def should_raise_exception(param, value):
try:
config.set(param, value)
except:
assert True
else:
assert False
def test_pebl(numvars, numsamples, greedy_iters, cachesize):
print "Testing with #vars=%d, #samples=%d, iters=%d, cachesize=%d" % (
numvars, numsamples, greedy_iters, cachesize)
config.set('localscore_cache.maxsize', cachesize)
d = data.Dataset(N.random.rand(numsamples, numvars))
d.discretize()
g = greedy.GreedyLearner(d, max_iterations=greedy_iters)
g.run()
return g
def test_configobj1(self):
expected = \
"""[test]
param1 = foo
param0 = foo
[test1]
param1 = 5
"""
config.IntParameter('test1.param1', 'a param', default=5)
config.set('test.param1', 'foo')
params = [config._parameters.get(x) for x in ('test.param0', 'test.param1', 'test1.param1')]
tmpfile = NamedTemporaryFile(prefix="pebl.test")
config.configobj(params).write(tmpfile)
tmpfile.file.seek(0)
actual = tmpfile.read()
assert actual == expected
def test_set8(self):
config.set('test.param1', 'foo')
config.set('test.param1', 'bar')
should_raise_exception('test.param1', 'foobar')
def test_set7(self):
config.set('test.param7', 1.50)
config.set('test.param7', "1.50")
config.set('test.param7', "1.5e0")
assert config.get('test.param7') == 1.5
assert isinstance(config.get('test.param7'), float)
def test_set4(self):
config.set('test.param5', 50) # no exception
should_raise_exception('test.param5', 5)
assert config.get('test.param5') == 50
def test_set3(self):
config.set('test.param4', 150) # no exception
should_raise_exception('test.param4', 50)
def test_get4(self):
config.set('test.param2', "10")
assert isinstance(config.get('test.param2'), int)
assert config.get('test.param2') == 10
def setUp(self):
config.set("evaluator.missingdata_evaluator", "exact")
self.data = data.fromfile(testfile("testdata13.txt")).subset(samples=range(5))
self.learner = greedy.GreedyLearner(self.data, max_iterations=10)
def setUp(self):
config.set("evaluator.missingdata_evaluator", self.missing_evaluator)
self.data = data.fromfile(testfile("testdata13.txt"))
self.learner = self.learnertype(self.data)