def load_training_set(self, filename, encoding='UTF-8'):
"""\
Load the given training data set into memory and strip it if
configured to via the train_part parameter.
"""
log_info('Loading training data set from ' + str(filename) + '...')
train = DataSet()
train.load_from_arff(filename, encoding)
if self.train_part < 1:
train = train.subset(0, int(round(self.train_part * len(train))),
copy=False)
return train
def load_training_set(self, filename, encoding='UTF-8'):
"""\
Load the given training data set into memory and strip it if
configured to via the train_part parameter.
"""
log_info('Loading training data set from ' + str(filename) + '...')
train = DataSet()
train.load_from_arff(filename, encoding)
if self.train_part < 1:
train = train.subset(0,
int(round(self.train_part * len(train))),
copy=False)
return train
def evaluate(self, test_file, encoding='UTF-8', classif_file=None):
"""\
Evaluate on the given test data file. Return accuracy.
If classif_file is set, save the classification results to this file.
"""
test = DataSet()
test.load_from_arff(test_file, encoding)
values = self.classify(test)
golden = self.get_classes(test, dtype=None)
if classif_file is not None:
classif = DataSet()
classif.load_from_vect(test.get_attrib(self.class_attr), values)
classif.rename_attrib(self.class_attr, self.PREDICTED)
test.merge(classif)
test.save_to_arff(classif_file, encoding)
return zero_one_score(golden, values)
def evaluate(self, test_file, encoding='UTF-8', classif_file=None):
"""\
Evaluate on the given test data file. Return accuracy.
If classif_file is set, save the classification results to this file.
"""
test = DataSet()
test.load_from_arff(test_file, encoding)
values = self.classify(test)
golden = self.get_classes(test, dtype=None)
if classif_file is not None:
classif = DataSet()
classif.load_from_vect(test.get_attrib(self.class_attr), values)
classif.rename_attrib(self.class_attr, self.PREDICTED)
test.merge(classif)
test.save_to_arff(classif_file, encoding)
return zero_one_score(golden, values)
