##

    #!/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 SVM-Rank tool from the dlib C++
    # Library.  This is a tool useful for learning to rank objects.  For example,
    # you might use it to learn to rank web pages in response to a user's query.
    # The idea being to rank the most relevant pages higher than non-relevant pages.
    #
    # In this example, we will create a simple test dataset and show how to learn a
    # ranking function from it.  The purpose of the function will be to give
    # "relevant" objects higher scores than "non-relevant" objects.  The idea is
    # that you use this score to order the objects so that the most relevant objects
    # come to the top of the ranked list.
    #
    #
    # COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
    #   You can install dlib using the command:
    #       pip install dlib
    #
    #   Alternatively, if you want to compile dlib yourself then go into the dlib
    #   root folder and run:
    #       python setup.py install
    #   or
    #       python setup.py install --yes USE_AVX_INSTRUCTIONS
    #   if you have a CPU that supports AVX instructions, since this makes some
    #   things run faster.
    #
    #   Compiling dlib 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

    # Now let's make some testing data.  To make it really simple, let's suppose
    # that we are ranking 2D vectors and that vectors with positive values in the
    # first dimension should rank higher than other vectors.  So what we do is make
    # examples of relevant (i.e. high ranking) and non-relevant (i.e. low ranking)
    # vectors and store them into a ranking_pair object like so:
    data = dlib.ranking_pair()
    # Here we add two examples.  In real applications, you would want lots of
    # examples of relevant and non-relevant vectors.
    data.relevant.append(dlib.vector([1, 0]))
    data.nonrelevant.append(dlib.vector([0, 1]))

    # Now that we have some data, we can use a machine learning method to learn a
    # function that will give high scores to the relevant vectors and low scores to
    # the non-relevant vectors.
    trainer = dlib.svm_rank_trainer()
    # Note that the trainer object has some parameters that control how it behaves.
    # For example, since this is the SVM-Rank algorithm it has a C parameter that
    # controls the trade-off between trying to fit the training data exactly or
    # selecting a "simpler" solution which might generalize better.
    trainer.c = 10

    # So let's do the training.
    rank = trainer.train(data)

    # Now if you call rank on a vector it will output a ranking score.  In
    # particular, the ranking score for relevant vectors should be larger than the
    # score for non-relevant vectors.
    print("Ranking score for a relevant vector:     {}".format(
        rank(data.relevant[0])))
    print("Ranking score for a non-relevant vector: {}".format(
        rank(data.nonrelevant[0])))
    # The output is the following:
    #    ranking score for a relevant vector:     0.5
    #    ranking score for a non-relevant vector: -0.5

    # If we want an overall measure of ranking accuracy we can compute the ordering
    # accuracy and mean average precision values by calling test_ranking_function().
    # In this case, the ordering accuracy tells us how often a non-relevant vector
    # was ranked ahead of a relevant vector.  In this case, it returns 1 for both
    # metrics, indicating that the rank function outputs a perfect ranking.
    print(dlib.test_ranking_function(rank, data))

    # The ranking scores are computed by taking the dot product between a learned
    # weight vector and a data vector.  If you want to see the learned weight vector
    # you can display it like so:
    print("Weights: {}".format(rank.weights))
    # In this case the weights are:
    #  0.5
    # -0.5

    # In the above example, our data contains just two sets of objects.  The
    # relevant set and non-relevant set.  The trainer is attempting to find a
    # ranking function that gives every relevant vector a higher score than every
    # non-relevant vector.  Sometimes what you want to do is a little more complex
    # than this.
    #
    # For example, in the web page ranking example we have to rank pages based on a
    # user's query.  In this case, each query will have its own set of relevant and
    # non-relevant documents.  What might be relevant to one query may well be
    # non-relevant to another.  So in this case we don't have a single global set of
    # relevant web pages and another set of non-relevant web pages.
    #
    # To handle cases like this, we can simply give multiple ranking_pair instances
    # to the trainer.  Therefore, each ranking_pair would represent the
    # relevant/non-relevant sets for a particular query.  An example is shown below
    # (for simplicity, we reuse our data from above to make 4 identical "queries").
    queries = dlib.ranking_pairs()
    queries.append(data)
    queries.append(data)
    queries.append(data)
    queries.append(data)

    # We can train just as before.
    rank = trainer.train(queries)

    # Now that we have multiple ranking_pair instances, we can also use
    # cross_validate_ranking_trainer().  This performs cross-validation by splitting
    # the queries up into folds.  That is, it lets the trainer train on a subset of
    # ranking_pair instances and tests on the rest.  It does this over 4 different
    # splits and returns the overall ranking accuracy based on the held out data.
    # Just like test_ranking_function(), it reports both the ordering accuracy and
    # mean average precision.
    print("Cross validation results: {}".format(
        dlib.cross_validate_ranking_trainer(trainer, queries, 4)))

    # Finally, note that the ranking tools also support the use of sparse vectors in
    # addition to dense vectors (which we used above).  So if we wanted to do
    # exactly what we did in the first part of the example program above but using
    # sparse vectors we would do it like so:

    data = dlib.sparse_ranking_pair()
    samp = dlib.sparse_vector()

    # Make samp represent the same vector as dlib.vector([1, 0]).  In dlib, a sparse
    # vector is just an array of pair objects.  Each pair stores an index and a
    # value.  Moreover, the svm-ranking tools require sparse vectors to be sorted
    # and to have unique indices.  This means that the indices are listed in
    # increasing order and no index value shows up more than once.  If necessary,
    # you can use the dlib.make_sparse_vector() routine to make a sparse vector
    # object properly sorted and contain unique indices.
    samp.append(dlib.pair(0, 1))
    data.relevant.append(samp)

    # Now make samp represent the same vector as dlib.vector([0, 1])
    samp.clear()
    samp.append(dlib.pair(1, 1))
    data.nonrelevant.append(samp)

    trainer = dlib.svm_rank_trainer_sparse()
    rank = trainer.train(data)
    print("Ranking score for a relevant vector:     {}".format(
        rank(data.relevant[0])))
    print("Ranking score for a non-relevant vector: {}".format(
        rank(data.nonrelevant[0])))
    # Just as before, the output is the following:
    #    ranking score for a relevant vector:     0.5
    #    ranking score for a non-relevant vector: -0.5