def test_classification_models(self):
""" Tests building classification models using provided learners
:return: True if all tests pass
"""
lrn_arr = self.test_classification_learners()
for lrn in lrn_arr:
try:
base_dir = normpath(dirname(__file__)) + '/../'
weka_dir = normpath(join(base_dir, 'weka'))
data_dir = normpath(join(weka_dir, 'data'))
# f = data_dir + os.sep + 'cpu.with.vendor.arff' # numeric target, with both numeric and nominal features
f = data_dir + os.sep + 'breast-cancer.arff' # nominal target
fi = open(f, 'r')
classification_dataset = fi.read()
fi.close()
data = ut.import_dataset_from_arff(classification_dataset)
lrn.buildClassifier(data)
data_new = lrn.applyClassifier(data)
self.assertIsNotNone(data_new['targetPredicted'])
except Exception, e:
print "Exception: " + str(e)
def convert_dataset_from_orange_to_scikit(dataset):
"""Converts dataset from an Orange Data Table to scikit Bunch format
:param dataset:
:return:
"""
arff_str = to_arff_string(dataset).getvalue()
dataset_new = u.import_dataset_from_arff(arff_str)
return dataset_new
def convert_dataset_from_orange_to_scikit(dataset):
"""Converts dataset from an Orange Data Table to scikit Bunch format
:param dataset:
:return:
"""
arff_str = to_arff_string(dataset).getvalue()
dataset_new = u.import_dataset_from_arff(arff_str)
return dataset_new
