##

    #!/usr/bin/python
    # 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.
    #
    # As an aside, dlib's C++ interface to the structural SVM solver is threaded.  So on a
    # multi-core computer it is significantly faster than using the python interface.  So
    # consider using the C++ interface instead if you find that running it in python is slow.
    #
    # COMPILING THE DLIB PYTHON INTERFACE
    #   Dlib comes with a compiled python interface for python 2.7 on MS Windows.  If
    #   you are using another python version or operating system then you need to
    #   compile the dlib python interface before you can use this file.  To do this,
    #   run compile_dlib_python_module.bat.  This should work on any operating system
    #   so long as you have CMake and boost-python installed.  On Ubuntu, this can be
    #   done easily by running the command:  sudo apt-get install libboost-python-dev cmake

    import dlib

    def 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].
        samples = [[0,2,0], [1,0,0], [0,4,0], [0,0,3]];
        # 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].
        labels =  [1,0,1,2]

        # 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.
        problem = three_class_classifier_problem(samples, labels)
        weights = dlib.solve_structural_svm_problem(problem)

        # Print the weights and then evaluate predict_label() on each of our training samples.
        # Note that the correct label is predicted for each sample.
        print weights
        for i in range(len(samples)):
            print "predicted label for sample[{0}]: {1}".format(i, predict_label(weights, samples[i]))

    def predict_label(weights, sample):
        """Given the 9-dimensional weight vector which defines a 3 class classifier, predict 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.
        w0 = weights[0:3]
        w1 = weights[3:6]
        w2 = weights[6:9]
        scores = [dot(w0, sample), dot(w1,sample), dot(w2, sample)]
        max_scoring_label = scores.index(max(scores))
        return max_scoring_label

    def dot(a, b):
        "Compute the dot product between the two vectors a and b."
        return sum(i*j for i,j in zip(a,b))

    ###########################################################################################

    class three_class_classifier_problem:
        # Now we arrive at the meat of this example program.  To use the
        # dlib.solve_structural_svm_problem() routine 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.  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 a structural SVM using
        # dlib.solve_structural_svm_problem() 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
        #

        # Finally, we come back to the code.  To use dlib.solve_structural_svm_problem() you
        # need to provide the things discussed above.  This is the value of C, 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).  To summarize, the
        # three_class_classifier_problem class is required to have the following fields:
        #   - C
        #   - num_samples
        #   - num_dimensions
        #   - get_truth_joint_feature_vector()
        #   - separation_oracle()

        C = 1

        # There are also a number of optional arguments:
        # epsilon is 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.
        #epsilon = 1e-13

        # 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.
        #be_verbose = True

        # If you want to require that the learned weights are all non-negative then set this
        # field to True.
        #learns_nonnegative_weights = True

        # 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
        # If you don't call this function it defaults to a value of 5.
        #max_cache_size = 20

        def __init__(self, samples, labels):
            # dlib.solve_structural_svm_problem() expects the class to have num_samples and
            # num_dimensions fields.  These fields should contain the number of training
            # samples and the dimensionality of the PSI feature vector respectively.
            self.num_samples = len(samples)
            self.num_dimensions = len(samples[0])*3

            self.samples = samples
            self.labels = labels

        def make_psi(self, x, label):
            """Compute 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.

            # Create a dense vector object (note that you can also use unsorted sparse vectors
            # (i.e.  dlib.sparse_vector objects) to represent your PSI vector.  This is useful
            # if you have very high dimensional PSI vectors that are mostly zeros.  In the
            # context of this example, you would simply return a dlib.sparse_vector at the end
            # of make_psi() and the rest of the example would still work properly. ).
            psi = dlib.vector()
            # Set it to have 9 dimensions.  Note that the elements of the vector are 0
            # initialized.
            psi.resize(self.num_dimensions)
            dims = len(x)
            if (label == 0):
                for i in range(0,dims):
                    psi[i] = x[i]
            elif (label == 1):
                for i in range(dims,2*dims):
                    psi[i] = x[i-dims]
            else: # the label must be 2
                for i in range(2*dims,3*dims):
                    psi[i] = x[i-2*dims]
            return psi

        # Now we get to the two member functions that are directly called by
        # dlib.solve_structural_svm_problem().
        #
        # In get_truth_joint_feature_vector(), all you have to do is return the PSI() vector
        # for the idx-th training sample when it has its true label.  So here it returns
        # PSI(self.samples[idx], self.labels[idx]).
        def get_truth_joint_feature_vector(self, idx):
            return self.make_psi(self.samples[idx], self.labels[idx])

        # separation_oracle() is more interesting.  dlib.solve_structural_svm_problem() will
        # call separation_oracle() many times during the optimization.  Each time it will give
        # it the current value of the parameter weights and the 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 < self.num_samples
        #       - len(current_solution) == self.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, separation_oracle() returns LOSS(idx,Y),PSI(X,Y)
        def separation_oracle(self, idx, current_solution):
            samp = self.samples[idx]
            dims = len(samp)
            scores = [0,0,0]
            # compute scores for each of the three classifiers
            scores[0] = dot(current_solution[0:dims], samp)
            scores[1] = dot(current_solution[dims:2*dims], samp)
            scores[2] = dot(current_solution[2*dims:3*dims], samp)

            # 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 (self.labels[idx] != 0):
                scores[0] += 1
            if (self.labels[idx] != 1):
                scores[1] += 1
            if (self.labels[idx] != 2):
                scores[2] += 1

            # Now figure out which classifier has the largest loss-augmented score.
            max_scoring_label = scores.index(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 == self.labels[idx]):
                loss = 0
            else:
                loss = 1

            # Finally, return the loss and PSI vector corresponding to the label we just found.
            psi = self.make_psi(samp, max_scoring_label)
            return loss,psi

    if __name__ == "__main__":
        main()