Merging upstream version 1.45.3+dfsg.

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 414 insertions, 0 deletions

diff --git a/src/ml/dlib/examples/svm_struct_ex.cpp b/src/ml/dlib/examples/svm_struct_ex.cpp
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+// 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 structural SVM solver from the dlib C++
+    Library.  Therefore, this example teaches you the central ideas needed to setup a
+    structural SVM model for your machine learning problems.  To illustrate the process, we
+    use dlib's structural SVM solver to learn the parameters of a simple multi-class
+    classifier.  We first discuss the multi-class classifier model and then walk through
+    using the structural SVM tools to find the parameters of this classification model.   
+
+*/
+
+
+#include <iostream>
+#include <dlib/svm_threaded.h>
+
+using namespace std;
+using namespace dlib;
+
+
+// Before we start, we define three typedefs we will use throughout this program.  The
+// first is used to represent the parameter vector the structural SVM is learning, the
+// second is used to represent the "sample type".  In this example program it is just a
+// vector but in general when using a structural SVM your sample type can be anything you
+// want (e.g. a string or an image).  The last typedef is the type used to represent the
+// PSI vector which is part of the structural SVM model which we will explain in detail
+// later on.  But the important thing to note here is that you can use either a dense
+// representation (i.e. a dlib::matrix object) or a sparse representation for the PSI
+// vector.  See svm_sparse_ex.cpp for an introduction to sparse vectors in dlib.  Here we
+// use the same type for each of these three things to keep the example program simple.
+typedef matrix<double,0,1> column_vector;       // Must be a dlib::matrix type.
+typedef matrix<double,0,1> sample_type;         // Can be anything you want.
+typedef matrix<double,0,1> feature_vector_type; // Must be dlib::matrix or some kind of sparse vector.
+
+// ----------------------------------------------------------------------------------------
+
+int           predict_label                (const column_vector& weights, const sample_type& sample);
+column_vector train_three_class_classifier (const std::vector<sample_type>& samples, const std::vector<int>& labels);
+
+// ----------------------------------------------------------------------------------------
+
+int main()
+{
+    // In this example, we have three types of samples: class 0, 1, or 2.  That is, each of
+    // our sample vectors falls into one of three classes.  To keep this example very
+    // simple, each sample vector is zero everywhere except at one place.  The non-zero
+    // dimension of each vector determines the class of the vector.  So for example, the
+    // first element of samples has a class of 1 because samples[0](1) is the only non-zero
+    // element of samples[0].   
+    sample_type samp(3);
+    std::vector<sample_type> samples;
+    samp = 0,2,0; samples.push_back(samp);
+    samp = 1,0,0; samples.push_back(samp);
+    samp = 0,4,0; samples.push_back(samp);
+    samp = 0,0,3; samples.push_back(samp);
+    // Since we want to use a machine learning method to learn a 3-class classifier we need
+    // to record the labels of our samples.  Here samples[i] has a class label of labels[i].
+    std::vector<int> labels;
+    labels.push_back(1);
+    labels.push_back(0);
+    labels.push_back(1);
+    labels.push_back(2);
+
+
+    // Now that we have some training data we can tell the structural SVM to learn the
+    // parameters of our 3-class classifier model.  The details of this will be explained
+    // later.  For now, just note that it finds the weights (i.e. a vector of real valued
+    // parameters) such that predict_label(weights, sample) always returns the correct
+    // label for a sample vector. 
+    column_vector weights = train_three_class_classifier(samples, labels);
+
+    // Print the weights and then evaluate predict_label() on each of our training samples.
+    // Note that the correct label is predicted for each sample.
+    cout << weights << endl;
+    for (unsigned long i = 0; i < samples.size(); ++i)
+        cout << "predicted label for sample["<<i<<"]: " << predict_label(weights, samples[i]) << endl;
+}
+
+// ----------------------------------------------------------------------------------------
+
+int predict_label (
+    const column_vector& weights,
+    const sample_type& sample
+)
+/*!
+    requires
+        - weights.size() == 9
+        - sample.size() == 3
+    ensures
+        - Given the 9-dimensional weight vector which defines a 3 class classifier, this
+          function predicts the class of the given 3-dimensional sample vector.
+          Therefore, the output of this function is either 0, 1, or 2 (i.e. one of the
+          three possible labels).
+!*/
+{
+    // Our 3-class classifier model can be thought of as containing 3 separate linear
+    // classifiers.  So to predict the class of a sample vector we evaluate each of these
+    // three classifiers and then whatever classifier has the largest output "wins" and
+    // predicts the label of the sample.  This is the popular one-vs-all multi-class
+    // classifier model.  
+    //
+    // Keeping this in mind, the code below simply pulls the three separate weight vectors
+    // out of weights and then evaluates each against sample.  The individual classifier
+    // scores are stored in scores and the highest scoring index is returned as the label.
+    column_vector w0, w1, w2;
+    w0 = rowm(weights, range(0,2));
+    w1 = rowm(weights, range(3,5));
+    w2 = rowm(weights, range(6,8));
+
+    column_vector scores(3);
+    scores = dot(w0, sample), dot(w1, sample), dot(w2, sample);
+
+    return index_of_max(scores);
+}
+
+// ----------------------------------------------------------------------------------------
+// ----------------------------------------------------------------------------------------
+// ----------------------------------------------------------------------------------------
+
+class three_class_classifier_problem : public structural_svm_problem_threaded<column_vector, feature_vector_type>
+{
+    /*!
+        Now we arrive at the meat of this example program.  To use dlib's structural SVM
+        solver you need to define an object which tells the structural SVM solver what to
+        do for your problem.  In this example, this is done by defining the three_class_classifier_problem 
+        object which inherits from structural_svm_problem_threaded.  Before we get into the
+        details, we first discuss some background information on structural SVMs.  
+        
+        A structural SVM is a supervised machine learning method for learning to predict
+        complex outputs.  This is contrasted with a binary classifier which makes only simple
+        yes/no predictions.  A structural SVM, on the other hand, can learn to predict
+        complex outputs such as entire parse trees or DNA sequence alignments.  To do this,
+        it learns a function F(x,y) which measures how well a particular data sample x
+        matches a label y, where a label is potentially a complex thing like a parse tree.
+        However, to keep this example program simple we use only a 3 category label output. 
+       
+        At test time, the best label for a new x is given by the y which maximizes F(x,y).
+        To put this into the context of the current example, F(x,y) computes the score for
+        a given sample and class label.  The predicted class label is therefore whatever
+        value of y which makes F(x,y) the biggest.  This is exactly what predict_label()
+        does.  That is, it computes F(x,0), F(x,1), and F(x,2) and then reports which label
+        has the biggest value.
+       
+        At a high level, a structural SVM can be thought of as searching the parameter space
+        of F(x,y) for the set of parameters that make the following inequality true as often
+        as possible:
+            F(x_i,y_i) > max{over all incorrect labels of x_i} F(x_i, y_incorrect)
+        That is, it seeks to find the parameter vector such that F(x,y) always gives the
+        highest score to the correct output.  To define the structural SVM optimization
+        problem precisely, we first introduce some notation:
+            - let PSI(x,y)    == the joint feature vector for input x and a label y.
+            - let F(x,y|w)    == dot(w,PSI(x,y)).  
+              (we use the | notation to emphasize that F() has the parameter vector of
+              weights called w)
+            - let LOSS(idx,y) == the loss incurred for predicting that the idx-th training 
+              sample has a label of y.  Note that LOSS() should always be >= 0 and should
+              become exactly 0 when y is the correct label for the idx-th sample.  Moreover,
+              it should notionally indicate how bad it is to predict y for the idx'th sample.
+            - let x_i == the i-th training sample.
+            - let y_i == the correct label for the i-th training sample.
+            - The number of data samples is N.
+       
+        Then the optimization problem solved by dlib's structural SVM solver is the following:
+            Minimize: h(w) == 0.5*dot(w,w) + C*R(w)
+       
+            Where R(w) == sum from i=1 to N: 1/N * sample_risk(i,w)
+            and sample_risk(i,w) == max over all Y: LOSS(i,Y) + F(x_i,Y|w) - F(x_i,y_i|w)
+            and C > 0
+       
+        You can think of the sample_risk(i,w) as measuring the degree of error you would make
+        when predicting the label of the i-th sample using parameters w.  That is, it is zero
+        only when the correct label would be predicted and grows larger the more "wrong" the
+        predicted output becomes.  Therefore, the objective function is minimizing a balance
+        between making the weights small (typically this reduces overfitting) and fitting the
+        training data.  The degree to which you try to fit the data is controlled by the C
+        parameter.
+       
+        For a more detailed introduction to structured support vector machines you should
+        consult the following paper: 
+            Predicting Structured Objects with Support Vector Machines by 
+            Thorsten Joachims, Thomas Hofmann, Yisong Yue, and Chun-nam Yu
+       
+    !*/
+
+public:
+
+    // Finally, we come back to the code.  To use dlib's structural SVM solver you need to
+    // provide the things discussed above.  This is the number of training samples, the
+    // dimensionality of PSI(), as well as methods for calculating the loss values and
+    // PSI() vectors.  You will also need to write code that can compute: max over all Y:
+    // LOSS(i,Y) + F(x_i,Y|w).  In particular, the three_class_classifier_problem class is
+    // required to implement the following four virtual functions:
+    //   - get_num_dimensions()
+    //   - get_num_samples() 
+    //   - get_truth_joint_feature_vector()
+    //   - separation_oracle()
+
+
+    // But first, we declare a constructor so we can populate our three_class_classifier_problem
+    // object with the data we need to define our machine learning problem.  All we do here
+    // is take in the training samples and their labels as well as a number indicating how
+    // many threads the structural SVM solver will use.  You can declare this constructor
+    // any way you like since it is not used by any of the dlib tools.
+    three_class_classifier_problem (
+        const std::vector<sample_type>& samples_,
+        const std::vector<int>& labels_,
+        const unsigned long num_threads
+    ) : 
+        structural_svm_problem_threaded<column_vector, feature_vector_type>(num_threads),
+        samples(samples_),
+        labels(labels_)
+    {}
+
+    feature_vector_type make_psi (
+        const sample_type& x,
+        const int label
+    ) const
+    /*!
+        ensures
+            - returns the vector PSI(x,label)
+    !*/
+    {
+        // All we are doing here is taking x, which is a 3 dimensional sample vector in this
+        // example program, and putting it into one of 3 places in a 9 dimensional PSI
+        // vector, which we then return.  So this function returns PSI(x,label).  To see why
+        // we setup PSI like this, recall how predict_label() works.  It takes in a 9
+        // dimensional weight vector and breaks the vector into 3 pieces.  Each piece then
+        // defines a different classifier and we use them in a one-vs-all manner to predict
+        // the label.  So now that we are in the structural SVM code we have to define the
+        // PSI vector to correspond to this usage.  That is, we need to setup PSI so that
+        // argmax_y dot(weights,PSI(x,y)) == predict_label(weights,x).  This is how we tell
+        // the structural SVM solver what kind of problem we are trying to solve.
+        //
+        // It's worth emphasizing that the single biggest step in using a structural SVM is
+        // deciding how you want to represent PSI(x,label).  It is always a vector, but
+        // deciding what to put into it to solve your problem is often not a trivial task.
+        // Part of the difficulty is that you need an efficient method for finding the label
+        // that makes dot(w,PSI(x,label)) the biggest.  Sometimes this is easy, but often
+        // finding the max scoring label turns into a difficult combinatorial optimization
+        // problem.  So you need to pick a PSI that doesn't make the label maximization step
+        // intractable but also still well models your problem.  
+        //
+        // Finally, note that make_psi() is a helper routine we define in this example.  In
+        // general, you are not required to implement it.  That is, all you must implement
+        // are the four virtual functions defined below.
+
+
+        // So let's make an empty 9-dimensional PSI vector
+        feature_vector_type psi(get_num_dimensions());
+        psi = 0; // zero initialize it
+
+        // Now put a copy of x into the right place in PSI according to its label.  So for
+        // example, if label is 1 then psi would be:  [0 0 0 x(0) x(1) x(2) 0 0 0]
+        if (label == 0)
+            set_rowm(psi,range(0,2)) = x;
+        else if (label == 1)
+            set_rowm(psi,range(3,5)) = x;
+        else // the label must be 2 
+            set_rowm(psi,range(6,8)) = x;
+
+        return psi;
+    }
+
+    // We need to declare the dimensionality of the PSI vector (this is also the
+    // dimensionality of the weight vector we are learning).  Similarly, we need to declare
+    // the number of training samples.  We do this by defining the following virtual
+    // functions.
+    virtual long get_num_dimensions () const { return samples[0].size() * 3; }
+    virtual long get_num_samples ()    const { return samples.size(); }
+
+    // In get_truth_joint_feature_vector(), all you have to do is output the PSI() vector
+    // for the idx-th training sample when it has its true label.  So here it outputs
+    // PSI(samples[idx], labels[idx]).
+    virtual void get_truth_joint_feature_vector (
+        long idx,
+        feature_vector_type& psi 
+    ) const 
+    {
+        psi = make_psi(samples[idx], labels[idx]);
+    }
+
+    // separation_oracle() is more interesting.  dlib's structural SVM solver will call
+    // separation_oracle() many times during the optimization.  Each time it will give it
+    // the current value of the parameter weights and separation_oracle() is supposed to
+    // find the label that most violates the structural SVM objective function for the
+    // idx-th sample.  Then the separation oracle reports the corresponding PSI vector and
+    // loss value.  To state this more precisely, the separation_oracle() member function
+    // has the following contract:
+    //   requires
+    //       - 0 <= idx < get_num_samples()
+    //       - current_solution.size() == get_num_dimensions()
+    //   ensures
+    //       - runs the separation oracle on the idx-th sample.  We define this as follows: 
+    //           - let X           == the idx-th training sample.
+    //           - let PSI(X,y)    == the joint feature vector for input X and an arbitrary label y.
+    //           - let F(X,y)      == dot(current_solution,PSI(X,y)).  
+    //           - let LOSS(idx,y) == the loss incurred for predicting that the idx-th sample
+    //             has a label of y.  Note that LOSS() should always be >= 0 and should
+    //             become exactly 0 when y is the correct label for the idx-th sample.
+    //
+    //               Then the separation oracle finds a Y such that: 
+    //                   Y = argmax over all y: LOSS(idx,y) + F(X,y) 
+    //                   (i.e. It finds the label which maximizes the above expression.)
+    //
+    //               Finally, we can define the outputs of this function as:
+    //               - #loss == LOSS(idx,Y) 
+    //               - #psi == PSI(X,Y) 
+    virtual void separation_oracle (
+        const long idx,
+        const column_vector& current_solution,
+        scalar_type& loss,
+        feature_vector_type& psi
+    ) const 
+    {
+        // Note that the solver will use multiple threads to make concurrent calls to
+        // separation_oracle(), therefore, you must implement it in a thread safe manner
+        // (or disable threading by inheriting from structural_svm_problem instead of
+        // structural_svm_problem_threaded).  However, if your separation oracle is not
+        // very fast to execute you can get a very significant speed boost by using the
+        // threaded solver.  In general, all you need to do to make your separation oracle
+        // thread safe is to make sure it does not modify any global variables or members
+        // of three_class_classifier_problem.  So it is usually easy to make thread safe.
+
+        column_vector scores(3);
+
+        // compute scores for each of the three classifiers
+        scores = dot(rowm(current_solution, range(0,2)),  samples[idx]),
+                 dot(rowm(current_solution, range(3,5)),  samples[idx]),
+                 dot(rowm(current_solution, range(6,8)),  samples[idx]);
+
+        // Add in the loss-augmentation.  Recall that we maximize LOSS(idx,y) + F(X,y) in
+        // the separate oracle, not just F(X,y) as we normally would in predict_label().
+        // Therefore, we must add in this extra amount to account for the loss-augmentation.
+        // For our simple multi-class classifier, we incur a loss of 1 if we don't predict
+        // the correct label and a loss of 0 if we get the right label.
+        if (labels[idx] != 0)
+            scores(0) += 1;
+        if (labels[idx] != 1)
+            scores(1) += 1;
+        if (labels[idx] != 2)
+            scores(2) += 1;
+
+        // Now figure out which classifier has the largest loss-augmented score.
+        const int max_scoring_label = index_of_max(scores);
+        // And finally record the loss that was associated with that predicted label.
+        // Again, the loss is 1 if the label is incorrect and 0 otherwise.
+        if (max_scoring_label == labels[idx])
+            loss = 0;
+        else
+            loss = 1;
+
+        // Finally, compute the PSI vector corresponding to the label we just found and
+        // store it into psi for output.
+        psi = make_psi(samples[idx], max_scoring_label);
+    }
+
+private:
+
+    // Here we hold onto the training data by reference.  You can hold it by value or by
+    // any other method you like.
+    const std::vector<sample_type>& samples;
+    const std::vector<int>& labels;
+};
+    
+// ----------------------------------------------------------------------------------------
+
+// This function puts it all together.  In here we use the three_class_classifier_problem
+// along with dlib's oca cutting plane solver to find the optimal weights given our
+// training data.
+column_vector train_three_class_classifier (
+    const std::vector<sample_type>& samples,
+    const std::vector<int>& labels
+)
+{
+    const unsigned long num_threads = 4;
+    three_class_classifier_problem problem(samples, labels, num_threads);
+
+    // Before we run the solver we set up some general parameters.  First,
+    // you can set the C parameter of the structural SVM by calling set_c().
+    problem.set_c(1);
+
+    // The epsilon parameter controls the stopping tolerance.  The optimizer will run until
+    // R(w) is within epsilon of its optimal value. If you don't set this then it defaults
+    // to 0.001.
+    problem.set_epsilon(0.0001);
+
+    // Uncomment this and the optimizer will print its progress to standard out.  You will
+    // be able to see things like the current risk gap.  The optimizer continues until the
+    // risk gap is below epsilon.
+    //problem.be_verbose();
+
+    // The optimizer uses an internal cache to avoid unnecessary calls to your
+    // separation_oracle() routine.  This parameter controls the size of that cache.
+    // Bigger values use more RAM and might make the optimizer run faster.  You can also
+    // disable it by setting it to 0 which is good to do when your separation_oracle is
+    // very fast.  If you don't call this function it defaults to a value of 5.
+    //problem.set_max_cache_size(20);
+
+    
+    column_vector weights;
+    // Finally, we create the solver and then run it.
+    oca solver;
+    solver(problem, weights);
+
+    // Alternatively, if you wanted to require that the learned weights are all
+    // non-negative then you can call the solver as follows and it will put a constraint on
+    // the optimization problem which causes all elements of weights to be >= 0.  
+    //solver(problem, weights, problem.get_num_dimensions());
+
+    return weights;
+}
+
+// ----------------------------------------------------------------------------------------
+