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Diffstat (limited to 'ml/dlib/examples/sequence_labeler_ex.cpp')
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diff --git a/ml/dlib/examples/sequence_labeler_ex.cpp b/ml/dlib/examples/sequence_labeler_ex.cpp deleted file mode 100644 index bdb666a7e..000000000 --- a/ml/dlib/examples/sequence_labeler_ex.cpp +++ /dev/null @@ -1,392 +0,0 @@ -// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt -/* - - This is an example illustrating the use of the machine learning - tools for sequence labeling in the dlib C++ Library. - - The general problem addressed by these tools is the following. - Suppose you have a set of sequences of some kind and you want to - learn to predict a label for each element of a sequence. So for - example, you might have a set of English sentences where each - word is labeled with its part of speech and you want to learn a - model which can predict the part of speech for each word in a new - sentence. - - Central to these tools is the sequence_labeler object. It is the - object which represents the label prediction model. In particular, - the model used by this object is the following. Given an input - sequence x, predict an output label sequence y such that: - y == argmax_y dot(weight_vector, PSI(x,y)) - where PSI() is supplied by the user and defines the form of the - model. In this example program we will define it such that we - obtain a simple Hidden Markov Model. However, it's possible to - define much more sophisticated models. You should take a look - at the following papers for a few examples: - - Hidden Markov Support Vector Machines by - Y. Altun, I. Tsochantaridis, T. Hofmann - - Shallow Parsing with Conditional Random Fields by - Fei Sha and Fernando Pereira - - - - In the remainder of this example program we will show how to - define your own PSI(), as well as how to learn the "weight_vector" - parameter. Once you have these two items you will be able to - use the sequence_labeler to predict the labels of new sequences. -*/ - - -#include <iostream> -#include <dlib/svm_threaded.h> -#include <dlib/rand.h> - -using namespace std; -using namespace dlib; - - -/* - In this example we will be working with a Hidden Markov Model where - the hidden nodes and observation nodes both take on 3 different states. - The task will be to take a sequence of observations and predict the state - of the corresponding hidden nodes. -*/ - -const unsigned long num_label_states = 3; -const unsigned long num_sample_states = 3; - -// ---------------------------------------------------------------------------------------- - -class feature_extractor -{ - /* - This object is where you define your PSI(). To ensure that the argmax_y - remains a tractable problem, the PSI(x,y) vector is actually a sum of vectors, - each derived from the entire input sequence x but only part of the label - sequence y. This allows the argmax_y to be efficiently solved using the - well known Viterbi algorithm. - */ - -public: - // This defines the type used to represent the observed sequence. You can use - // any type here so long as it has a .size() which returns the number of things - // in the sequence. - typedef std::vector<unsigned long> sequence_type; - - unsigned long num_features() const - /*! - ensures - - returns the dimensionality of the PSI() feature vector. - !*/ - { - // Recall that we are defining a HMM. So in this case the PSI() vector - // should have the same dimensionality as the number of parameters in the HMM. - return num_label_states*num_label_states + num_label_states*num_sample_states; - } - - unsigned long order() const - /*! - ensures - - This object represents a Markov model on the output labels. - This parameter defines the order of the model. That is, this - value controls how many previous label values get to be taken - into consideration when performing feature extraction for a - particular element of the input sequence. Note that the runtime - of the algorithm is exponential in the order. So don't make order - very large. - !*/ - { - // In this case we are using a HMM model that only looks at the - // previous label. - return 1; - } - - unsigned long num_labels() const - /*! - ensures - - returns the number of possible output labels. - !*/ - { - return num_label_states; - } - - template <typename feature_setter, typename EXP> - void get_features ( - feature_setter& set_feature, - const sequence_type& x, - const matrix_exp<EXP>& y, - unsigned long position - ) const - /*! - requires - - EXP::type == unsigned long - (i.e. y contains unsigned longs) - - position < x.size() - - y.size() == min(position, order) + 1 - - is_vector(y) == true - - max(y) < num_labels() - - set_feature is a function object which allows expressions of the form: - - set_features((unsigned long)feature_index, (double)feature_value); - - set_features((unsigned long)feature_index); - ensures - - for all valid i: - - interprets y(i) as the label corresponding to x[position-i] - - This function computes the part of PSI() corresponding to the x[position] - element of the input sequence. Moreover, this part of PSI() is returned as - a sparse vector by invoking set_feature(). For example, to set the feature - with an index of 55 to the value of 1 this method would call: - set_feature(55); - Or equivalently: - set_feature(55,1); - Therefore, the first argument to set_feature is the index of the feature - to be set while the second argument is the value the feature should take. - Additionally, note that calling set_feature() multiple times with the same - feature index does NOT overwrite the old value, it adds to the previous - value. For example, if you call set_feature(55) 3 times then it will - result in feature 55 having a value of 3. - - This function only calls set_feature() with feature_index values < num_features() - !*/ - { - // Again, the features below only define a simple HMM. But in general, you can - // use a wide variety of sophisticated feature extraction methods here. - - // Pull out an indicator feature for the type of transition between the - // previous label and the current label. - if (y.size() > 1) - set_feature(y(1)*num_label_states + y(0)); - - // Pull out an indicator feature for the type of observed node given - // the current label. - set_feature(num_label_states*num_label_states + - y(0)*num_sample_states + x[position]); - } -}; - -// We need to define serialize() and deserialize() for our feature extractor if we want -// to be able to serialize and deserialize our learned models. In this case the -// implementation is empty since our feature_extractor doesn't have any state. But you -// might define more complex feature extractors which have state that needs to be saved. -void serialize(const feature_extractor&, std::ostream&) {} -void deserialize(feature_extractor&, std::istream&) {} - -// ---------------------------------------------------------------------------------------- - -void make_dataset ( - const matrix<double>& transition_probabilities, - const matrix<double>& emission_probabilities, - std::vector<std::vector<unsigned long> >& samples, - std::vector<std::vector<unsigned long> >& labels, - unsigned long dataset_size -); -/*! - requires - - transition_probabilities.nr() == transition_probabilities.nc() - - transition_probabilities.nr() == emission_probabilities.nr() - - The rows of transition_probabilities and emission_probabilities must sum to 1. - (i.e. sum_cols(transition_probabilities) and sum_cols(emission_probabilities) - must evaluate to vectors of all 1s.) - ensures - - This function randomly samples a bunch of sequences from the HMM defined by - transition_probabilities and emission_probabilities. - - The HMM is defined by: - - The probability of transitioning from hidden state H1 to H2 - is given by transition_probabilities(H1,H2). - - The probability of a hidden state H producing an observed state - O is given by emission_probabilities(H,O). - - #samples.size() == #labels.size() == dataset_size - - for all valid i: - - #labels[i] is a randomly sampled sequence of hidden states from the - given HMM. #samples[i] is its corresponding randomly sampled sequence - of observed states. -!*/ - -// ---------------------------------------------------------------------------------------- - -int main() -{ - // We need a dataset to test the machine learning algorithms. So we are going to - // define a HMM based on the following two matrices and then randomly sample a - // set of data from it. Then we will see if the machine learning method can - // recover the HMM model from the training data. - - - matrix<double> transition_probabilities(num_label_states, num_label_states); - transition_probabilities = 0.05, 0.90, 0.05, - 0.05, 0.05, 0.90, - 0.90, 0.05, 0.05; - - matrix<double> emission_probabilities(num_label_states,num_sample_states); - emission_probabilities = 0.5, 0.5, 0.0, - 0.0, 0.5, 0.5, - 0.5, 0.0, 0.5; - - std::vector<std::vector<unsigned long> > samples; - std::vector<std::vector<unsigned long> > labels; - // sample 1000 labeled sequences from the HMM. - make_dataset(transition_probabilities,emission_probabilities, - samples, labels, 1000); - - // print out some of the randomly sampled sequences - for (int i = 0; i < 10; ++i) - { - cout << "hidden states: " << trans(mat(labels[i])); - cout << "observed states: " << trans(mat(samples[i])); - cout << "******************************" << endl; - } - - // Next we use the structural_sequence_labeling_trainer to learn our - // prediction model based on just the samples and labels. - structural_sequence_labeling_trainer<feature_extractor> trainer; - // This is the common SVM C parameter. Larger values encourage the - // trainer to attempt to fit the data exactly but might overfit. - // In general, you determine this parameter by cross-validation. - trainer.set_c(4); - // This trainer can use multiple CPU cores to speed up the training. - // So set this to the number of available CPU cores. - trainer.set_num_threads(4); - - - // Learn to do sequence labeling from the dataset - sequence_labeler<feature_extractor> labeler = trainer.train(samples, labels); - - // Test the learned labeler on one of the training samples. In this - // case it will give the correct sequence of labels. - std::vector<unsigned long> predicted_labels = labeler(samples[0]); - cout << "true hidden states: "<< trans(mat(labels[0])); - cout << "predicted hidden states: "<< trans(mat(predicted_labels)); - - - - // We can also do cross-validation. The confusion_matrix is defined as: - // - confusion_matrix(T,P) == the number of times a sequence element with label T - // was predicted to have a label of P. - // So if all predictions are perfect then only diagonal elements of this matrix will - // be non-zero. - matrix<double> confusion_matrix; - confusion_matrix = cross_validate_sequence_labeler(trainer, samples, labels, 4); - cout << "\ncross-validation: " << endl; - cout << confusion_matrix; - cout << "label accuracy: "<< sum(diag(confusion_matrix))/sum(confusion_matrix) << endl; - - // In this case, the label accuracy is about 88%. At this point, we want to know if - // the machine learning method was able to recover the HMM model from the data. So - // to test this, we can load the true HMM model into another sequence_labeler and - // test it out on the data and compare the results. - - matrix<double,0,1> true_hmm_model_weights = log(join_cols(reshape_to_column_vector(transition_probabilities), - reshape_to_column_vector(emission_probabilities))); - // With this model, labeler_true will predict the most probable set of labels - // given an input sequence. That is, it will predict using the equation: - // y == argmax_y dot(true_hmm_model_weights, PSI(x,y)) - sequence_labeler<feature_extractor> labeler_true(true_hmm_model_weights); - - confusion_matrix = test_sequence_labeler(labeler_true, samples, labels); - cout << "\nTrue HMM model: " << endl; - cout << confusion_matrix; - cout << "label accuracy: "<< sum(diag(confusion_matrix))/sum(confusion_matrix) << endl; - - // Happily, we observe that the true model also obtains a label accuracy of 88%. - - - - - - - // Finally, the labeler can be serialized to disk just like most dlib objects. - serialize("labeler.dat") << labeler; - - // recall from disk - deserialize("labeler.dat") >> labeler; -} - -// ---------------------------------------------------------------------------------------- -// ---------------------------------------------------------------------------------------- -// Code for creating a bunch of random samples from our HMM. -// ---------------------------------------------------------------------------------------- -// ---------------------------------------------------------------------------------------- - -void sample_hmm ( - dlib::rand& rnd, - const matrix<double>& transition_probabilities, - const matrix<double>& emission_probabilities, - unsigned long previous_label, - unsigned long& next_label, - unsigned long& next_sample -) -/*! - requires - - previous_label < transition_probabilities.nr() - - transition_probabilities.nr() == transition_probabilities.nc() - - transition_probabilities.nr() == emission_probabilities.nr() - - The rows of transition_probabilities and emission_probabilities must sum to 1. - (i.e. sum_cols(transition_probabilities) and sum_cols(emission_probabilities) - must evaluate to vectors of all 1s.) - ensures - - This function randomly samples the HMM defined by transition_probabilities - and emission_probabilities assuming that the previous hidden state - was previous_label. - - The HMM is defined by: - - P(next_label |previous_label) == transition_probabilities(previous_label, next_label) - - P(next_sample|next_label) == emission_probabilities (next_label, next_sample) - - #next_label == the sampled value of the hidden state - - #next_sample == the sampled value of the observed state -!*/ -{ - // sample next_label - double p = rnd.get_random_double(); - for (long c = 0; p >= 0 && c < transition_probabilities.nc(); ++c) - { - next_label = c; - p -= transition_probabilities(previous_label, c); - } - - // now sample next_sample - p = rnd.get_random_double(); - for (long c = 0; p >= 0 && c < emission_probabilities.nc(); ++c) - { - next_sample = c; - p -= emission_probabilities(next_label, c); - } -} - -// ---------------------------------------------------------------------------------------- - -void make_dataset ( - const matrix<double>& transition_probabilities, - const matrix<double>& emission_probabilities, - std::vector<std::vector<unsigned long> >& samples, - std::vector<std::vector<unsigned long> >& labels, - unsigned long dataset_size -) -{ - samples.clear(); - labels.clear(); - - dlib::rand rnd; - - // now randomly sample some labeled sequences from our Hidden Markov Model - for (unsigned long iter = 0; iter < dataset_size; ++iter) - { - const unsigned long sequence_size = rnd.get_random_32bit_number()%20+3; - std::vector<unsigned long> sample(sequence_size); - std::vector<unsigned long> label(sequence_size); - - unsigned long previous_label = rnd.get_random_32bit_number()%num_label_states; - for (unsigned long i = 0; i < sample.size(); ++i) - { - unsigned long next_label = 0, next_sample = 0; - sample_hmm(rnd, transition_probabilities, emission_probabilities, - previous_label, next_label, next_sample); - - label[i] = next_label; - sample[i] = next_sample; - - previous_label = next_label; - } - - samples.push_back(sample); - labels.push_back(label); - } -} - -// ---------------------------------------------------------------------------------------- - |