def test_two_filters(self):
""" Tests saturation_filter (normal and prune) and classification_filter
"""
import cf_noise_detection.library as l
import cf_weka.classification as c
learner = c.j48()
data = ut.load_UCI_dataset("iris")
inp_dict = {'data': data, 'satur_type': 'normal'}
out_dict = l.saturation_filter(inp_dict, None)
self.assertGreaterEqual(len(out_dict.keys()), 1)
inp_dict = {'data': data, 'satur_type': 'prune'}
out_dict = l.saturation_filter(inp_dict, None)
self.assertGreaterEqual(len(out_dict.keys()), 1)
inp_dict = {
'learner': learner,
'data': data,
'timeout': 60.0,
'k_folds': 10
}
out_dict = l.classification_filter(inp_dict, None)
self.assertGreaterEqual(len(out_dict.keys()), 1)
def test_two_filters(self):
""" Tests saturation_filter (normal and prune) and classification_filter
"""
import cf_noise_detection.library as l
import cf_weka.classification as c
learner = c.j48()
data = ut.load_UCI_dataset("iris")
inp_dict = {'data': data, 'satur_type': 'normal'}
out_dict = l.saturation_filter(inp_dict, None)
self.assertGreaterEqual(len(out_dict.keys()), 1)
inp_dict = {'data': data, 'satur_type': 'prune'}
out_dict = l.saturation_filter(inp_dict, None)
self.assertGreaterEqual(len(out_dict.keys()), 1)
inp_dict = {'learner': learner, 'data': data, 'timeout': 60.0, 'k_folds': 10}
out_dict = l.classification_filter(inp_dict, None)
self.assertGreaterEqual(len(out_dict.keys()), 1)
def test_add_class_noise(self):
import cf_noise_detection.library as l
data = ut.load_UCI_dataset("iris")
inp_dict = {'data': data, 'noise_level': 10.0, 'rnd_seed': 1}
out_dict = l.add_class_noise(inp_dict)
self.assertGreaterEqual(len(out_dict["noise_inds"]), 1)
def test_add_class_noise(self):
import cf_noise_detection.library as l
data = ut.load_UCI_dataset("iris")
inp_dict = {'data': data, 'noise_level': 10.0, 'rnd_seed': 1}
out_dict = l.add_class_noise(inp_dict)
self.assertGreaterEqual(len(out_dict["noise_inds"]), 1)
def test_regression_models(self):
""" Tests building regression models using provided learners"""
num_exceptions = 0
lrn_arr = self.test_regression_learners()
for lrn in lrn_arr:
try:
regression_dataset = ut.load_UCI_dataset("boston")
ev.build_classifier(lrn, regression_dataset)
except Exception, e:
num_exceptions += 1
print "Exception: " + str(e)
def test_regression_models(self):
""" Tests building regression models using provided learners"""
num_exceptions = 0
lrn_arr = self.test_regression_learners()
for lrn in lrn_arr:
try:
regression_dataset = ut.load_UCI_dataset("boston")
ev.build_classifier(lrn, regression_dataset)
except Exception, e:
num_exceptions += 1
print "Exception: " + str(e)
def test_classification_models(self):
""" Tests building classification models using provided learners"""
num_exceptions = 0
lrn_arr = self.test_classification_learners()
for lrn in lrn_arr:
try:
classification_dataset = ut.load_UCI_dataset("iris")
ev.build_classifier(lrn, classification_dataset)
except Exception as e:
num_exceptions += 1
print("Exception: " + str(e))
self.assertIs(num_exceptions, 0)
def test_noise_rank(self):
"""Tests noise rank widget
"""
import cf_noise_detection.library as l
import cf_weka.classification as c
learner = c.j48()
data = ut.load_UCI_dataset("iris")
inp_dict = {'learner': learner, 'data': data, 'timeout': 60.0, 'k_folds': 10}
out_dict = l.classification_filter(inp_dict, None)
inp_dict = {'noise': [out_dict['noise_dict']],
'data': data}
out_dict = l.noiserank(inp_dict)
self.assertGreaterEqual(len(out_dict['allnoise']), 1)
def test_noise_rank(self):
"""Tests noise rank widget
"""
import cf_noise_detection.library as l
import cf_weka.classification as c
learner = c.j48()
data = ut.load_UCI_dataset("iris")
inp_dict = {
'learner': learner,
'data': data,
'timeout': 60.0,
'k_folds': 10
}
out_dict = l.classification_filter(inp_dict, None)
inp_dict = {'noise': [out_dict['noise_dict']], 'data': data}
out_dict = l.noiserank(inp_dict)
self.assertGreaterEqual(len(out_dict['allnoise']), 1)
