def test_multinomial(self):
# Test that raw_random.multinomial generates the same results as numpy.
# Check over two calls to see if the random state is correctly updated.
rng_R = random_state_type()
post_r, out = multinomial(rng_R, (7, 3), 6, [0.2] * 5)
f = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_r,
mutable=True,
)
],
[out],
accept_inplace=True,
)
numpy_rng = np.random.RandomState(utt.fetch_seed())
(val0, ) = f()
(val1, ) = f()
numpy_val0 = numpy_rng.multinomial(6, [0.2] * 5, (7, 3))
numpy_val1 = numpy_rng.multinomial(6, [0.2] * 5, (7, 3))
assert np.all(val0 == numpy_val0)
assert np.all(val1 == numpy_val1)
assert val0.shape == (7, 3, 5)
assert val1.shape == (7, 3, 5)
def test_poisson(self):
# Test that raw_random.poisson generates the same results as numpy.
# Check over two calls to see if the random state is correctly updated.
rng_R = random_state_type()
# Use non-default parameters, and larger dimensions because of
# the integer nature of the result
post_r, out = poisson(rng_R, lam=5, size=(11, 8))
f = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_r,
mutable=True,
)
],
[out],
accept_inplace=True,
)
numpy_rng = np.random.RandomState(utt.fetch_seed())
val0 = f()
val1 = f()
numpy_val0 = numpy_rng.poisson(5, size=(11, 8))
numpy_val1 = numpy_rng.poisson(5, size=(11, 8))
assert np.allclose(val0, numpy_val0)
assert np.allclose(val1, numpy_val1)
def test_binomial(self):
# Test that raw_random.binomial generates the same results as numpy.
# Check over two calls to see if the random state is correctly updated.
rng_R = random_state_type()
# Use non-default parameters, and larger dimensions because of
# the integer nature of the result
post_r, bin = binomial(rng_R, (7, 12), 5, 0.8)
f = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_r,
mutable=True,
)
],
[bin],
accept_inplace=True,
)
numpy_rng = np.random.RandomState(utt.fetch_seed())
val0 = f()
val1 = f()
numpy_val0 = numpy_rng.binomial(5, 0.8, size=(7, 12))
numpy_val1 = numpy_rng.binomial(5, 0.8, size=(7, 12))
assert np.all(val0 == numpy_val0)
assert np.all(val1 == numpy_val1)
def test_normal(self):
# Test that raw_random.normal generates the same results as numpy.
# Check over two calls to see if the random state is correctly updated.
rng_R = random_state_type()
# Use non-default parameters
post_r, out = normal(rng_R, (2, 3), 4.0, 2.0)
f = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_r,
mutable=True,
)
],
[out],
accept_inplace=True,
)
numpy_rng = np.random.RandomState(utt.fetch_seed())
val0 = f()
val1 = f()
numpy_val0 = numpy_rng.normal(4.0, 2.0, size=(2, 3))
numpy_val1 = numpy_rng.normal(4.0, 2.0, size=(2, 3))
assert np.allclose(val0, numpy_val0)
assert np.allclose(val1, numpy_val1)
def test_random_function_ndim(self):
# Test that random_function helper function accepts argument ndim
rng_R = random_state_type()
# ndim is an optional argument indicating the length of the 'shape'
# ndim not specified, OK
post_out4, out4 = uniform(rng_R, (4, ))
# ndim specified, consistent with shape, OK
post_out1_4, out1_4 = uniform(rng_R, (4, ), ndim=1)
post_out2_4_4, out2_4_4 = uniform(rng_R, (4, 4), ndim=2)
# ndim specified, but not compatible with shape
with pytest.raises(ValueError):
uniform(rng_R, (4, ), ndim=2)
f_ok = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_out2_4_4,
mutable=True,
)
],
[out4, out1_4, out2_4_4],
accept_inplace=True,
)
# The correct cases should execute properly
o4, o1_4, o2_4_4 = f_ok()
# Check the sanity of the answers
assert np.allclose(o4, o1_4)
assert np.allclose(o4, o2_4_4[0])
def test_no_inplace(self):
# Test that when not running inplace, the RandomState is not updated
rf = RandomFunction("uniform", tensor.dvector)
rng_R = random_state_type()
post_r, out = rf(rng_R, (3, ), 0.0, 1.0)
f = compile.function([rng_R], [post_r, out])
rng = np.random.RandomState(utt.fetch_seed())
rng0, val0 = f(rng)
rng_ = np.random.RandomState(utt.fetch_seed())
# rng should still be in a fresh state
assert rng_R.type.values_eq(rng, rng_)
# rng0 should be in an updated state
assert not rng_R.type.values_eq(rng, rng0)
f2 = compile.function(
[compile.In(rng_R, value=rng, update=post_r, mutable=False)],
[post_r, out])
rng2, val2 = f2()
# rng should be in a fresh state
assert rng_R.type.values_eq(rng, rng_)
# rng2 should be in an updated state
assert not rng_R.type.values_eq(rng, rng2)
# The updated state should be the same for both functions
assert rng_R.type.values_eq(rng2, rng0)
rng3, val3 = f2()
# rng2 should not have changed
assert rng_R.type.values_eq(rng2, rng0)
# rng3 should be an updated again version of rng2
assert not rng_R.type.values_eq(rng3, rng2)
assert not rng_R.type.values_eq(rng3, rng)
def test_inplace_optimization(self):
# Test that FAST_RUN includes the random_make_inplace optimization
# inplace = False
rf2 = RandomFunction(np.random.RandomState.uniform, tensor.dvector)
rng_R = random_state_type()
# If calling RandomFunction directly, all args have to be specified,
# because shape will have to be moved to the end
post_r2, out2 = rf2(rng_R, (4, ), 0.0, 1.0)
f = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_r2,
mutable=True,
)
],
out2,
mode="FAST_RUN",
) # DEBUG_MODE can't pass the id-based
# test below
# test that the RandomState object stays the same from function call to
# function call, but that the values returned change from call to call.
id0 = id(f[rng_R])
val0 = f()
assert id0 == id(f[rng_R])
val1 = f()
assert id0 == id(f[rng_R])
assert not np.allclose(val0, val1)
def test_random_function_noshape_args(self):
# Test if random_function helper works with args but without shape
rng_R = random_state_type()
# No shape, default args -> OK
post_out, out = uniform(rng_R, size=None, ndim=2)
f = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_out,
mutable=True,
)
],
[out],
accept_inplace=True,
)
(o, ) = f()
# No shape, args that have to be broadcasted -> OK
low = tensor.TensorType(dtype="float64",
broadcastable=(False, True, True))()
high = tensor.TensorType(dtype="float64",
broadcastable=(True, True, True, False))()
post_out2, out2 = uniform(rng_R, size=None, ndim=2, low=low, high=high)
assert out2.ndim == 4
assert out2.broadcastable == (True, False, True, False)
g = compile.function(
[
low,
high,
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_out2,
mutable=True,
),
],
[out2],
accept_inplace=True,
)
low_v = [[[3]], [[4]], [[-5]]]
high_v = [[[[5, 8]]]]
(o2, ) = g(low_v, high_v)
assert o2.shape == (1, 3, 1, 2)
def test_permutation(self):
# Test that raw_random.permutation generates the same results as numpy.
rng_R = random_state_type()
post_r, out = permutation(rng_R, size=(9, ), n=6)
f = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_r,
mutable=True,
)
],
[out],
accept_inplace=True,
)
numpy_rng = np.random.RandomState(utt.fetch_seed())
# Check over two calls to see if the random state is correctly updated.
# numpy_rng.permutation outputs one vector at a time,
# so we call it iteratively to generate all the samples.
val0 = f()
val1 = f()
numpy_val0 = np.asarray([numpy_rng.permutation(6) for i in range(9)])
numpy_val1 = np.asarray([numpy_rng.permutation(6) for i in range(9)])
assert np.all(val0 == numpy_val0)
assert np.all(val1 == numpy_val1)
# Test that we can generate a list: have size=None or ().
for ndim in [1, None]:
post_r, out = permutation(rng_R, n=10, size=None, ndim=ndim)
inp = compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_r,
mutable=True,
)
f = aesara.function([inp], out)
o = f()
assert o.shape == (10, )
assert (np.sort(o) == np.arange(10)).all()
# Wrong number of dimensions asked
with pytest.raises(TypeError):
permutation(rng_R, size=None, ndim=2)
def test_args(self):
# Test that arguments to RandomFunction are honored
rf2 = RandomFunction(np.random.RandomState.uniform, tensor.dvector)
rf4 = RandomFunction(np.random.RandomState.uniform,
tensor.dvector,
inplace=True)
rng_R = random_state_type()
# use make_node to override some of the self.args
post_r2, out2 = rf2(rng_R, (4, ), -2, 2) # NOT INPLACE
post_r4, out4 = rf4(rng_R, (4, ), -4, 4) # INPLACE
post_r2_4, out2_4 = rf2(rng_R, (4, ), -4.0, 2) # NOT INPLACE
post_r2_4_4, out2_4_4 = rf2(rng_R, (4, ), -4.0, 4.0) # NOT INPLACE
# configure out4 to be computed inplace
# The update expression means that the random state rng_R will
# be maintained by post_r4
f = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_r4,
mutable=True,
)
],
[out2, out4, out2_4, out2_4_4],
accept_inplace=True,
)
f2, f4, f2_4, f2_4_4 = f()
f2b, f4b, f2_4b, f2_4_4b = f()
# print f2
# print f4
# print f2_4
# print f2_4_4
# print f2b
# print f4b
# print f2_4b
# print f2_4_4b
# setting bounds is same as multiplying by 2
assert np.allclose(f2 * 2, f4), (f2, f4)
# retrieving from non-inplace generator
# is same as inplace one for first call
assert np.allclose(f2_4_4, f4), (f2_4_4, f4)
# f4 changes from call to call, that the update has worked
assert not np.allclose(f4, f4b), (f4, f4b)
def test_permutation_helper(self):
# Test that raw_random.permutation_helper generates the same
# results as numpy,
# and that the 'ndim_added' keyword behaves correctly.
# permutation_helper needs "ndim_added=1", because its output
# is one dimension more than its "shape" argument (and there's
# no way to determine that automatically).
# Check the working case, over two calls to see if the random
# state is correctly updated.
rf = RandomFunction(permutation_helper,
tensor.imatrix,
8,
ndim_added=1)
rng_R = random_state_type()
post_r, out = rf(rng_R, (7, ), 8)
f = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_r,
mutable=True,
)
],
[out],
accept_inplace=True,
)
numpy_rng = np.random.RandomState(utt.fetch_seed())
val0 = f()
val1 = f()
# numpy_rng.permutation outputs one vector at a time,
# so we call it iteratively to generate all the samples.
numpy_val0 = np.asarray([numpy_rng.permutation(8) for i in range(7)])
numpy_val1 = np.asarray([numpy_rng.permutation(8) for i in range(7)])
assert np.all(val0 == numpy_val0)
assert np.all(val1 == numpy_val1)
# This call lacks "ndim_added=1", so ndim_added defaults to 0.
# A ValueError should be raised.
rf0 = RandomFunction(permutation_helper, tensor.imatrix, 8)
post_r0, out0 = rf0(rng_R, (7, ), 8)
f0 = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_r0,
mutable=True,
)
],
[out0],
accept_inplace=True,
)
with pytest.raises(ValueError):
f0()
# Here, ndim_added is 2 instead of 1. A ValueError should be raised.
rf2 = RandomFunction(permutation_helper,
tensor.imatrix,
8,
ndim_added=2)
post_r2, out2 = rf2(rng_R, (7, ), 8)
f2 = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=post_r2,
mutable=True,
)
],
[out2],
accept_inplace=True,
)
with pytest.raises(ValueError):
f2()
def test_random_function_ndim_added(self):
# Test that random_function helper function accepts ndim_added as
# keyword argument
# If using numpy's uniform distribution, ndim_added should be 0,
# because the shape provided as argument is the output shape.
# Specifying a different ndim_added will change the Op's output ndim,
# so np.uniform will produce a result of incorrect shape,
# and a ValueError should be raised.
def ndim_added_deco(ndim_added):
def randomfunction(random_state,
size=(),
low=0.0,
high=0.0,
ndim=None):
ndim, size, bcast = raw_random._infer_ndim_bcast(ndim, size)
if ndim_added < 0:
bcast = bcast[:ndim_added]
else:
bcast = bcast + ((False, ) * ndim_added)
assert len(bcast) == ndim + ndim_added
op = RandomFunction(
"uniform",
tensor.TensorType(dtype="float64", broadcastable=bcast),
ndim_added=ndim_added,
)
return op(random_state, size, low, high)
return randomfunction
uni_1 = ndim_added_deco(1)
uni_0 = ndim_added_deco(0)
uni_m1 = ndim_added_deco(-1)
rng_R = random_state_type()
p_uni11, uni11 = uni_1(rng_R, size=(4, ))
p_uni12, uni12 = uni_1(rng_R, size=(3, 4))
p_uni01, uni01 = uni_0(rng_R, size=(4, ))
p_uni02, uni02 = uni_0(rng_R, size=(3, 4))
p_unim11, unim11 = uni_m1(rng_R, size=(4, ))
p_unim12, unim12 = uni_m1(rng_R, size=(3, 4))
assert uni11.ndim == 2
assert uni12.ndim == 3
assert uni01.ndim == 1
assert uni02.ndim == 2
assert unim11.ndim == 0
assert unim12.ndim == 1
f11 = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=p_uni11,
mutable=True,
)
],
[uni11],
accept_inplace=True,
)
f12 = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=p_uni12,
mutable=True,
)
],
[uni12],
accept_inplace=True,
)
fm11 = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=p_unim11,
mutable=True,
)
],
[unim11],
accept_inplace=True,
)
fm12 = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=p_unim12,
mutable=True,
)
],
[unim12],
accept_inplace=True,
)
f0 = compile.function(
[
compile.In(
rng_R,
value=np.random.RandomState(utt.fetch_seed()),
update=p_uni02,
mutable=True,
)
],
[uni01, uni02],
accept_inplace=True,
)
with pytest.raises(ValueError):
f11()
with pytest.raises(ValueError):
f12()
with pytest.raises(ValueError):
fm11()
with pytest.raises(ValueError):
fm12()
u01, u02 = f0()
assert np.allclose(u01, u02[0])