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_binomial_vector(self):
rng_R = random_state_type()
n = tensor.lvector()
prob = tensor.vector()
post_r, out = binomial(rng_R, n=n, p=prob)
assert out.ndim == 1
f = compile.function([rng_R, n, prob], [post_r, out],
accept_inplace=True)
n_val = [1, 2, 3]
prob_val = np.asarray([0.1, 0.2, 0.3], dtype=config.floatX)
rng = np.random.RandomState(utt.fetch_seed())
numpy_rng = np.random.RandomState(utt.fetch_seed())
# Arguments of size (3,)
rng0, val0 = f(rng, n_val, prob_val)
numpy_val0 = numpy_rng.binomial(n=n_val, p=prob_val)
assert np.all(val0 == numpy_val0)
# arguments of size (2,)
rng1, val1 = f(rng0, n_val[:-1], prob_val[:-1])
numpy_val1 = numpy_rng.binomial(n=n_val[:-1], p=prob_val[:-1])
assert np.all(val1 == numpy_val1)
# Specifying the size explicitly
g = compile.function(
[rng_R, n, prob],
binomial(rng_R, n=n, p=prob, size=(3, )),
accept_inplace=True,
)
rng2, val2 = g(rng1, n_val, prob_val)
numpy_val2 = numpy_rng.binomial(n=n_val, p=prob_val, size=(3, ))
assert np.all(val2 == numpy_val2)
with pytest.raises(ValueError):
g(rng2, n_val[:-1], prob_val[:-1])
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_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_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_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_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_random_function_noshape_noargs(self):
# Test if random_function helper works without args or shape
rng_R = random_state_type()
# No shape, no args -> TypeError
with pytest.raises(TypeError):
poisson(rng_R, size=None, ndim=2)
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_vector_arguments(self):
rng_R = random_state_type()
low = tensor.vector()
post_r, out = uniform(rng_R, low=low, high=1)
assert out.ndim == 1
f = compile.function([rng_R, low], [post_r, out], accept_inplace=True)
def as_floatX(thing):
return np.asarray(thing, dtype=aesara.config.floatX)
rng_state0 = np.random.RandomState(utt.fetch_seed())
numpy_rng = np.random.RandomState(utt.fetch_seed())
post0, val0 = f(rng_state0, [-5, 0.5, 0, 1])
post1, val1 = f(post0, as_floatX([0.9]))
numpy_val0 = as_floatX(numpy_rng.uniform(low=[-5, 0.5, 0, 1], high=1))
numpy_val1 = as_floatX(numpy_rng.uniform(low=as_floatX([0.9]), high=1))
assert np.all(val0 == numpy_val0)
assert np.all(val1 == numpy_val1)
high = tensor.vector()
post_rb, outb = uniform(rng_R, low=low, high=high)
assert outb.ndim == 1
fb = compile.function([rng_R, low, high], [post_rb, outb],
accept_inplace=True)
post0b, val0b = fb(post1, [-4.0, -2], [-1, 0])
post1b, val1b = fb(post0b, [-4.0], [-1])
numpy_val0b = as_floatX(numpy_rng.uniform(low=[-4.0, -2], high=[-1,
0]))
numpy_val1b = as_floatX(numpy_rng.uniform(low=[-4.0], high=[-1]))
assert np.all(val0b == numpy_val0b)
assert np.all(val1b == numpy_val1b)
with pytest.raises(ValueError):
fb(post1b, [-4.0, -2], [-1, 0, 1])
# TODO: do we want that?
# with pytest.raises(ValueError):
# fb(post1b, [-4., -2], [-1])
size = tensor.lvector()
post_rc, outc = uniform(rng_R, low=low, high=high, size=size, ndim=1)
fc = compile.function([rng_R, low, high, size], [post_rc, outc],
accept_inplace=True)
post0c, val0c = fc(post1b, [-4.0, -2], [-1, 0], [2])
post1c, val1c = fc(post0c, [-4.0], [-1], [1])
numpy_val0c = as_floatX(numpy_rng.uniform(low=[-4.0, -2], high=[-1,
0]))
numpy_val1c = as_floatX(numpy_rng.uniform(low=[-4.0], high=[-1]))
assert np.all(val0c == numpy_val0c)
assert np.all(val1c == numpy_val1c)
with pytest.raises(ValueError):
fc(post1c, [-4.0, -2], [-1, 0], [1, 2])
with pytest.raises(ValueError):
fc(post1c, [-4.0, -2], [-1, 0], [2, 1])
with pytest.raises(ValueError):
fc(post1c, [-4.0, -2], [-1, 0], [1])
with pytest.raises(ValueError):
fc(post1c, [-4.0, -2], [-1], [1])
def test_dtype_normal_uniform_687(self):
# Regression test for #687.
rng_R = random_state_type()
assert (uniform(rng_R,
low=tensor.constant(0, dtype="float64"),
dtype="float32")[1].dtype == "float32")
assert (normal(rng_R,
avg=tensor.constant(0, dtype="float64"),
dtype="float32")[1].dtype == "float32")
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_symbolic_shape(self):
rng_R = random_state_type()
shape = tensor.lvector()
post_r, out = uniform(rng_R, shape, ndim=2)
f = compile.function([rng_R, shape], out)
rng_state0 = np.random.RandomState(utt.fetch_seed())
assert f(rng_state0, [2, 3]).shape == (2, 3)
assert f(rng_state0, [4, 8]).shape == (4, 8)
with pytest.raises(ValueError):
f(rng_state0, [4])
with pytest.raises(ValueError):
f(rng_state0, [4, 3, 4, 5])
def test_pkl(self):
# Test pickling of RandomFunction.
# binomial was created by calling RandomFunction on a string,
# random_integers by calling it on a function.
rng_r = random_state_type()
mode = None
if aesara.config.mode in ["DEBUG_MODE", "DebugMode"]:
mode = "FAST_COMPILE"
post_bin_r, bin_sample = binomial(rng_r, (3, 5), 1, 0.3)
f = aesara.function([rng_r], [post_bin_r, bin_sample], mode=mode)
pickle.dumps(f)
post_int_r, int_sample = random_integers(rng_r, (3, 5), -1, 8)
g = aesara.function([rng_r], [post_int_r, int_sample], mode=mode)
pkl_g = pickle.dumps(g)
pickle.loads(pkl_g)
def test_mixed_shape(self):
# Test when the provided shape is a tuple of ints and scalar vars
rng_R = random_state_type()
shape0 = tensor.lscalar()
shape = (shape0, 3)
post_r, u = uniform(rng_R, size=shape, ndim=2)
f = compile.function([rng_R, shape0], u)
rng_state0 = np.random.RandomState(utt.fetch_seed())
assert f(rng_state0, 2).shape == (2, 3)
assert f(rng_state0, 8).shape == (8, 3)
post_r, v = uniform(rng_R, size=shape)
g = compile.function([rng_R, shape0], v)
assert g(rng_state0, 2).shape == (2, 3)
assert g(rng_state0, 8).shape == (8, 3)
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_multinomial_vector(self):
rng_R = random_state_type()
n = tensor.lvector()
pvals = tensor.matrix()
post_r, out = multinomial(rng_R, n=n, pvals=pvals)
assert out.ndim == 2
f = compile.function([rng_R, n, pvals], [post_r, out],
accept_inplace=True)
n_val = [1, 2, 3]
pvals_val = [[0.1, 0.9], [0.2, 0.8], [0.3, 0.7]]
pvals_val = np.asarray(pvals_val, dtype=config.floatX)
rng = np.random.RandomState(utt.fetch_seed())
numpy_rng = np.random.RandomState(utt.fetch_seed())
# Arguments of size (3,)
rng0, val0 = f(rng, n_val, pvals_val)
numpy_val0 = np.asarray([
numpy_rng.multinomial(n=nv, pvals=pv)
for nv, pv in zip(n_val, pvals_val)
])
assert np.all(val0 == numpy_val0)
# arguments of size (2,)
rng1, val1 = f(rng0, n_val[:-1], pvals_val[:-1])
numpy_val1 = np.asarray([
numpy_rng.multinomial(n=nv, pvals=pv)
for nv, pv in zip(n_val[:-1], pvals_val[:-1])
])
assert np.all(val1 == numpy_val1)
# Specifying the size explicitly
g = compile.function(
[rng_R, n, pvals],
multinomial(rng_R, n=n, pvals=pvals, size=(3, )),
accept_inplace=True,
)
rng2, val2 = g(rng1, n_val, pvals_val)
numpy_val2 = np.asarray([
numpy_rng.multinomial(n=nv, pvals=pv)
for nv, pv in zip(n_val, pvals_val)
])
assert np.all(val2 == numpy_val2)
with pytest.raises(ValueError):
g(rng2, n_val[:-1], pvals_val[:-1])
def test_normal_vector(self):
rng_R = random_state_type()
avg = tensor.vector()
std = tensor.vector()
post_r, out = normal(rng_R, avg=avg, std=std)
assert out.ndim == 1
f = compile.function([rng_R, avg, std], [post_r, out],
accept_inplace=True)
def as_floatX(thing):
return np.asarray(thing, dtype=aesara.config.floatX)
avg_val = [1, 2, 3]
std_val = as_floatX([0.1, 0.2, 0.3])
rng = np.random.RandomState(utt.fetch_seed())
numpy_rng = np.random.RandomState(utt.fetch_seed())
# Arguments of size (3,)
rng0, val0 = f(rng, avg_val, std_val)
numpy_val0 = as_floatX(
numpy_rng.normal(loc=as_floatX(avg_val), scale=as_floatX(std_val)))
assert np.all(val0 == numpy_val0)
# arguments of size (2,)
rng1, val1 = f(rng0, avg_val[:-1], std_val[:-1])
numpy_val1 = np.asarray(
numpy_rng.normal(loc=avg_val[:-1], scale=std_val[:-1]),
dtype=aesara.config.floatX,
)
assert np.all(val1 == numpy_val1)
# Specifying the size explicitly
g = compile.function(
[rng_R, avg, std],
normal(rng_R, avg=avg, std=std, size=(3, )),
accept_inplace=True,
)
rng2, val2 = g(rng1, avg_val, std_val)
numpy_val2 = np.asarray(
numpy_rng.normal(loc=avg_val, scale=std_val, size=(3, )),
dtype=aesara.config.floatX,
)
assert np.all(val2 == numpy_val2)
with pytest.raises(ValueError):
g(rng2, avg_val[:-1], std_val[:-1])
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_random_integers_vector(self):
rng_R = random_state_type()
low = tensor.lvector()
high = tensor.lvector()
post_r, out = random_integers(rng_R, low=low, high=high)
assert out.ndim == 1
f = compile.function([rng_R, low, high], [post_r, out],
accept_inplace=True)
low_val = [100, 200, 300]
high_val = [110, 220, 330]
rng = np.random.RandomState(utt.fetch_seed())
numpy_rng = np.random.RandomState(utt.fetch_seed())
# Arguments of size (3,)
rng0, val0 = f(rng, low_val, high_val)
numpy_val0 = np.asarray([
numpy_rng.randint(low=lv, high=hv + 1)
for lv, hv in zip(low_val, high_val)
])
assert np.all(val0 == numpy_val0)
# arguments of size (2,)
rng1, val1 = f(rng0, low_val[:-1], high_val[:-1])
numpy_val1 = np.asarray([
numpy_rng.randint(low=lv, high=hv + 1)
for lv, hv in zip(low_val[:-1], high_val[:-1])
])
assert np.all(val1 == numpy_val1)
# Specifying the size explicitly
g = compile.function(
[rng_R, low, high],
random_integers(rng_R, low=low, high=high, size=(3, )),
accept_inplace=True,
)
rng2, val2 = g(rng1, low_val, high_val)
numpy_val2 = np.asarray([
numpy_rng.randint(low=lv, high=hv + 1)
for lv, hv in zip(low_val, high_val)
])
assert np.all(val2 == numpy_val2)
with pytest.raises(ValueError):
g(rng2, low_val[:-1], high_val[:-1])
def test_dtype(self):
rng_R = random_state_type()
low = tensor.lscalar()
high = tensor.lscalar()
post_r, out = random_integers(rng_R,
low=low,
high=high,
size=(20, ),
dtype="int8")
assert out.dtype == "int8"
f = compile.function([rng_R, low, high], [post_r, out])
rng = np.random.RandomState(utt.fetch_seed())
rng0, val0 = f(rng, 0, 9)
assert val0.dtype == "int8"
rng1, val1 = f(rng0, 255, 257)
assert val1.dtype == "int8"
assert np.all(abs(val1) <= 1)
def test_multinomial_tensor3_b(self):
# Test the examples given in the multinomial documentation regarding
# tensor3 objects
rng_R = random_state_type()
n = 9
pvals = tensor.dtensor3()
post_r, out = multinomial(rng_R, n=n, pvals=pvals, size=(10, 1, -1))
assert out.ndim == 4
assert out.broadcastable == (False, True, False, False)
f = compile.function([rng_R, pvals], [post_r, out],
accept_inplace=True)
rng = np.random.RandomState(utt.fetch_seed())
pvals_val = np.asarray([[[0.1, 0.9], [0.2, 0.8], [0.3, 0.7]]])
assert pvals_val.shape == (1, 3, 2)
out_rng, draw = f(rng, pvals_val)
assert draw.shape == (10, 1, 3, 2)
assert np.allclose(draw.sum(axis=3), 9)
def test_basic_usage(self):
rf = RandomFunction(np.random.RandomState.uniform, tensor.dvector)
assert not rf.inplace
assert getattr(rf, "destroy_map", {}) == {}
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_r, out = rf(rng_R, (4, ), 0.0, 1.0)
assert out.type == tensor.dvector
f = compile.function([rng_R], out)
rng_state0 = np.random.RandomState(utt.fetch_seed())
f_0 = f(rng_state0)
f_1 = f(rng_state0)
assert np.all(f_0 == f_1)
def test_uniform_vector(self):
rng_R = random_state_type()
low = tensor.vector()
high = tensor.vector()
post_r, out = uniform(rng_R, low=low, high=high)
assert out.ndim == 1
f = compile.function([rng_R, low, high], [post_r, out],
accept_inplace=True)
def as_floatX(thing):
return np.asarray(thing, dtype=aesara.config.floatX)
low_val = as_floatX([0.1, 0.2, 0.3])
high_val = as_floatX([1.1, 2.2, 3.3])
rng = np.random.RandomState(utt.fetch_seed())
numpy_rng = np.random.RandomState(utt.fetch_seed())
# Arguments of size (3,)
rng0, val0 = f(rng, low_val, high_val)
numpy_val0 = as_floatX(numpy_rng.uniform(low=low_val, high=high_val))
assert np.all(val0 == numpy_val0)
# arguments of size (2,)
rng1, val1 = f(rng0, low_val[:-1], high_val[:-1])
numpy_val1 = as_floatX(
numpy_rng.uniform(low=low_val[:-1], high=high_val[:-1]))
assert np.all(val1 == numpy_val1)
# Specifying the size explicitly
g = compile.function(
[rng_R, low, high],
uniform(rng_R, low=low, high=high, size=(3, )),
accept_inplace=True,
)
rng2, val2 = g(rng1, low_val, high_val)
numpy_val2 = as_floatX(
numpy_rng.uniform(low=low_val, high=high_val, size=(3, )))
assert np.all(val2 == numpy_val2)
with pytest.raises(ValueError):
g(rng2, low_val[:-1], high_val[:-1])
def test_broadcast_arguments(self):
rng_R = random_state_type()
low = tensor.dvector()
high = tensor.dcol()
post_r, out = uniform(rng_R, low=low, high=high)
assert out.ndim == 2
f = compile.function([rng_R, low, high], [post_r, out],
accept_inplace=True)
rng_state0 = np.random.RandomState(utt.fetch_seed())
numpy_rng = np.random.RandomState(utt.fetch_seed())
post0, val0 = f(rng_state0, [-5, 0.5, 0, 1], [[1.0]])
post1, val1 = f(post0, [0.9], [[1.0], [1.1], [1.5]])
post2, val2 = f(post1, [-5, 0.5, 0, 1], [[1.0], [1.1], [1.5]])
numpy_val0 = numpy_rng.uniform(low=[-5, 0.5, 0, 1], high=[1.0])
numpy_val1 = numpy_rng.uniform(low=[0.9], high=[[1.0], [1.1], [1.5]])
numpy_val2 = numpy_rng.uniform(low=[-5, 0.5, 0, 1],
high=[[1.0], [1.1], [1.5]])
assert np.all(val0 == numpy_val0), (val0, numpy_val0)
assert np.all(val1 == numpy_val1)
assert np.all(val2 == numpy_val2)
def test_default_shape(self):
rng_R = random_state_type()
post_r, out = uniform(rng_R)
f = compile.function([rng_R], [post_r, out], accept_inplace=True)
rng_state0 = np.random.RandomState(utt.fetch_seed())
numpy_rng = np.random.RandomState(utt.fetch_seed())
post0, val0 = f(rng_state0)
post1, val1 = f(post0)
numpy_val0 = np.asarray(numpy_rng.uniform(),
dtype=aesara.config.floatX)
numpy_val1 = np.asarray(numpy_rng.uniform(),
dtype=aesara.config.floatX)
assert np.all(val0 == numpy_val0)
assert np.all(val1 == numpy_val1)
post_r, out = multinomial(rng_R)
g = compile.function([rng_R], [post_r, out], accept_inplace=True)
post2, val2 = g(post1)
numpy_val2 = np.asarray(numpy_rng.multinomial(n=1, pvals=[0.5, 0.5]),
dtype=aesara.config.floatX)
assert np.all(val2 == numpy_val2)
def test_infer_shape(self):
rng_R = random_state_type()
rng_R_val = np.random.RandomState(utt.fetch_seed())
# no shape specified, default args
post_r, out = uniform(rng_R)
self._compile_and_check([rng_R], [out], [rng_R_val], RandomFunction)
post_r, out = uniform(rng_R, size=None, ndim=2)
self._compile_and_check([rng_R], [out], [rng_R_val], RandomFunction)
"""
#infer_shape don't work for multinomial.
#The parameter ndim_added is set to 1 and in this case, the infer_shape
#inplementation don't know how to infer the shape
post_r, out = multinomial(rng_R)
self._compile_and_check([rng_R], [out], [rng_R_val],
RandomFunction)
"""
# no shape specified, args have to be broadcasted
low = tensor.TensorType(dtype="float64",
broadcastable=(False, True, True))()
high = tensor.TensorType(dtype="float64",
broadcastable=(True, True, True, False))()
post_r, out = uniform(rng_R, size=None, ndim=2, low=low, high=high)
low_val = [[[3]], [[4]], [[-5]]]
high_val = [[[[5, 8]]]]
self._compile_and_check([rng_R, low, high], [out],
[rng_R_val, low_val, high_val], RandomFunction)
# multinomial, specified shape
"""
#infer_shape don't work for multinomial
n = iscalar()
pvals = dvector()
size_val = (7, 3)
n_val = 6
pvals_val = [0.2] * 5
post_r, out = multinomial(rng_R, size=size_val, n=n, pvals=pvals,
ndim=2)
self._compile_and_check([rng_R, n, pvals], [out],
[rng_R_val, n_val, pvals_val],
RandomFunction)
"""
# uniform vector low and high
low = dvector()
high = dvector()
post_r, out = uniform(rng_R, low=low, high=1)
low_val = [-5, 0.5, 0, 1]
self._compile_and_check([rng_R, low], [out], [rng_R_val, low_val],
RandomFunction)
low_val = [0.9]
self._compile_and_check([rng_R, low], [out], [rng_R_val, low_val],
RandomFunction)
post_r, out = uniform(rng_R, low=low, high=high)
low_val = [-4.0, -2]
high_val = [-1, 0]
self._compile_and_check([rng_R, low, high], [out],
[rng_R_val, low_val, high_val], RandomFunction)
low_val = [-4.0]
high_val = [-1]
self._compile_and_check([rng_R, low, high], [out],
[rng_R_val, low_val, high_val], RandomFunction)
# uniform broadcasting low and high
low = dvector()
high = dcol()
post_r, out = uniform(rng_R, low=low, high=high)
low_val = [-5, 0.5, 0, 1]
high_val = [[1.0]]
self._compile_and_check([rng_R, low, high], [out],
[rng_R_val, low_val, high_val], RandomFunction)
low_val = [0.9]
high_val = [[1.0], [1.1], [1.5]]
self._compile_and_check([rng_R, low, high], [out],
[rng_R_val, low_val, high_val], RandomFunction)
low_val = [-5, 0.5, 0, 1]
high_val = [[1.0], [1.1], [1.5]]
self._compile_and_check([rng_R, low, high], [out],
[rng_R_val, low_val, high_val], RandomFunction)
# uniform with vector slice
low = dvector()
high = dvector()
post_r, out = uniform(rng_R, low=low, high=high)
low_val = [0.1, 0.2, 0.3]
high_val = [1.1, 2.2, 3.3]
size_val = (3, )
self._compile_and_check(
[rng_R, low, high],
[out],
[rng_R_val, low_val[:-1], high_val[:-1]],
RandomFunction,
)
# uniform with explicit size and size implicit in parameters
# NOTE 1: Would it be desirable that size could also be supplied
# as a Aesara variable?
post_r, out = uniform(rng_R, size=size_val, low=low, high=high)
self._compile_and_check([rng_R, low, high], [out],
[rng_R_val, low_val, high_val], RandomFunction)
# binomial with vector slice
n = ivector()
prob = dvector()
post_r, out = binomial(rng_R, n=n, p=prob)
n_val = [1, 2, 3]
prob_val = [0.1, 0.2, 0.3]
size_val = (3, )
self._compile_and_check(
[rng_R, n, prob],
[out],
[rng_R_val, n_val[:-1], prob_val[:-1]],
RandomFunction,
)
# binomial with explicit size and size implicit in parameters
# cf. NOTE 1
post_r, out = binomial(rng_R, n=n, p=prob, size=size_val)
self._compile_and_check([rng_R, n, prob], [out],
[rng_R_val, n_val, prob_val], RandomFunction)
# normal with vector slice
avg = dvector()
std = dvector()
post_r, out = normal(rng_R, avg=avg, std=std)
avg_val = [1, 2, 3]
std_val = [0.1, 0.2, 0.3]
size_val = (3, )
self._compile_and_check(
[rng_R, avg, std],
[out],
[rng_R_val, avg_val[:-1], std_val[:-1]],
RandomFunction,
)
# normal with explicit size and size implicit in parameters
# cf. NOTE 1
post_r, out = normal(rng_R, avg=avg, std=std, size=size_val)
self._compile_and_check([rng_R, avg, std], [out],
[rng_R_val, avg_val, std_val], RandomFunction)
# multinomial with tensor-3 probabilities
"""
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])
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()
