def classify(self, text):
"""Classifies a string of text."""
raise NotImplementedError('Must implement a "classify" method.')
python类classify()的实例源码
def classify(self, text):
"""Classifies the text.
:param str text: A string of text.
"""
text_features = self.extract_features(text)
return self.classifier.classify(text_features)
def accuracy(self, test_set, format=None):
"""Compute the accuracy on a test set.
:param test_set: A list of tuples of the form ``(text, label)``, or a
file pointer.
:param format: If ``test_set`` is a filename, the file format, e.g.
``"csv"`` or ``"json"``. If ``None``, will attempt to detect the
file format.
"""
if is_filelike(test_set):
test_data = self._read_data(test_set)
else: # test_set is a list of tuples
test_data = test_set
test_features = [(self.extract_features(d), c) for d, c in test_data]
return nltk.classify.accuracy(self.classifier, test_features)
def train(self, *args, **kwargs):
"""Train the classifier with a labeled and unlabeled feature sets and return
the classifier. Takes the same arguments as the wrapped NLTK class.
This method is implicitly called when calling ``classify`` or
``accuracy`` methods and is included only to allow passing in arguments
to the ``train`` method of the wrapped NLTK class.
:rtype: A classifier
"""
self.classifier = self.nltk_class.train(self.positive_features,
self.unlabeled_features,
self.positive_prob_prior)
return self.classifier
def classify(self, text):
"""Classifies the text.
:param str text: A string of text.
"""
text_features = self.extract_features(text)
return self.classifier.classify(text_features)
def accuracy(self, test_set, format=None):
"""Compute the accuracy on a test set.
:param test_set: A list of tuples of the form ``(text, label)``, or a
file pointer.
:param format: If ``test_set`` is a filename, the file format, e.g.
``"csv"`` or ``"json"``. If ``None``, will attempt to detect the
file format.
"""
if is_filelike(test_set):
test_data = self._read_data(test_set)
else: # test_set is a list of tuples
test_data = test_set
test_features = [(self.extract_features(d), c) for d, c in test_data]
return nltk.classify.accuracy(self.classifier, test_features)
def train(self, *args, **kwargs):
"""Train the classifier with a labeled and unlabeled feature sets and return
the classifier. Takes the same arguments as the wrapped NLTK class.
This method is implicitly called when calling ``classify`` or
``accuracy`` methods and is included only to allow passing in arguments
to the ``train`` method of the wrapped NLTK class.
:rtype: A classifier
"""
self.classifier = self.nltk_class.train(self.positive_features,
self.unlabeled_features,
self.positive_prob_prior)
return self.classifier
def classify(query,
engine=engine,
threshold=.85,
limit=5):
"""spell out most probable diseases and respective percentages."""
words = preprocess(' '.join(query))
print('understanding {}...'.format(words))
objects = engine.prob_classify(words)
keys = list(objects.samples())
samples = [tuple((key, objects.prob(key))) for key in keys]
return feed_conversation(samples, limit, threshold)
def classify(self, text):
"""Classifies a string of text."""
raise NotImplementedError('Must implement a "classify" method.')
