# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
# This example shows how to use dlib to learn to do sequence segmentation.  In
# a sequence segmentation task we are given a sequence of objects (e.g. words in
# a sentence) and we are supposed to detect certain subsequences (e.g. the names
# of people).  Therefore, in the code below we create some very simple training
# sequences and use them to learn a sequence segmentation model.  In particular,
# our sequences will be sentences represented as arrays of words and our task
# will be to learn to identify person names.  Once we have our segmentation
# model we can use it to find names in new sentences, as we will show.
#   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 sys
import dlib

# The sequence segmentation models we work with in this example are chain
# structured conditional random field style models.  Therefore, central to a
# sequence segmentation model is some method for converting the elements of a
# sequence into feature vectors. That is, while you might start out representing
# your sequence as an array of strings, the dlib interface works in terms of
# arrays of feature vectors.  Each feature vector should capture important
# information about its corresponding element in the original raw sequence.  So
# in this example, since we work with sequences of words and want to identify
# names, we will create feature vectors that tell us if the word is capitalized
# or not.  In our simple data, this will be enough to identify names.
# Therefore, we define sentence_to_vectors() which takes a sentence represented
# as a string and converts it into an array of words and then associates a
# feature vector with each word.
def sentence_to_vectors(sentence):
    # Create an empty array of vectors
    vects = dlib.vectors()
    for word in sentence.split():
        # Our vectors are very simple 1-dimensional vectors.  The value of the
        # single feature is 1 if the first letter of the word is capitalized and
        # 0 otherwise.
        if word[0].isupper():
    return vects

# Dlib also supports the use of a sparse vector representation.  This is more
# efficient than the above form when you have very high dimensional vectors that
# are mostly full of zeros.  In dlib, each sparse vector is represented as an
# array of pair objects.  Each pair contains an index and value.  Any index not
# listed in the vector is implicitly associated with a value of zero.
# Additionally, when using sparse vectors with dlib.train_sequence_segmenter()
# you can use "unsorted" sparse vectors.  This means you can add the index/value
# pairs into your sparse vectors in any order you want and don't need to worry
# about them being in sorted order.
def sentence_to_sparse_vectors(sentence):
    vects = dlib.sparse_vectors()
    has_cap = dlib.sparse_vector()
    no_cap = dlib.sparse_vector()
    # make has_cap equivalent to dlib.vector([1])
    has_cap.append(dlib.pair(0, 1))

    # Since we didn't add anything to no_cap it is equivalent to
    # dlib.vector([0])
    for word in sentence.split():
        if word[0].isupper():
    return vects

def print_segment(sentence, names):
    words = sentence.split()
    for name in names:
        for i in name:
            sys.stdout.write(words[i] + " ")

# Now let's make some training data.  Each example is a sentence as well as a
# set of ranges which indicate the locations of any names.   
names = dlib.ranges()     # make an array of dlib.range objects.
segments = dlib.rangess() # make an array of arrays of dlib.range objects.
sentences = []

sentences.append("The other day I saw a man named Jim Smith")
# We want to detect person names.  So we note that the name is located within
# the range [8, 10).  Note that we use half open ranges to identify segments.
# So in this case, the segment identifies the string "Jim Smith".
names.append(dlib.range(8, 10))
names.clear() # make names empty for use again below

sentences.append("Davis King is the main author of the dlib Library")
names.append(dlib.range(0, 2))

sentences.append("Bob Jones is a name and so is George Clinton")
names.append(dlib.range(0, 2))
names.append(dlib.range(8, 10))

sentences.append("My dog is named Bob Barker")
names.append(dlib.range(4, 6))

sentences.append("ABC is an acronym but John James Smith is a name")
names.append(dlib.range(5, 8))

sentences.append("No names in this sentence at all")

# Now before we can pass these training sentences to the dlib tools we need to
# convert them into arrays of vectors as discussed above.  We can use either a
# sparse or dense representation depending on our needs.  In this example, we
# show how to do it both ways.
use_sparse_vects = False
if use_sparse_vects:
    # Make an array of arrays of dlib.sparse_vector objects.
    training_sequences = dlib.sparse_vectorss()
    for s in sentences:
    # Make an array of arrays of dlib.vector objects.
    training_sequences = dlib.vectorss()
    for s in sentences:

# Now that we have a simple training set we can train a sequence segmenter.
# However, the sequence segmentation trainer has some optional parameters we can
# set.  These parameters determine properties of the segmentation model we will
# learn.  See the dlib documentation for the sequence_segmenter object for a
# full discussion of their meanings.
params = dlib.segmenter_params()
params.window_size = 3
params.use_high_order_features = True
params.use_BIO_model = True
# 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.
params.C = 10

# Train a model.  The model object is responsible for predicting the locations
# of names in new sentences.
model = dlib.train_sequence_segmenter(training_sequences, segments, params)

# Let's print out the things the model thinks are names.  The output is a set
# of ranges which are predicted to contain names.  If you run this example
# program you will see that it gets them all correct.
for i, s in enumerate(sentences):
    print_segment(s, model(training_sequences[i]))

# Let's also try segmenting a new sentence.  This will print out "Bob Bucket".
# Note that we need to remember to use the same vector representation as we used
# during training.
test_sentence = "There once was a man from Nantucket " \
                "whose name rhymed with Bob Bucket"
if use_sparse_vects:
    print_segment(test_sentence, model(sentence_to_vectors(test_sentence)))

# We can also measure the accuracy of a model relative to some labeled data.
# This statement prints the precision, recall, and F1-score of the model
# relative to the data in training_sequences/segments.
print("Test on training data: {}".format(
      dlib.test_sequence_segmenter(model, training_sequences, segments)))

# We can also do 5-fold cross-validation and print the resulting precision,
# recall, and F1-score.
print("Cross validation: {}".format(
      dlib.cross_validate_sequence_segmenter(training_sequences, segments, 5,