Classifiers

Classifiers label tokens with category labels (or class labels). Typically, labels are represented with strings (such as "health" or "sports". In NLTK, classifiers are defined using classes that implement the ClassifyI interface:

>>> import nltk
>>> nltk.usage(nltk.classify.ClassifierI)
ClassifierI supports the following operations:
  - self.classify(featureset)
  - self.classify_many(featuresets)
  - self.labels()
  - self.prob_classify(featureset)
  - self.prob_classify_many(featuresets)

NLTK defines several classifier classes:

Classifiers are typically created by training them on a training corpus.

Regression Tests

We define a very simple training corpus with 3 binary features: ['a', 'b', 'c'], and are two labels: ['x', 'y']. We use a simple feature set so that the correct answers can be calculated analytically (although we haven't done this yet for all tests).

>>> train = [
...     (dict(a=1,b=1,c=1), 'y'),
...     (dict(a=1,b=1,c=1), 'x'),
...     (dict(a=1,b=1,c=0), 'y'),
...     (dict(a=0,b=1,c=1), 'x'),
...     (dict(a=0,b=1,c=1), 'y'),
...     (dict(a=0,b=0,c=1), 'y'),
...     (dict(a=0,b=1,c=0), 'x'),
...     (dict(a=0,b=0,c=0), 'x'),
...     (dict(a=0,b=1,c=1), 'y'),
...     ]
>>> test = [
...     (dict(a=1,b=0,c=1)), # unseen
...     (dict(a=1,b=0,c=0)), # unseen
...     (dict(a=0,b=1,c=1)), # seen 3 times, labels=y,y,x
...     (dict(a=0,b=1,c=0)), # seen 1 time, label=x
...     ]

Test the Naive Bayes classifier:

>>> classifier = nltk.classify.NaiveBayesClassifier.train(train)
>>> sorted(classifier.labels())
['x', 'y']
>>> classifier.classify_many(test)
['y', 'x', 'y', 'x']
>>> for pdist in classifier.prob_classify_many(test):
...     print('%.4f %.4f' % (pdist.prob('x'), pdist.prob('y')))
0.3203 0.6797
0.5857 0.4143
0.3792 0.6208
0.6470 0.3530
>>> classifier.show_most_informative_features()
Most Informative Features
                       c = 0                   x : y      =      2.0 : 1.0
                       c = 1                   y : x      =      1.5 : 1.0
                       a = 1                   y : x      =      1.4 : 1.0
                       b = 0                   x : y      =      1.2 : 1.0
                       a = 0                   x : y      =      1.2 : 1.0
                       b = 1                   y : x      =      1.1 : 1.0

Test the Decision Tree classifier:

>>> classifier = nltk.classify.DecisionTreeClassifier.train(
...     train, entropy_cutoff=0,
...                                                support_cutoff=0)
>>> sorted(classifier.labels())
['x', 'y']
>>> print(classifier)
c=0? .................................................. x
  a=0? ................................................ x
  a=1? ................................................ y
c=1? .................................................. y
<BLANKLINE>
>>> classifier.classify_many(test)
['y', 'y', 'y', 'x']
>>> for pdist in classifier.prob_classify_many(test):
...     print('%.4f %.4f' % (pdist.prob('x'), pdist.prob('y')))
Traceback (most recent call last):
  . . .
NotImplementedError

Test SklearnClassifier, which requires the scikit-learn package.

>>> from nltk.classify import SklearnClassifier
>>> from sklearn.naive_bayes import BernoulliNB
>>> from sklearn.svm import SVC
>>> train_data = [({"a": 4, "b": 1, "c": 0}, "ham"),
...               ({"a": 5, "b": 2, "c": 1}, "ham"),
...               ({"a": 0, "b": 3, "c": 4}, "spam"),
...               ({"a": 5, "b": 1, "c": 1}, "ham"),
...               ({"a": 1, "b": 4, "c": 3}, "spam")]
>>> classif = SklearnClassifier(BernoulliNB()).train(train_data)
>>> test_data = [{"a": 3, "b": 2, "c": 1},
...              {"a": 0, "b": 3, "c": 7}]
>>> classif.classify_many(test_data)
['ham', 'spam']
>>> classif = SklearnClassifier(SVC(), sparse=False).train(train_data)
>>> classif.classify_many(test_data)
['ham', 'spam']

Test the Maximum Entropy classifier training algorithms; they should all generate the same results.

>>> def print_maxent_test_header():
...     print(' '*11+''.join(['      test[%s]  ' % i
...                           for i in range(len(test))]))
...     print(' '*11+'     p(x)  p(y)'*len(test))
...     print('-'*(11+15*len(test)))

>>> def test_maxent(algorithm):
...     print('%11s' % algorithm, end=' ')
...     try:
...         classifier = nltk.classify.MaxentClassifier.train(
...                         train, algorithm, trace=0, max_iter=1000)
...     except Exception as e:
...         print('Error: %r' % e)
...         return
...
...     for featureset in test:
...         pdist = classifier.prob_classify(featureset)
...         print('%8.2f%6.2f' % (pdist.prob('x'), pdist.prob('y')), end=' ')
...     print()

>>> print_maxent_test_header(); test_maxent('GIS'); test_maxent('IIS')
                 test[0]        test[1]        test[2]        test[3]
                p(x)  p(y)     p(x)  p(y)     p(x)  p(y)     p(x)  p(y)
-----------------------------------------------------------------------
        GIS     0.16  0.84     0.46  0.54     0.41  0.59     0.76  0.24
        IIS     0.16  0.84     0.46  0.54     0.41  0.59     0.76  0.24

>>> test_maxent('MEGAM'); test_maxent('TADM') # doctest: +SKIP
        MEGAM   0.16  0.84     0.46  0.54     0.41  0.59     0.76  0.24
        TADM    0.16  0.84     0.46  0.54     0.41  0.59     0.76  0.24

Regression tests for TypedMaxentFeatureEncoding

>>> from nltk.classify import maxent
>>> train = [
...     ({'a': 1, 'b': 1, 'c': 1}, 'y'),
...     ({'a': 5, 'b': 5, 'c': 5}, 'x'),
...     ({'a': 0.9, 'b': 0.9, 'c': 0.9}, 'y'),
...     ({'a': 5.5, 'b': 5.4, 'c': 5.3}, 'x'),
...     ({'a': 0.8, 'b': 1.2, 'c': 1}, 'y'),
...     ({'a': 5.1, 'b': 4.9, 'c': 5.2}, 'x')
... ]

>>> test = [
...     {'a': 1, 'b': 0.8, 'c': 1.2},
...     {'a': 5.2, 'b': 5.1, 'c': 5}
... ]

>>> encoding = maxent.TypedMaxentFeatureEncoding.train(
...     train, count_cutoff=3, alwayson_features=True)

>>> classifier = maxent.MaxentClassifier.train(
...     train, bernoulli=False, encoding=encoding, trace=0)

>>> classifier.classify_many(test)
['y', 'x']