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185 lines
8.0 KiB
Python
Executable File
185 lines
8.0 KiB
Python
Executable File
#!/usr/bin/python
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# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
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#
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#
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# This example shows how to use dlib to learn to do sequence segmentation. In a sequence
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# segmentation task we are given a sequence of objects (e.g. words in a sentence) and we
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# are supposed to detect certain subsequences (e.g. the names of people). Therefore, in
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# the code below we create some very simple training sequences and use them to learn a
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# sequence segmentation model. In particular, our sequences will be sentences represented
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# as arrays of words and our task will be to learn to identify person names. Once we have
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# our segmentation model we can use it to find names in new sentences, as we will show.
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#
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# COMPILING THE DLIB PYTHON INTERFACE
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# Dlib comes with a compiled python interface for python 2.7 on MS Windows. If
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# you are using another python version or operating system then you need to
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# compile the dlib python interface before you can use this file. To do this,
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# run compile_dlib_python_module.bat. This should work on any operating system
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# so long as you have CMake and boost-python installed. On Ubuntu, this can be
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# done easily by running the command: sudo apt-get install libboost-python-dev cmake
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import dlib
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import sys
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# The sequence segmentation models we work with in this example are chain structured
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# conditional random field style models. Therefore, central to a sequence segmentation
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# model is some method for converting the elements of a sequence into feature vectors.
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# That is, while you might start out representing your sequence as an array of strings, the
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# dlib interface works in terms of arrays of feature vectors. Each feature vector should
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# capture important information about its corresponding element in the original raw
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# sequence. So in this example, since we work with sequences of words and want to identify
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# names, we will create feature vectors that tell us if the word is capitalized or not. In
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# our simple data, this will be enough to identify names. Therefore, we define
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# sentence_to_vectors() which takes a sentence represented as a string and converts it into
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# an array of words and then associates a feature vector with each word.
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def sentence_to_vectors(sentence):
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# Create an empty array of vectors
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vects = dlib.vectors()
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for word in sentence.split():
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# Our vectors are very simple 1-dimensional vectors. The value of the single
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# feature is 1 if the first letter of the word is capitalized and 0 otherwise.
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if (word[0].isupper()):
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vects.append(dlib.vector([1]))
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else:
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vects.append(dlib.vector([0]))
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return vects
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# Dlib also supports the use of a sparse vector representation. This is more efficient
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# than the above form when you have very high dimensional vectors that are mostly full of
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# zeros. In dlib, each sparse vector is represented as an array of pair objects. Each
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# pair contains an index and value. Any index not listed in the vector is implicitly
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# associated with a value of zero. Additionally, when using sparse vectors with
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# dlib.train_sequence_segmenter() you can use "unsorted" sparse vectors. This means you
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# can add the index/value pairs into your sparse vectors in any order you want and don't
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# need to worry about them being in sorted order.
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def sentence_to_sparse_vectors(sentence):
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vects = dlib.sparse_vectors()
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has_cap = dlib.sparse_vector()
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no_cap = dlib.sparse_vector()
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# make has_cap equivalent to dlib.vector([1])
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has_cap.append(dlib.pair(0,1))
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# Since we didn't add anything to no_cap it is equivalent to dlib.vector([0])
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for word in sentence.split():
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if (word[0].isupper()):
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vects.append(has_cap)
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else:
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vects.append(no_cap)
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return vects
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def print_segment(sentence, names):
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words = sentence.split()
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for name in names:
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for i in name:
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sys.stdout.write(words[i] + " ")
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sys.stdout.write("\n")
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# Now let's make some training data. Each example is a sentence as well as a set of ranges
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# which indicate the locations of any names.
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names = dlib.ranges() # make an array of dlib.range objects.
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segments = dlib.rangess() # make an array of arrays of dlib.range objects.
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sentences = []
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sentences.append("The other day I saw a man named Jim Smith")
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# We want to detect person names. So we note that the name is located within the
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# range [8, 10). Note that we use half open ranges to identify segments. So in
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# this case, the segment identifies the string "Jim Smith".
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names.append(dlib.range(8, 10))
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segments.append(names)
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names.clear() # make names empty for use again below
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sentences.append("Davis King is the main author of the dlib Library")
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names.append(dlib.range(0, 2))
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segments.append(names)
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names.clear()
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sentences.append("Bob Jones is a name and so is George Clinton")
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names.append(dlib.range(0, 2))
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names.append(dlib.range(8, 10))
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segments.append(names)
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names.clear()
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sentences.append("My dog is named Bob Barker")
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names.append(dlib.range(4, 6))
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segments.append(names)
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names.clear()
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sentences.append("ABC is an acronym but John James Smith is a name")
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names.append(dlib.range(5, 8))
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segments.append(names)
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names.clear()
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sentences.append("No names in this sentence at all")
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segments.append(names)
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names.clear()
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# Now before we can pass these training sentences to the dlib tools we need to convert them
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# into arrays of vectors as discussed above. We can use either a sparse or dense
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# representation depending on our needs. In this example, we show how to do it both ways.
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use_sparse_vects = False
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if use_sparse_vects:
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# Make an array of arrays of dlib.sparse_vector objects.
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training_sequences = dlib.sparse_vectorss()
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for s in sentences:
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training_sequences.append(sentence_to_sparse_vectors(s))
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else:
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# Make an array of arrays of dlib.vector objects.
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training_sequences = dlib.vectorss()
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for s in sentences:
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training_sequences.append(sentence_to_vectors(s))
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# Now that we have a simple training set we can train a sequence segmenter. However, the
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# sequence segmentation trainer has some optional parameters we can set. These parameters
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# determine properties of the segmentation model we will learn. See the dlib documentation
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# for the sequence_segmenter object for a full discussion of their meanings.
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params = dlib.segmenter_params()
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params.window_size = 3
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params.use_high_order_features = True
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params.use_BIO_model = True
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# This is the common SVM C parameter. Larger values encourage the trainer to attempt to
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# fit the data exactly but might overfit. In general, you determine this parameter by
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# cross-validation.
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params.C = 10
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# Train a model. The model object is responsible for predicting the locations of names in
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# new sentences.
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model = dlib.train_sequence_segmenter(training_sequences, segments, params)
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# Let's print out the things the model thinks are names. The output is a set of ranges
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# which are predicted to contain names. If you run this example program you will see that
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# it gets them all correct.
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for i in range(len(sentences)):
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print_segment(sentences[i], model(training_sequences[i]))
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# Let's also try segmenting a new sentence. This will print out "Bob Bucket". Note that we
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# need to remember to use the same vector representation as we used during training.
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test_sentence = "There once was a man from Nantucket whose name rhymed with Bob Bucket"
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if use_sparse_vects:
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print_segment(test_sentence, model(sentence_to_sparse_vectors(test_sentence)))
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else:
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print_segment(test_sentence, model(sentence_to_vectors(test_sentence)))
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# We can also measure the accuracy of a model relative to some labeled data. This
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# statement prints the precision, recall, and F1-score of the model relative to the data in
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# training_sequences/segments.
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print "Test on training data:", dlib.test_sequence_segmenter(model, training_sequences, segments)
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# We can also do 5-fold cross-validation and print the resulting precision, recall, and F1-score.
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print "cross validation:", dlib.cross_validate_sequence_segmenter(training_sequences, segments, 5, params)
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