sure calling set_feature() multiple
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times works the way it is supposed to.
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*/
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print_spinner();
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std::vector<funny_sequence> samples;
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std::vector<std::vector<unsigned long> > labels;
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matrix<double> transition_probabilities(num_label_states, num_label_states);
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transition_probabilities = 0.05, 0.90, 0.05,
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0.05, 0.05, 0.90,
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0.90, 0.05, 0.05;
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matrix<double> emission_probabilities(num_label_states,num_sample_states);
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emission_probabilities = 0.5, 0.5, 0.0,
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0.0, 0.5, 0.5,
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0.5, 0.0, 0.5;
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make_dataset(transition_probabilities,emission_probabilities,
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samples, labels, 1000);
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dlog << LINFO << "samples.size(): "<< samples.size();
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structural_sequence_labeling_trainer<feature_extractor> trainer;
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structural_sequence_labeling_trainer<feature_extractor_partial> trainer_part;
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trainer.set_c(4);
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trainer_part.set_c(4);
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trainer.set_num_threads(4);
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trainer_part.set_num_threads(4);
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trainer.set_epsilon(1e-8);
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trainer_part.set_epsilon(1e-8);
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// Learn to do sequence labeling from the dataset
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sequence_labeler<feature_extractor> labeler = trainer.train(samples, labels);
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sequence_labeler<feature_extractor_partial> labeler_part = trainer_part.train(samples, labels);
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dlog << LINFO << "weight disagreement: "<< max(abs(labeler.get_weights() - labeler_part.get_weights()));
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dlog << LINFO << "max weight magnitude: "<< max(abs(labeler.get_weights()));
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// Both feature extractors should be equivalent.
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DLIB_TEST(max(abs(labeler.get_weights() - labeler_part.get_weights())) < 1e-6);
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}
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// ----------------------------------------------------------------------------------------
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class sequence_labeler_tester : public tester
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{
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public:
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sequence_labeler_tester (
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) :
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tester ("test_sequence_labeler",
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"Runs tests on the sequence labeling code.")
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{}
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void perform_test (
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)
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{
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do_test<feature_extractor>();
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DLIB_TEST(called_rejct_labeling == false);
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do_test<feature_extractor2>();
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DLIB_TEST(called_rejct_labeling == true);
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test2();
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}
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} a;
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}
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