def test_default_parameters(self):
cea = ht.ConstantParameter('a', value='ce.a')
cec = ht.ConstantParameter('c', value='ce.c')
toa = (None, ) * 2
args = CE, [cec, cea], fit, acc, *toa, 0
print(args)
r = ht.HyperTune(*args).tune()['params']
exp = {'a': 'ce.a', 'c': 'ce.c'}
self.assertEqual(r, exp)
def test_constant_param(self):
c = ht.ConstantParameter('c', value=10)
self.assertEqual(c.shape, 0)
self.assertEqual(c.name, 'c')
r = rand()
v = c.get_val(r)
self.assertEqual(v, 10)
ca = CA()
c = ht.ConstantParameter('a', value=ca)
v = c.get_val([-1])
self.assertEqual(v, ca)
da = DA()
c = ht.ConstantParameter('da', value=da)
v = c.get_val([-1])
self.assertEqual(v, da)
def test_parameter_scope(self):
cca = ht.ConstantParameter('a', value='cc.a')
ccb = ht.ConstantParameter('b', value='cc.b')
aio = ht.ObjectParameter('O', obj=CC, parameters=(cca, ccb))
aia = ht.ConstantParameter('a', value='ai.a')
r = ht.HyperTune(AI, [aia, aio], fit, acc,
max_evals=0).tune()['params']
exp = {'O': {'a': 'cc.a', 'b': 'cc.b'}, 'a': 'ai.a'}
self.assertEqual(r, exp)
r = ht.HyperTune(AI, [aia, aio], fit, acc,
max_evals=0).tune()['params']
self.assertEqual(r, exp)
aio = ht.ObjectParameter('O', obj=CC, parameters=(ccb, cca))
r = ht.HyperTune(AI, [aia, aio], fit, acc,
max_evals=0).tune()['params']
self.assertEqual(r, exp)
def test_categorical_param(self):
c = ht.CategoricalParameter('c', options=('a', 'b'))
self.assertEqual(c.name, 'c')
self.assertEqual(c.shape, 1)
v = c.get_val([-1])
self.assertEqual(v, 'a')
co = ht.ContinuousParameter('co', lower_bound=0, upper_bound=1)
con = ht.ConstantParameter('con', value=100)
c = ht.CategoricalParameter('c', options=('a', co, con))
self.assertEqual(c.shape, 2)
def test_object_param(self):
o = ht.ObjectParameter('o', obj=CA, parameters=tuple([]))
self.assertEqual(o.shape, 0)
prams = 'o', DA, (1, )
self.assertRaises(ValueError, ht.ObjectParameter, *prams)
cba = ht.ConstantParameter('a', value='a')
o = ht.ObjectParameter('o', obj=CB, parameters=(cba, ))
self.assertEqual(o.shape, 0)
dba = ht.ConstantParameter('a', value='aaa')
o = ht.ObjectParameter('', obj=DB, parameters=(dba, ))
v = o.get_val([1])
self.assertEqual(v, 'aaa')
cba = ht.DiscreteParameter('a', lower_bound=0, upper_bound=1)
o = ht.ObjectParameter('o', obj=CB, parameters=(cba, ))
self.assertEqual(o.shape, 1)
p = [-1]
v = o.get_val(p)
self.assertEqual(v.a, cba.get_val(p))
cca = ht.DiscreteParameter('a', lower_bound=0, upper_bound=1)
ccb = ht.DiscreteParameter('b', lower_bound=-100, upper_bound=50)
o = ht.ObjectParameter('o', obj=CC, parameters=(cca, ccb))
p = [0.5, 0.75]
v = o.get_val(p)
self.assertEqual(v.a, cca.get_val(p[:1]))
self.assertEqual(v.b, ccb.get_val(p[1:]))
cca = ht.ConstantParameter('a', value='aaa')
ccb = ht.ConstantParameter('b', value='bbb')
o = ht.ObjectParameter('o', obj=CC, parameters=(ccb, cca))
p = [0.5, 0.75]
v = o.get_val(p)
self.assertEqual(v.a, cca.get_val(p[:1]))
self.assertEqual(v.b, ccb.get_val(p[1:]))
def test_parameter_scope2(self):
cca = ht.ConstantParameter('a', value='cc.a')
ccb = ht.ConstantParameter('b', value='cc.b')
aio = ht.ObjectParameter('O', obj=CC, parameters=(ccb, cca))
aia = ht.ConstantParameter('a', value='ai.a')
cdo = ht.ObjectParameter('O', obj=AI, parameters=(aio, aia))
cda = ht.ConstantParameter('a', value='cd.a')
cdb = ht.ConstantParameter('b', value='cd.b')
r = ht.HyperTune(CD, [cda, cdb, cdo], fit, acc,
max_evals=0).tune()['params']
exp = {
'a': 'cd.a',
'b': 'cd.b',
'O': {
'O': {
'a': 'cc.a',
'b': 'cc.b'
},
'a': 'ai.a'
}
}
self.assertEqual(r, exp)
def test_tuple_param(self):
t = ht.TupleParameter('t', values=(1, 1, 1))
self.assertEqual(t.shape, 0)
c = ht.ContinuousParameter('c', lower_bound=0, upper_bound=1)
d = ht.DiscreteParameter('d', lower_bound=0, upper_bound=1)
co = ht.ConstantParameter('co', value='world')
t = ht.TupleParameter('t', values=(c, d, co))
self.assertEqual(t.shape, 2)
r = [-1, 0.5]
v = t.get_val(r)
exp = (0.0, 1, 'world')
self.assertEqual(v, exp)
t = ht.TupleParameter('t', values=(c, {'z': -1}, d))
r = [-1, 0.5]
v = t.get_val(r)
exp = (0.0, {'z': -1}, 1)
self.assertEqual(v, exp)