def test_pyll_scope_doesnt_overwrite():
raised = False
try:
def float(x):
return x + 1
as_pyll(partial(float, 3))
except ValueError:
raised = True
assert raised
def test_pyll_list_tuple_nested():
x = as_partialplus([[5, 3, (5, 3)], (4, 5)])
y = as_pyll(x)
# rec_eval always uses tuple
val_y = rec_eval(y)
# Correct for tuple-only in rec_eval.
assert evaluate(x) == [list(val_y[0]), val_y[1]]
def test_randint():
v = variable('some_random_int', value_type=int, distribution='randint',
maximum=5)
p = as_pyll(v)
assert p.name == 'hyperopt_param'
assert p.pos_args[0].obj == 'some_random_int'
assert p.pos_args[1].name == 'randint'
assert p.pos_args[1].arg['upper'].obj == 6
# upper is not a constant
v2 = variable('some_other_int', value_type=int, distribution='randint',
maximum=partial(operator.add, 2, 3))
p2 = as_pyll(v2)
assert p2.name == 'hyperopt_param'
assert p2.pos_args[0].obj == 'some_other_int'
assert p2.pos_args[1].name == 'randint'
assert p2.pos_args[1].arg['upper'].name == 'add'
def test_dict():
x = as_partialplus({'x': partial(float,
partial(float,
partial(int, 3.3))) / 2,
'y': partial(float, 3)
})
y = as_pyll(x)
assert evaluate(x) == rec_eval(y)
def test_pyll_deeply_nested_func():
# N.B. uses stuff that isn't in the SymbolTable yet, must remove.
try:
def my_add(x, y):
return x + y
x = as_partialplus(
(partial(float, partial(my_add, 0, partial(int, 3.3))) / 2,
partial(float, 3))
)
y = as_pyll(x)
evaluate(x) == rec_eval(y)
finally:
scope.undefine(my_add)
def test_uniform_categorical():
p = as_pyll(variable('foo', value_type=[-1, 1, 4]))
assert p.name == 'getitem'
assert p.pos_args[0].name == 'pos_args'
assert p.pos_args[1].name == 'hyperopt_param'
assert p.pos_args[1].pos_args[0].name == 'literal'
assert p.pos_args[1].pos_args[0].obj == 'foo'
assert p.pos_args[1].pos_args[1].name == 'randint'
# Make sure this executes and yields a value in the right domain.
recursive_set_rng_kwarg(p, np.random)
try:
values = [rec_eval(p) for _ in xrange(10)]
except Exception:
assert False
assert all(v in [-1, 1, 4] for v in values)
def check_continuous_variable(label, dist_name, **params):
v = variable(label, value_type=float, distribution=dist_name,
**params)
p = as_pyll(v)
assert p.name == 'float' # Implementation detail
assert p.pos_args[0].name == 'hyperopt_param'
assert p.pos_args[0].pos_args[0].obj == label
assert p.pos_args[0].pos_args[1].name == dist_name
if 'minimum' in params:
params['low'] = params['minimum']
del params['minimum']
if 'maximum' in params:
params['high'] = params['maximum']
del params['maximum']
for key in params:
assert p.pos_args[0].pos_args[1].arg[key].obj == params[key]
def test_uniform_choice():
p = as_pyll(choice(variable('foo', value_type=[7, 9, 11]),
(7, 'rst'),
(9, 'uvw'),
(11, 'xyz')))
assert p.name == 'switch'
assert p.pos_args[0].name == 'hyperopt_param'
assert p.pos_args[0].pos_args[0].obj == 'foo'
assert p.pos_args[0].pos_args[1].name == 'randint'
assert p.pos_args[0].pos_args[1].arg['upper'].obj == 3
# Make sure this executes and yields a value in the right domain.
recursive_set_rng_kwarg(p, np.random)
try:
values = [rec_eval(p) for _ in xrange(10)]
except Exception:
assert False
assert all(v in ['rst', 'uvw', 'xyz'] for v in values)
def test_nonuniform_categorical():
p = as_pyll(variable('baz', value_type=[3, 5, 9],
distribution='categorical',
p=[0.1, 0.4, 0.5]))
assert p.name == 'getitem'
assert p.pos_args[0].name == 'pos_args'
assert p.pos_args[1].name == 'hyperopt_param'
assert p.pos_args[1].pos_args[0].name == 'literal'
assert p.pos_args[1].pos_args[0].obj == 'baz'
assert p.pos_args[1].pos_args[1].name == 'categorical'
assert p.pos_args[1].pos_args[1].arg['p'].name == 'pos_args'
assert p.pos_args[1].pos_args[1].arg['p'].pos_args[0].obj == 0.1
assert p.pos_args[1].pos_args[1].arg['p'].pos_args[1].obj == 0.4
assert p.pos_args[1].pos_args[1].arg['p'].pos_args[2].obj == 0.5
# Make sure this executes and yields a value in the right domain.
recursive_set_rng_kwarg(p, np.random)
try:
values = [rec_eval(p) for _ in xrange(10)]
except Exception:
assert False
assert all(v in [3, 5, 9] for v in values)
def test_nonuniform_choice():
var = variable('blu', value_type=[2, 4, 8], distribution='categorical',
p=[0.2, 0.7, 0.1])
p = as_pyll(choice(var,
(2, 'abc'),
(4, 'def'),
(8, 'ghi')))
assert p.name == 'switch'
assert p.pos_args[0].name == 'hyperopt_param'
assert p.pos_args[0].pos_args[0].obj == 'blu'
assert p.pos_args[0].pos_args[1].name == 'categorical'
assert p.pos_args[0].pos_args[1].arg['p'].name == 'pos_args'
assert p.pos_args[0].pos_args[1].arg['p'].pos_args[0].obj == 0.2
assert p.pos_args[0].pos_args[1].arg['p'].pos_args[1].obj == 0.7
assert p.pos_args[0].pos_args[1].arg['p'].pos_args[2].obj == 0.1
# Make sure this executes and yields a value in the right domain.
recursive_set_rng_kwarg(p, np.random)
try:
values = [rec_eval(p) for _ in xrange(10)]
except Exception:
assert False
assert all(v in ['abc', 'def', 'ghi'] for v in values)
def test_pyll_nested_func():
x = partial(float, partial(int, 5.5))
y = as_pyll(x)
assert evaluate(x) == rec_eval(y)
def test_pyll_func():
# N.B. Only uses stuff that's already in the SymbolTable.
x = partial(float, 5)
y = as_pyll(x)
assert evaluate(x) == rec_eval(y)
def test_pyll_list():
x = as_partialplus([5, 3, 9])
y = as_pyll(x)
# rec_eval always uses tuple
assert evaluate(x) == list(rec_eval(y))
def test_pyll_tuple():
x = as_partialplus((6, 9, 4))
y = as_pyll(x)
assert evaluate(x) == rec_eval(y)
def test_repeated_node():
q = partial(float, 5)
p = as_pyll(as_partialplus([q, q, [q]]))
assert p.pos_args[0] is p.pos_args[1]
assert p.pos_args[0] is p.pos_args[2].pos_args[0]