mirror of https://github.com/davisking/dlib.git
194 lines
8.1 KiB
Python
Executable File
194 lines
8.1 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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# This example shows how to use dlib to learn to do sequence segmentation. In
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# a sequence segmentation task we are given a sequence of objects (e.g. words in
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# a sentence) and we are supposed to detect certain subsequences (e.g. the names
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# of people). Therefore, in the code below we create some very simple training
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# sequences and use them to learn a sequence segmentation model. In particular,
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# our sequences will be sentences represented as arrays of words and our task
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# will be to learn to identify person names. Once we have our segmentation
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# model we can use it to find names in new sentences, as we will show.
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#
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# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
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# You can install dlib using the command:
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# pip install dlib
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#
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# Alternatively, if you want to compile dlib yourself then go into the dlib
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# root folder and run:
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# python setup.py install
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#
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# Compiling dlib should work on any operating system so long as you have
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# CMake installed. On Ubuntu, this can be done easily by running the
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# command:
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# sudo apt-get install cmake
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#
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import sys
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import dlib
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# The sequence segmentation models we work with in this example are chain
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# structured conditional random field style models. Therefore, central to a
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# sequence segmentation model is some method for converting the elements of a
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# sequence into feature vectors. That is, while you might start out representing
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# your sequence as an array of strings, the dlib interface works in terms of
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# arrays of feature vectors. Each feature vector should capture important
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# information about its corresponding element in the original raw sequence. So
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# 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
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# or not. In our simple data, this will be enough to identify names.
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# Therefore, we define sentence_to_vectors() which takes a sentence represented
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# as a string and converts it into an array of words and then associates a
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# 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
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# single feature is 1 if the first letter of the word is capitalized and
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# 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
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# efficient than the above form when you have very high dimensional vectors that
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# are mostly full of zeros. In dlib, each sparse vector is represented as an
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# array of pair objects. Each pair contains an index and value. Any index not
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# listed in the vector is implicitly associated with a value of zero.
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# Additionally, when using sparse vectors with dlib.train_sequence_segmenter()
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# you can use "unsorted" sparse vectors. This means you can add the index/value
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# pairs into your sparse vectors in any order you want and don't need to worry
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# 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
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# 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
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# set of ranges 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
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# the range [8, 10). Note that we use half open ranges to identify segments.
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# So in 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
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# convert them into arrays of vectors as discussed above. We can use either a
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# sparse or dense representation depending on our needs. In this example, we
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# 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.
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# However, the sequence segmentation trainer has some optional parameters we can
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# set. These parameters determine properties of the segmentation model we will
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# learn. See the dlib documentation for the sequence_segmenter object for a
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# 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
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# attempt to fit the data exactly but might overfit. In general, you determine
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# this parameter by 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
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# of names in 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
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# of ranges which are predicted to contain names. If you run this example
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# program you will see that it gets them all correct.
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for i, s in enumerate(sentences):
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print_segment(s, model(training_sequences[i]))
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# Let's also try segmenting a new sentence. This will print out "Bob Bucket".
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# Note that we need to remember to use the same vector representation as we used
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# during training.
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test_sentence = "There once was a man from Nantucket " \
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"whose name rhymed with Bob Bucket"
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if use_sparse_vects:
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print_segment(test_sentence,
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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.
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# This statement prints the precision, recall, and F1-score of the model
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# relative to the data in training_sequences/segments.
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print("Test on training data: {}".format(
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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,
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# recall, and F1-score.
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print("Cross validation: {}".format(
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dlib.cross_validate_sequence_segmenter(training_sequences, segments, 5,
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params)))
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