def train(self, trainer, training_set, save_classifier=None, **kwargs):
"""
Train classifier on the training set, optionally saving the output in the
file specified by `save_classifier`.
Additional arguments depend on the specific trainer used. For example,
a MaxentClassifier can use `max_iter` parameter to specify the number
of iterations, while a NaiveBayesClassifier cannot.
:param trainer: `train` method of a classifier.
E.g.: NaiveBayesClassifier.train
:param training_set: the training set to be passed as argument to the
classifier `train` method.
:param save_classifier: the filename of the file where the classifier
will be stored (optional).
:param kwargs: additional parameters that will be passed as arguments to
the classifier `train` function.
:return: A classifier instance trained on the training set.
:rtype:
"""
print("Training classifier")
self.classifier = trainer(training_set, **kwargs)
if save_classifier:
save_file(self.classifier, save_classifier)
return self.classifier
def train(self, trainer, training_set, save_classifier=None, **kwargs):
"""
Train classifier on the training set, optionally saving the output in the
file specified by `save_classifier`.
Additional arguments depend on the specific trainer used. For example,
a MaxentClassifier can use `max_iter` parameter to specify the number
of iterations, while a NaiveBayesClassifier cannot.
:param trainer: `train` method of a classifier.
E.g.: NaiveBayesClassifier.train
:param training_set: the training set to be passed as argument to the
classifier `train` method.
:param save_classifier: the filename of the file where the classifier
will be stored (optional).
:param kwargs: additional parameters that will be passed as arguments to
the classifier `train` function.
:return: A classifier instance trained on the training set.
:rtype:
"""
print("Training classifier")
self.classifier = trainer(training_set, **kwargs)
if save_classifier:
save_file(self.classifier, save_classifier)
return self.classifier