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提交 39c11472 authored 作者: kc611's avatar kc611 提交者： Brandon T. Willard
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Refactor remaining tests to use NumPy Generator

上级 1ff4b9d3
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正在显示 包含 334 行增加 和 276 行删除
tests/compile/function/test_pfunc.py
浏览文件 @ 39c11472
......@@ -64,7 +64,7 @@ class TestPfunc:
def test_shared(self):
# CHECK: two functions (f1 and f2) can share w
w = shared(np.random.rand(2, 2), "w")
w = shared(np.random.random((2, 2)), "w")
wval = w.get_value(borrow=False)
x = dmatrix()
......@@ -72,7 +72,7 @@ class TestPfunc:
out2 = w * x
f1 = pfunc([x], [out1])
f2 = pfunc([x], [out2])
xval = np.random.rand(2, 2)
xval = np.random.random((2, 2))
assert np.all(f1(xval) == xval + wval)
assert np.all(f2(xval) == xval * wval)
......@@ -89,7 +89,7 @@ class TestPfunc:
def test_no_shared_as_input(self):
# Test that shared variables cannot be used as function inputs.
w_init = np.random.rand(2, 2)
w_init = np.random.random((2, 2))
w = shared(w_init.copy(), "w")
with pytest.raises(
TypeError, match=r"^Cannot use a shared variable \(w\) as explicit input"
......@@ -100,8 +100,8 @@ class TestPfunc:
# Ensure it is possible to (implicitly) use a shared variable in a
# function, as a 'state' that can be updated at will.
rng = np.random.RandomState(1827)
w_init = rng.rand(5)
rng = np.random.default_rng(1827)
w_init = rng.random((5))
w = shared(w_init.copy(), "w")
reg = aet_sum(w * w)
f = pfunc([], reg)
......@@ -127,8 +127,8 @@ class TestPfunc:
out = a + b
f = pfunc([In(a, strict=False)], [out])
# works, rand generates float64 by default
f(np.random.rand(8))
# works, random( generates float64 by default
f(np.random.random((8)))
# works, casting is allowed
f(np.array([1, 2, 3, 4], dtype="int32"))
......@@ -145,14 +145,14 @@ class TestPfunc:
# using mutable=True will let fip change the value in aval
fip = pfunc([In(a, mutable=True)], [a_out], mode="FAST_RUN")
aval = np.random.rand(10)
aval = np.random.random((10))
aval2 = aval.copy()
assert np.all(fip(aval) == (aval2 * 2))
assert not np.all(aval == aval2)
# using mutable=False should leave the input untouched
f = pfunc([In(a, mutable=False)], [a_out], mode="FAST_RUN")
aval = np.random.rand(10)
aval = np.random.random((10))
aval2 = aval.copy()
assert np.all(f(aval) == (aval2 * 2))
assert np.all(aval == aval2)
......@@ -375,7 +375,7 @@ class TestPfunc:
def test_update_err_broadcast(self):
# Test that broadcastable dimensions raise error
data = np.random.rand(10, 10).astype("float32")
data = np.random.random((10, 10)).astype("float32")
output_var = shared(name="output", value=data)
# the update_var has type matrix, and the update expression
......
tests/compile/test_debugmode.py
浏览文件 @ 39c11472
......@@ -736,7 +736,7 @@ class VecAsRowAndCol(Op):
class TestPreallocatedOutput:
def setup_method(self):
self.rng = np.random.RandomState(seed=utt.fetch_seed())
self.rng = np.random.default_rng(seed=utt.fetch_seed())
def test_f_contiguous(self):
a = fmatrix("a")
......@@ -745,8 +745,8 @@ class TestPreallocatedOutput:
# In this test, we do not want z to be an output of the graph.
out = dot(z, np.eye(7))
a_val = self.rng.randn(7, 7).astype("float32")
b_val = self.rng.randn(7, 7).astype("float32")
a_val = self.rng.standard_normal((7, 7)).astype("float32")
b_val = self.rng.standard_normal((7, 7)).astype("float32")
# Should work
mode = DebugMode(check_preallocated_output=["c_contiguous"])
......@@ -776,8 +776,8 @@ class TestPreallocatedOutput:
b = fmatrix("b")
out = BrokenCImplementationAdd()(a, b)
a_val = self.rng.randn(7, 7).astype("float32")
b_val = self.rng.randn(7, 7).astype("float32")
a_val = self.rng.standard_normal((7, 7)).astype("float32")
b_val = self.rng.standard_normal((7, 7)).astype("float32")
# Should work
mode = DebugMode(check_preallocated_output=["c_contiguous"])
......@@ -805,5 +805,5 @@ class TestPreallocatedOutput:
c, r = VecAsRowAndCol()(v)
f = function([v], [c, r])
v_val = self.rng.randn(5).astype("float32")
v_val = self.rng.standard_normal((5)).astype("float32")
f(v_val)
tests/compile/test_misc.py
浏览文件 @ 39c11472
......@@ -54,8 +54,8 @@ class NNet:
def test_nnet():
rng = np.random.RandomState(1827)
data = rng.rand(10, 4)
rng = np.random.default_rng(279)
data = rng.random((10, 4))
nnet = NNet(n_input=3, n_hidden=10)
for epoch in range(3):
mean_cost = 0
......@@ -66,7 +66,8 @@ def test_nnet():
mean_cost += cost
mean_cost /= float(len(data))
# print 'Mean cost at epoch %s: %s' % (epoch, mean_cost)
assert abs(mean_cost - 0.20588975452) < 1e-6
# Seed based test
assert abs(mean_cost - 0.2301901) < 1e-6
# Just call functions to make sure they do not crash.
nnet.compute_output(input)
nnet.output_from_hidden(np.ones(10))
tests/d3viz/models.py
浏览文件 @ 39c11472
......@@ -11,7 +11,7 @@ class Mlp:
if rng is None:
rng = 0
if isinstance(rng, int):
rng = np.random.RandomState(rng)
rng = np.random.default_rng(rng)
self.rng = rng
self.nfeatures = nfeatures
self.noutputs = noutputs
......
tests/d3viz/test_d3viz.py
浏览文件 @ 39c11472
......@@ -19,7 +19,7 @@ if not pydot_imported:
class TestD3Viz:
def setup_method(self):
self.rng = np.random.RandomState(0)
self.rng = np.random.default_rng(0)
self.data_dir = pt.join("data", "test_d3viz")
def check(self, f, reference=None, verbose=False):
......
tests/d3viz/test_formatting.py
浏览文件 @ 39c11472
......@@ -13,7 +13,7 @@ from tests.d3viz import models
class TestPyDotFormatter:
def setup_method(self):
self.rng = np.random.RandomState(0)
self.rng = np.random.default_rng(0)
def node_counts(self, graph):
node_types = [node.get_attributes()["node_type"] for node in graph.get_nodes()]
......
tests/link/test_jax.py
浏览文件 @ 39c11472
......@@ -218,6 +218,8 @@ def test_jax_compile_ops():
def test_jax_basic():
rng = np.random.default_rng(28494)
x = matrix("x")
y = matrix("y")
b = vector("b")
......@@ -259,7 +261,11 @@ def test_jax_basic():
out_fg = FunctionGraph([x], [out])
compare_jax_and_py(
out_fg,
[(np.eye(10) + np.random.randn(10, 10) * 0.01).astype(config.floatX)],
[
(np.eye(10) + rng.standard_normal(size=(10, 10)) * 0.01).astype(
config.floatX
)
],
)
# not sure why this isn't working yet with lower=False
......@@ -267,7 +273,11 @@ def test_jax_basic():
out_fg = FunctionGraph([x], [out])
compare_jax_and_py(
out_fg,
[(np.eye(10) + np.random.randn(10, 10) * 0.01).astype(config.floatX)],
[
(np.eye(10) + rng.standard_normal(size=(10, 10)) * 0.01).astype(
config.floatX
)
],
)
out = aet_slinalg.solve(x, b)
......@@ -294,7 +304,11 @@ def test_jax_basic():
out_fg = FunctionGraph([x], [out])
compare_jax_and_py(
out_fg,
[(np.eye(10) + np.random.randn(10, 10) * 0.01).astype(config.floatX)],
[
(np.eye(10) + rng.standard_normal(size=(10, 10)) * 0.01).astype(
config.floatX
)
],
)
......@@ -405,9 +419,9 @@ def test_jax_eye():
def test_jax_basic_multiout():
rng = np.random.default_rng(213234)
np.random.seed(213234)
M = np.random.normal(size=(3, 3))
M = rng.normal(size=(3, 3))
X = M.dot(M.T)
x = matrix("x")
......@@ -638,7 +652,9 @@ def test_jax_Subtensors_omni():
reason="Omnistaging cannot be disabled",
)
def test_jax_IncSubtensor():
x_np = np.random.uniform(-1, 1, size=(3, 4, 5)).astype(config.floatX)
rng = np.random.default_rng(213234)
x_np = rng.uniform(-1, 1, size=(3, 4, 5)).astype(config.floatX)
x_aet = aet.arange(3 * 4 * 5).reshape((3, 4, 5)).astype(config.floatX)
# "Set" basic indices
......@@ -661,7 +677,7 @@ def test_jax_IncSubtensor():
# "Set" advanced indices
st_aet = aet.as_tensor_variable(
np.random.uniform(-1, 1, size=(2, 4, 5)).astype(config.floatX)
rng.uniform(-1, 1, size=(2, 4, 5)).astype(config.floatX)
)
out_aet = aet_subtensor.set_subtensor(x_aet[np.r_[0, 2]], st_aet)
assert isinstance(out_aet.owner.op, aet_subtensor.AdvancedIncSubtensor1)
......@@ -707,7 +723,7 @@ def test_jax_IncSubtensor():
# "Increment" advanced indices
st_aet = aet.as_tensor_variable(
np.random.uniform(-1, 1, size=(2, 4, 5)).astype(config.floatX)
rng.uniform(-1, 1, size=(2, 4, 5)).astype(config.floatX)
)
out_aet = aet_subtensor.inc_subtensor(x_aet[np.r_[0, 2]], st_aet)
assert isinstance(out_aet.owner.op, aet_subtensor.AdvancedIncSubtensor1)
......@@ -1202,6 +1218,7 @@ def test_random_unimplemented():
compare_jax_and_py(fgraph, [])
@pytest.mark.xfail(reason="Generators not yet supported in JAX")
def test_RandomStream():
srng = RandomStream(seed=123)
out = srng.normal() - srng.normal()
......@@ -1211,3 +1228,11 @@ def test_RandomStream():
jax_res_2 = fn()
assert np.array_equal(jax_res_1, jax_res_2)
@pytest.mark.xfail(reason="Generators not yet supported in JAX")
def test_random_generators():
rng = shared(np.random.default_rng(123))
out = normal(rng=rng)
fgraph = FunctionGraph([out.owner.inputs[0]], [out], clone=False)
compare_jax_and_py(fgraph, [])
tests/link/test_numba.py
浏览文件 @ 39c11472
......@@ -88,7 +88,7 @@ opts = OptimizationQuery(include=[None], exclude=["cxx_only", "BlasOpt"])
numba_mode = Mode(NumbaLinker(), opts)
py_mode = Mode("py", opts)
rng = np.random.RandomState(42849)
rng = np.random.default_rng(42849)
def set_test_value(x, v):
......@@ -291,13 +291,13 @@ def test_create_numba_signature(v, expected, force_scalar):
[
(
[aet.vector()],
[rng.randn(100).astype(config.floatX)],
[rng.standard_normal(100).astype(config.floatX)],
lambda x: aet.sigmoid(x),
None,
),
(
[aet.vector() for i in range(4)],
[rng.randn(100).astype(config.floatX) for i in range(4)],
[rng.standard_normal(100).astype(config.floatX) for i in range(4)],
lambda x, y, x1, y1: (x + y) * (x1 + y1) * y,
None,
),
......@@ -311,8 +311,8 @@ def test_create_numba_signature(v, expected, force_scalar):
(
[aet.vector(), aet.vector()],
[
rng.randn(100).astype(config.floatX),
rng.randn(100).astype(config.floatX),
rng.standard_normal(100).astype(config.floatX),
rng.standard_normal(100).astype(config.floatX),
],
lambda x, y: ati.add_inplace(x, y),
None,
......@@ -320,8 +320,8 @@ def test_create_numba_signature(v, expected, force_scalar):
(
[aet.vector(), aet.vector()],
[
rng.randn(100).astype(config.floatX),
rng.randn(100).astype(config.floatX),
rng.standard_normal(100).astype(config.floatX),
rng.standard_normal(100).astype(config.floatX),
],
lambda x, y: my_multi_out(x, y),
NotImplementedError,
......@@ -1954,7 +1954,9 @@ def test_MaxAndArgmax(x, axes, exc):
(
set_test_value(
aet.lmatrix(),
(lambda x: x.T.dot(x))(rng.randint(1, 10, size=(3, 3)).astype("int64")),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
True,
None,
......@@ -2004,7 +2006,9 @@ def test_Cholesky(x, lower, exc):
(
set_test_value(
aet.lmatrix(),
(lambda x: x.T.dot(x))(rng.randint(1, 10, size=(3, 3)).astype("int64")),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
set_test_value(aet.dvector(), rng.random(size=(3,)).astype("float64")),
"general",
......@@ -2120,7 +2124,9 @@ y = np.array(
(
set_test_value(
aet.lmatrix(),
(lambda x: x.T.dot(x))(rng.randint(1, 10, size=(3, 3)).astype("int64")),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
None,
),
......@@ -2160,7 +2166,9 @@ def test_Eig(x, exc):
(
set_test_value(
aet.lmatrix(),
(lambda x: x.T.dot(x))(rng.randint(1, 10, size=(3, 3)).astype("int64")),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
"U",
UserWarning,
......@@ -2200,7 +2208,9 @@ def test_Eigh(x, uplo, exc):
(
set_test_value(
aet.lmatrix(),
(lambda x: x.T.dot(x))(rng.randint(1, 10, size=(3, 3)).astype("int64")),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
None,
),
......@@ -2244,7 +2254,9 @@ def test_MatrixInverse(x, exc):
(
set_test_value(
aet.lmatrix(),
(lambda x: x.T.dot(x))(rng.randint(1, 10, size=(3, 3)).astype("int64")),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
"reduced",
None,
......@@ -2252,7 +2264,9 @@ def test_MatrixInverse(x, exc):
(
set_test_value(
aet.lmatrix(),
(lambda x: x.T.dot(x))(rng.randint(1, 10, size=(3, 3)).astype("int64")),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
"complete",
UserWarning,
......@@ -2303,7 +2317,9 @@ def test_QRFull(x, mode, exc):
(
set_test_value(
aet.lmatrix(),
(lambda x: x.T.dot(x))(rng.randint(1, 10, size=(3, 3)).astype("int64")),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
True,
True,
......@@ -2312,7 +2328,9 @@ def test_QRFull(x, mode, exc):
(
set_test_value(
aet.lmatrix(),
(lambda x: x.T.dot(x))(rng.randint(1, 10, size=(3, 3)).astype("int64")),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
True,
False,
......
tests/sandbox/linalg/test_linalg.py
浏览文件 @ 39c11472
......@@ -41,9 +41,9 @@ def test_rop_lop():
)
scan_f = function([mx, mv], sy)
rng = np.random.RandomState(utt.fetch_seed())
vx = np.asarray(rng.randn(4, 4), aesara.config.floatX)
vv = np.asarray(rng.randn(4, 4), aesara.config.floatX)
rng = np.random.default_rng(utt.fetch_seed())
vx = np.asarray(rng.standard_normal((4, 4)), aesara.config.floatX)
vv = np.asarray(rng.standard_normal((4, 4)), aesara.config.floatX)
v1 = rop_f(vx, vv)
v2 = scan_f(vx, vv)
......@@ -75,13 +75,13 @@ def test_rop_lop():
def test_spectral_radius_bound():
tol = 10 ** (-6)
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
x = matrix()
radius_bound = spectral_radius_bound(x, 5)
f = aesara.function([x], radius_bound)
shp = (3, 4)
m = rng.rand(*shp)
m = rng.random(shp)
m = np.cov(m).astype(config.floatX)
radius_bound_aesara = f(m)
......
tests/sandbox/test_minimal.py
浏览文件 @ 39c11472
......@@ -16,7 +16,7 @@ class TestMinimal:
"""
def setup_method(self):
self.rng = np.random.RandomState(utt.fetch_seed(666))
self.rng = np.random.default_rng(utt.fetch_seed(666))
def test_minimal(self):
A = matrix()
......@@ -26,7 +26,7 @@ class TestMinimal:
f = function([A, b], minimal(A, A, b, b, A))
print("built")
Aval = self.rng.randn(5, 5)
Aval = self.rng.standard_normal((5, 5))
bval = np.arange(5, dtype=float)
f(Aval, bval)
print("done")
tests/sandbox/test_rng_mrg.py
浏览文件 @ 39c11472
......@@ -25,7 +25,6 @@ from tests import unittest_tools as utt
# TODO: test optimizer mrg_random_make_inplace
utt.seed_rng()
# Results generated by Java code using L'Ecuyer et al.'s code, with:
# main seed: [12345]*6 (default)
......@@ -90,7 +89,9 @@ def test_get_substream_rstates():
n_streams = 100
dtype = "float32"
rng = MRG_RandomStream(np.random.randint(2147462579))
rng = MRG_RandomStream(
np.random.default_rng(utt.fetch_seed()).integers(2147462579)
)
rng.get_substream_rstates(n_streams, dtype)
......@@ -889,13 +890,13 @@ def test_multMatVect():
f0 = function([A1, s1, m1, A2, s2, m2], g0)
i32max = np.iinfo(np.int32).max
A1 = np.random.randint(0, i32max, (3, 3)).astype("int64")
s1 = np.random.randint(0, i32max, 3).astype("int32")
m1 = np.asarray(np.random.randint(i32max), dtype="int32")
A2 = np.random.randint(0, i32max, (3, 3)).astype("int64")
s2 = np.random.randint(0, i32max, 3).astype("int32")
m2 = np.asarray(np.random.randint(i32max), dtype="int32")
rng = np.random.default_rng(utt.fetch_seed())
A1 = rng.integers(0, i32max, (3, 3)).astype("int64")
s1 = rng.integers(0, i32max, 3).astype("int32")
m1 = np.asarray(rng.integers(i32max), dtype="int32")
A2 = rng.integers(0, i32max, (3, 3)).astype("int64")
s2 = rng.integers(0, i32max, 3).astype("int32")
m2 = np.asarray(rng.integers(i32max), dtype="int32")
f0.input_storage[0].storage[0] = A1
f0.input_storage[1].storage[0] = s1
......@@ -964,7 +965,7 @@ def rng_mrg_overflow(sizes, fct, mode, should_raise_error):
@pytest.mark.slow
def test_overflow_cpu():
# run with AESARA_FLAGS=mode=FAST_RUN,device=cpu,floatX=float32
rng = MRG_RandomStream(np.random.randint(1234))
rng = MRG_RandomStream(np.random.default_rng(utt.fetch_seed()).integers(1234))
fct = rng.uniform
with config.change_flags(compute_test_value="off"):
# should raise error as the size overflows
......@@ -1107,8 +1108,10 @@ def test_target_parameter():
@config.change_flags(compute_test_value="off")
def test_undefined_grad_opt():
# Make sure that undefined grad get removed in optimized graph.
random = MRG_RandomStream(np.random.randint(1, 2147462579))
pvals = shared(np.random.rand(10, 20).astype(config.floatX))
random = MRG_RandomStream(
np.random.default_rng(utt.fetch_seed()).integers(1, 2147462579)
)
pvals = shared(np.random.random((10, 20)).astype(config.floatX))
pvals = pvals / pvals.sum(axis=1)
pvals = zero_grad(pvals)
samples = random.multinomial(pvals=pvals, n=1)
......
tests/scan/test_basic.py
浏览文件 @ 39c11472
......@@ -254,8 +254,6 @@ def scan_nodes_from_fct(fct):
class TestScan:
def setup_method(self):
utt.seed_rng()
# generator network, only one output , type scalar ; no sequence or
# non sequence arguments
......@@ -299,7 +297,7 @@ class TestScan:
if tmpdir is not None:
shutil.rmtree(tmpdir)
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
state = rng.uniform()
steps = 5
......@@ -332,7 +330,7 @@ class TestScan:
assert all(i.value is None for i in scan_node.op.fn.input_storage)
assert all(o.value is None for o in scan_node.op.fn.output_storage)
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
state = rng.uniform()
steps = 5
......@@ -365,7 +363,7 @@ class TestScan:
[state, n_steps], output, updates=updates, allow_input_downcast=True
)
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
state = rng.uniform()
steps = 5
......@@ -404,7 +402,7 @@ class TestScan:
# This assertion fails if savemem optimization failed on scan
if config.mode != "FAST_COMPILE":
assert nodes[0].op._scan_savemem_visited
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
my_f(rng.uniform(size=(3,)), 4, np.int64([2, 2, 3]))
@pytest.mark.slow
......@@ -451,7 +449,7 @@ class TestScan:
[u, x0, W_in, W], output, updates=updates, allow_input_downcast=True
)
# get random initial values
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
v_u = rng.uniform(-5.0, 5.0, size=(4,))
v_x0 = rng.uniform()
W = rng.uniform()
......@@ -468,7 +466,7 @@ class TestScan:
# simple rnn, one input, one state, weights for each; input/state
# are vectors, weights are scalars; using shared variables
def test_one_sequence_one_output_weights_shared(self):
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
u = vector("u")
x0 = scalar("x0")
W_in = shared(asarrayX(rng.uniform()), name="w_in")
......@@ -503,7 +501,7 @@ class TestScan:
# some rnn with multiple outputs and multiple inputs; other
# dimension instead of scalars/vectors
def test_multiple_inputs_multiple_outputs(self):
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
vW_in2 = asarrayX(rng.uniform(-5.0, 5.0, size=(2,)))
vW = asarrayX(rng.uniform(-5.0, 5.0, size=(2, 2)))
vWout = asarrayX(rng.uniform(-5.0, 5.0, size=(2,)))
......@@ -557,7 +555,7 @@ class TestScan:
def test_multiple_outs_taps(self):
l = 5
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
vW_in2 = asarrayX(rng.uniform(-2.0, 2.0, size=(2,)))
vW = asarrayX(rng.uniform(-2.0, 2.0, size=(2, 2)))
......@@ -778,7 +776,9 @@ class TestScan:
# Call verify_grad to ensure the correctness of the second gradients
floatX = config.floatX
inputs_test_values = [np.random.random(3).astype(floatX)]
inputs_test_values = [
np.random.default_rng(utt.fetch_seed()).random(3).astype(floatX)
]
utt.verify_grad(get_sum_of_grad, inputs_test_values)
def test_verify_second_grad_mitsot1(self):
......@@ -805,11 +805,13 @@ class TestScan:
# Call verify_grad to ensure the correctness of the second gradients
floatX = config.floatX
rng = np.random.default_rng(utt.fetch_seed())
inputs_test_values = [
np.random.random((2, 3)).astype(floatX),
np.random.random(3).astype(floatX),
rng.random((2, 3)).astype(floatX),
rng.random(3).astype(floatX),
]
utt.verify_grad(get_sum_of_grad, inputs_test_values)
utt.verify_grad(get_sum_of_grad, inputs_test_values, rng=rng)
def test_grad_two_scans(self):
......@@ -818,7 +820,11 @@ class TestScan:
t = imatrix("t")
# forward pass
W = shared(np.random.randn(2, 2).astype("float32"), name="W", borrow=True)
W = shared(
np.random.default_rng(utt.fetch_seed()).random((2, 2)).astype("float32"),
name="W",
borrow=True,
)
def forward_scanner(x_t):
a2_t = dot(x_t, W)
......@@ -841,7 +847,7 @@ class TestScan:
# vectors, weights are scalars; using shared variables and past
# taps (sequences and outputs)
def test_using_taps_input_output(self):
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
vW = asarrayX(rng.uniform())
vW_in = asarrayX(rng.uniform())
vu = asarrayX(rng.uniform(-5.0, 5.0, size=(4,)))
......@@ -885,7 +891,7 @@ class TestScan:
# vectors, weights are scalars; using shared variables and past
# taps (sequences and outputs) and future taps for sequences
def test_past_future_taps_shared(self):
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
vW = asarrayX(rng.uniform())
vW_in = asarrayX(rng.uniform())
vu = asarrayX(rng.uniform(-5.0, 5.0, size=(6,)))
......@@ -922,7 +928,7 @@ class TestScan:
# simple rnn ; compute inplace version 1
@utt.assertFailure_fast
def test_inplace1(self):
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
vW = asarrayX(np.random.uniform())
vW_in = asarrayX(np.random.uniform())
vu0 = asarrayX(rng.uniform(-5.0, 5.0, size=(3,)))
......@@ -989,7 +995,7 @@ class TestScan:
# simple rnn ; compute inplace version 2
@utt.assertFailure_fast
def test_inplace2(self):
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
vW = asarrayX(np.random.uniform())
vW_in = asarrayX(np.random.uniform())
vu0 = asarrayX(rng.uniform(-5.0, 5.0, size=(3,)))
......@@ -1057,7 +1063,7 @@ class TestScan:
@utt.assertFailure_fast
def test_inplace3(self):
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
vx0 = asarrayX(rng.uniform())
vx1 = asarrayX(rng.uniform())
......@@ -1079,15 +1085,15 @@ class TestScan:
# Shared variable with updates
def test_shared_arguments_with_updates(self):
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
vW1 = asarrayX(rng.rand(2, 3))
vW2 = asarrayX(rng.rand(3, 2))
vu1 = asarrayX(rng.rand(3, 2))
vu2 = asarrayX(rng.rand(3, 3))
vy0 = asarrayX(rng.rand(3, 2))
vy1 = asarrayX(rng.rand(2))
vu1 = asarrayX(rng.rand(3, 2))
vW1 = asarrayX(rng.random((2, 3)))
vW2 = asarrayX(rng.random((3, 2)))
vu1 = asarrayX(rng.random((3, 2)))
vu2 = asarrayX(rng.random((3, 3)))
vy0 = asarrayX(rng.random((3, 2)))
vy1 = asarrayX(rng.random((2)))
vu1 = asarrayX(rng.random((3, 2)))
W1 = shared(vW1, "W1")
W2 = shared(vW2, "W2")
......@@ -1182,8 +1188,8 @@ class TestScan:
)
my_f = function([], values, updates=updates, allow_input_downcast=True)
rng_seed = np.random.RandomState(utt.fetch_seed()).randint(2 ** 30)
rng = np.random.RandomState(int(rng_seed)) # int() is for 32bit
rng_seed = np.random.default_rng(utt.fetch_seed()).integers(2 ** 30)
rng = np.random.default_rng(int(rng_seed)) # int() is for 32bit
numpy_v = np.zeros((10, 2))
for i in range(10):
......@@ -1195,8 +1201,8 @@ class TestScan:
utt.assert_allclose(aesara_v, numpy_v[5:, :])
def test_gibbs_chain(self):
rng = np.random.RandomState(utt.fetch_seed())
v_W = np.array(rng.rand(20, 30) - 0.5, dtype="float32")
rng = np.random.default_rng(utt.fetch_seed())
v_W = np.array(rng.random((20, 30)) - 0.5, dtype="float32")
v_vsample = np.array(
rng.binomial(
1,
......@@ -1205,8 +1211,8 @@ class TestScan:
),
dtype="float32",
)
v_bvis = np.array(rng.rand(20) - 0.5, dtype="float32")
v_bhid = np.array(rng.rand(30) - 0.5, dtype="float32")
v_bvis = np.array(rng.random((20)) - 0.5, dtype="float32")
v_bhid = np.array(rng.random((30)) - 0.5, dtype="float32")
W = shared(v_W, "vW")
bhid = shared(v_bhid, "vbhid")
bvis = shared(v_bvis, "vbvis")
......@@ -1231,12 +1237,12 @@ class TestScan:
[vsample], aesara_vsamples[-1], updates=updates, allow_input_downcast=True
)
_rng = np.random.RandomState(utt.fetch_seed())
rng_seed = _rng.randint(2 ** 30)
nrng1 = np.random.RandomState(int(rng_seed)) # int() is for 32bit
_rng = np.random.default_rng(utt.fetch_seed())
rng_seed = _rng.integers(2 ** 30)
nrng1 = np.random.default_rng(int(rng_seed)) # int() is for 32bit
rng_seed = _rng.randint(2 ** 30)
nrng2 = np.random.RandomState(int(rng_seed)) # int() is for 32bit
rng_seed = _rng.integers(2 ** 30)
nrng2 = np.random.default_rng(int(rng_seed)) # int() is for 32bit
def numpy_implementation(vsample):
for idx in range(10):
......@@ -1256,7 +1262,7 @@ class TestScan:
utt.assert_allclose(t_result, n_result)
def test_only_shared_no_input_no_output(self):
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
v_state = asarrayX(rng.uniform())
state = shared(v_state, "vstate")
......@@ -1284,7 +1290,7 @@ class TestScan:
)
f2 = function([u], outputs, updates=updates, allow_input_downcast=True)
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
v_u = rng.uniform(-5.0, 5.0, size=(5,))
numpy_result = v_u + 3
......@@ -1299,7 +1305,7 @@ class TestScan:
f = function([v], abs_expr, updates=abs_updates, allow_input_downcast=True)
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
vals = rng.uniform(-5.0, 5.0, size=(10,))
abs_vals = abs(vals)
aesara_vals = f(vals)
......@@ -1328,7 +1334,7 @@ class TestScan:
[u, x0, W_in, W], output, updates=updates, allow_input_downcast=True
)
# get random initial values
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
v_u = rng.uniform(-5.0, 5.0, size=(4,))
v_x0 = rng.uniform()
W = rng.uniform()
......@@ -1349,7 +1355,7 @@ class TestScan:
result, updates = aet_reduce(lambda x, y: x + y, v, s)
f = function([v, s], result, updates=updates, allow_input_downcast=True)
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
v_v = rng.uniform(-5.0, 5.0, size=(5,))
assert abs(np.sum(v_v) - f(v_v, 0.0)) < 1e-3
......@@ -1388,7 +1394,7 @@ class TestScan:
)
# get random initial values
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
v_u = np.array(rng.uniform(-0.5, 0.5, size=(10,)), dtype=config.floatX)
v_x0 = np.array(rng.uniform(), dtype=config.floatX)
W = np.array(rng.uniform(), dtype=config.floatX)
......@@ -1401,7 +1407,7 @@ class TestScan:
assert max_err <= 1e-2
def test_grad_multiple_outs(self):
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
vW_in2 = asarrayX(rng.uniform(-0.1, 0.1, size=(2,)))
vW = asarrayX(rng.uniform(-0.1, 0.1, size=(2, 2)))
vWout = asarrayX(rng.uniform(-0.1, 0.1, size=(2,)))
......@@ -1464,7 +1470,7 @@ class TestScan:
@pytest.mark.slow
def test_grad_multiple_outs_taps(self):
n = 5
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
vW_in2 = asarrayX(rng.uniform(-0.2, 0.2, size=(2,)))
vW = asarrayX(rng.uniform(-0.2, 0.2, size=(2, 2)))
......@@ -1543,7 +1549,7 @@ class TestScan:
@pytest.mark.slow
def test_grad_multiple_outs_taps_backwards(self):
n = 5
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
vW_in2 = asarrayX(rng.uniform(-0.2, 0.2, size=(2,)))
vW = asarrayX(rng.uniform(-0.2, 0.2, size=(2, 2)))
vWout = asarrayX(rng.uniform(-0.2, 0.2, size=(2,)))
......@@ -1603,7 +1609,7 @@ class TestScan:
assert max_err <= 1e-2
def test_grad_multiple_outs_some_uncomputable(self):
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
vW_in = asarrayX(rng.uniform(-3.0, 3.0, size=(2, 2)))
v_u = asarrayX(rng.uniform(-3.0, 3.0, size=(5, 2)))
v_u2 = np.array([1, 3, 4, 6, 8], dtype="int32")
......@@ -1652,9 +1658,9 @@ class TestScan:
def reset_rng_fn(fn, *args):
for idx, arg in enumerate(fn.maker.expanded_inputs):
if arg.value and isinstance(arg.value.data, np.random.RandomState):
if arg.value and isinstance(arg.value.data, np.random.Generator):
obj = fn.maker.expanded_inputs[idx].value
obj.data = np.random.RandomState(123)
obj.data = np.random.default_rng(123)
fn.maker.expanded_inputs[idx].value = obj
return fn(*args)
......@@ -1686,7 +1692,7 @@ class TestScan:
assert result == expected_result
def test_grad_multiple_outs_some_truncate(self):
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
vW_in = asarrayX(rng.uniform(-0.1, 0.1, size=(2, 2)))
v_u = asarrayX(rng.uniform(-0.1, 0.1, size=(5, 2)))
v_x0 = asarrayX(rng.uniform(-0.1, 0.1, size=(2,)))
......@@ -1733,9 +1739,9 @@ class TestScan:
def reset_rng_fn(fn, *args):
for idx, arg in enumerate(fn.maker.expanded_inputs):
if arg.value and isinstance(arg.value.data, np.random.RandomState):
if arg.value and isinstance(arg.value.data, np.random.Generator):
obj = fn.maker.expanded_inputs[idx].value
obj.data = np.random.RandomState(123)
obj.data = np.random.default_rng(123)
fn.maker.expanded_inputs[idx].value = obj
out = fn(*args)
return out
......@@ -1763,7 +1769,7 @@ class TestScan:
def _grad_mout_helper(self, n_iters, mode):
# Created on Tue Oct 07 13:28:51 2014
# @author: vaneetke
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
n_hid = 3
n_in = 1
n_out = 1
......@@ -1842,7 +1848,7 @@ class TestScan:
f = function([x], [y, z], updates=updates, allow_input_downcast=True)
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
nx = rng.uniform(size=(10, 10))
ny1, nz1 = f(nx)
ny2, nz2 = f(nx)
......@@ -1921,7 +1927,7 @@ class TestScan:
a = vector()
init_a = vector()
b = shared(np.random.rand(5, 4))
b = shared(np.random.default_rng(utt.fetch_seed()).random((5, 4)))
def inner_func(a):
return a + 1, OrderedDict([(b, 2 * b)])
......@@ -2064,7 +2070,7 @@ class TestScan:
# some rnn with multiple outputs and multiple inputs; other
# dimension instead of scalars/vectors
def test_reordering(self):
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
vW_in2 = asarrayX(rng.uniform(-0.5, 0.5, size=(2,)))
vW = asarrayX(rng.uniform(-0.5, 0.5, size=(2, 2)))
......@@ -2141,7 +2147,7 @@ class TestScan:
function(inputs=[to_scan, seq, f1], outputs=t_grad, allow_input_downcast=True)
def test_save_mem(self):
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
vW_in2 = asarrayX(rng.uniform(-0.5, 0.5, size=(2,)))
vW = asarrayX(rng.uniform(-0.5, 0.5, size=(2, 2)))
......@@ -2255,7 +2261,7 @@ class TestScan:
allow_input_downcast=True,
)
# get random initial values
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
v_u = rng.uniform(-5.0, 5.0, size=(20,))
# compute the output in numpy
......@@ -2314,7 +2320,7 @@ class TestScan:
)
# get random initial values
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
v_u = rng.uniform(-5.0, 5.0, size=(20,))
# compute the output in numpy
......@@ -2591,7 +2597,7 @@ class TestScan:
scans = [n for n in topo if isinstance(n.op, Scan)]
assert len(scans) == 2
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
x_val = rng.uniform(size=(4,)).astype(config.floatX)
y_val = rng.uniform(size=(4,)).astype(config.floatX)
# Run it so DebugMode can detect optimization problems.
......@@ -2645,9 +2651,9 @@ class TestScan:
M = init_predictive_output(inputs, targets, hyp, x_star, s_star)
X = np.random.random((10, 4))
Y = np.random.random((10, 3))
test_m = np.random.random((4,))
X = np.random.default_rng(utt.fetch_seed()).random((10, 4))
Y = np.random.default_rng(utt.fetch_seed()).random((10, 3))
test_m = np.random.default_rng(utt.fetch_seed()).random((4,))
test_s = np.eye(4)
# Compute expected outputs (jacobian of M wrt x_star)
......@@ -2737,7 +2743,9 @@ class TestScan:
mem_val = np.zeros((2,), dtype="float32")
memory = shared(mem_val)
W = shared(np.random.random((5, 2)).astype("float32"))
W = shared(
np.random.default_rng(utt.fetch_seed()).random((5, 2)).astype("float32")
)
def f(inp, mem):
i = aet.join(0, inp, mem)
......@@ -2751,7 +2759,7 @@ class TestScan:
f = function([x], outs[0])
f2 = function([x], outs[1])
x_val = np.random.random((4, 3)).astype("float32")
x_val = np.random.default_rng(utt.fetch_seed()).random((4, 3)).astype("float32")
f_vals = f(x_val)
memory.set_value(mem_val)
......@@ -2858,7 +2866,7 @@ class TestScan:
@pytest.mark.slow
def test_rop2(self):
seed = utt.fetch_seed()
rng = np.random.RandomState(seed)
rng = np.random.default_rng(seed)
floatX = config.floatX
v_u = np.array(rng.uniform(size=(3, 5)) - 0.5, dtype=floatX)
v_W = np.array(rng.uniform(size=(5, 5)) - 0.5, dtype=floatX)
......@@ -2940,7 +2948,7 @@ class TestScan:
def test_rop(self):
seed = utt.fetch_seed()
rng = np.random.RandomState(seed)
rng = np.random.default_rng(seed)
floatX = config.floatX
v_u = np.array(rng.uniform(size=(20, 5)), dtype=floatX)
v_W = np.array(rng.uniform(size=(5, 5)), dtype=floatX)
......@@ -3048,7 +3056,7 @@ class TestScan:
assert len(scan_nodes) == 0
seed = utt.fetch_seed()
rng = np.random.RandomState(seed)
rng = np.random.default_rng(seed)
floatX = config.floatX
v_h = np.array(rng.uniform(size=(2,)), dtype=floatX)
v_W1 = np.array(rng.uniform(size=(2, 2)), dtype=floatX)
......@@ -3094,8 +3102,8 @@ class TestScan:
# Compare the results of the two implementations
input_values = [
np.random.random((5, 5)).astype("float32"),
np.random.random((5, 5)).astype("float32"),
np.random.default_rng(utt.fetch_seed()).random((5, 5)).astype("float32"),
np.random.default_rng(utt.fetch_seed()).random((5, 5)).astype("float32"),
np.arange(5).astype("float32"),
]
......@@ -3585,9 +3593,13 @@ class TestScan:
# Run the function and validate the outputs
dtype = config.floatX
seq_value = np.random.random((10, 3)).astype(dtype)
out_init_value = np.random.random((3, 3)).astype(dtype)
non_seq_value = np.random.random(3).astype(dtype)
seq_value = (
np.random.default_rng(utt.fetch_seed()).random((10, 3)).astype(dtype)
)
out_init_value = (
np.random.default_rng(utt.fetch_seed()).random((3, 3)).astype(dtype)
)
non_seq_value = np.random.default_rng(utt.fetch_seed()).random(3).astype(dtype)
outputs = fct(seq_value, out_init_value, non_seq_value)
......@@ -3689,7 +3701,7 @@ class TestScan:
assert tf([1.0, 2.0, -3.0, 4.0], 2.0) == 42
def test_return_steps(self):
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
vW_in2 = asarrayX(rng.uniform(-0.5, 0.5, size=(2,)))
vW = asarrayX(rng.uniform(-0.5, 0.5, size=(2, 2)))
......@@ -3817,8 +3829,14 @@ class TestScan:
f = function(inputs=[x, w], outputs=get_outputs(x, w))
# Test the function to ensure it returns valid results
x_value = np.random.random((2, 2, 3)).astype(config.floatX)
w_value = np.random.random((3, 3)).astype(config.floatX)
x_value = (
np.random.default_rng(utt.fetch_seed())
.random((2, 2, 3))
.astype(config.floatX)
)
w_value = (
np.random.default_rng(utt.fetch_seed()).random((3, 3)).astype(config.floatX)
)
expected_output = np.tile(x_value[:, 0].sum(0), (3, 1)).transpose()
output = f(x_value, w_value)
......@@ -3846,7 +3864,7 @@ class TestScan:
gw, gx = grad(loss, [w, xinit])
grad_fn = function([xinit, w], [gx, gw], allow_input_downcast=True)
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
# If numbers are small, the gradients with respect to x are small
# and the numeric differentiation becomes unstable.
# To fix this issue I ensure we are sampling numbers larger in
......@@ -4085,7 +4103,7 @@ class TestScan:
f = function([v], gv)
# Ensure the output of the function is valid
output = f(np.random.random(5))
output = f(np.random.default_rng(utt.fetch_seed()).random(5))
utt.assert_allclose(output, np.ones(5))
def test_dot_optimization(self):
......@@ -4097,7 +4115,7 @@ class TestScan:
outputs_info=[aet.zeros_like(A)],
)
f = function([A, B], S.owner.inputs[0][-1])
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
vA = rng.uniform(size=(5, 5)).astype(config.floatX)
vB = rng.uniform(size=(5, 5)).astype(config.floatX)
utt.assert_allclose(f(vA, vB), np.dot(vA.T, vB))
......@@ -4159,7 +4177,7 @@ class TestScan:
mode=Mode(linker="py"),
allow_input_downcast=True,
)
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
v_u = asarrayX(rng.uniform(size=(5,)))
outs = f(v_u, [0, 0, 0], 0)
utt.assert_allclose(outs[0], v_u + 1)
......@@ -4196,7 +4214,7 @@ class TestScan:
mode=Mode(linker="py"),
allow_input_downcast=True,
)
rng = np.random.RandomState(utt.fetch_seed())
rng = np.random.default_rng(utt.fetch_seed())
v_w = asarrayX(rng.uniform())
outs = f(v_w, [0, 0, 0], 0)
utt.assert_allclose(outs[0], v_w + 1)
......@@ -4627,7 +4645,7 @@ def test_speed_rnn():
np.random.seed(2523452)
r = np.arange(L * N).astype(config.floatX).reshape(L, N)
w = np.random.randn(N, N).astype(config.floatX)
w = np.random.default_rng(utt.fetch_seed()).random((N, N)).astype(config.floatX)
def f_py():
for i in range(1, L):
......@@ -4694,7 +4712,7 @@ def test_speed_batchrnn():
np.random.seed(2523452)
r = np.arange(B * L * N).astype(config.floatX).reshape(L, B, N)
w = np.random.randn(N, N).astype(config.floatX)
w = np.random.default_rng(utt.fetch_seed()).random((N, N)).astype(config.floatX)
t0 = time.time()
for i in range(1, L):
......@@ -4802,7 +4820,12 @@ def test_compute_test_value_grad_cast():
h = matrix("h")
h.tag.test_value = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=config.floatX)
w = shared(np.random.randn(4, 3).astype(config.floatX), name="w")
w = shared(
np.random.default_rng(utt.fetch_seed())
.random((4, 3))
.astype(config.floatX),
name="w",
)
outputs, _ = scan(
lambda i, h, w: (dot(h[i], w), i),
......@@ -4848,7 +4871,9 @@ def test_default_value_broadcasted():
return np.asarray(X, dtype=config.floatX)
def init_weights(shape, name):
return shared(floatx(np.random.randn(*shape) * 0.1), name)
return shared(
floatx(np.random.default_rng(utt.fetch_seed()).random(shape) * 0.1), name
)
X = matrix("X")
in_size = 2
......@@ -4869,7 +4894,7 @@ def test_default_value_broadcasted():
gW_x = grad(cost, W_x)
updates = [(W_x, W_x - 0.1 * gW_x)]
f = function([X], outputs=cost, updates=updates)
f(np.random.rand(10, in_size).astype(X.dtype))
f(np.random.default_rng(utt.fetch_seed()).random((10, in_size)).astype(X.dtype))
class TestInconsistentBroadcast:
......
tests/scan/test_opt.py
浏览文件 @ 39c11472
......@@ -24,7 +24,7 @@ class TestGaussNewton:
"""
def setup_method(self):
self.rng = np.random.RandomState(utt.fetch_seed())
self.rng = np.random.default_rng(utt.fetch_seed())
def _run(self, num_features, num_timesteps, batch_size, mode):
# determine shapes of inputs and targets depending on the batch size
......
tests/sparse/sandbox/test_sp.py
浏览文件 @ 39c11472
......@@ -33,9 +33,9 @@ class TestSP:
bias = dvector()
kerns = dmatrix()
input = dmatrix()
rng = np.random.RandomState(3423489)
filters = rng.randn(nkern, np.prod(kshp))
biasvals = rng.randn(nkern)
rng = np.random.default_rng(3423489)
filters = rng.standard_normal((nkern, np.prod(kshp)))
biasvals = rng.standard_normal((nkern))
for mode in ("FAST_COMPILE", "FAST_RUN"):
ttot, ntot = 0, 0
......@@ -133,7 +133,7 @@ class TestSP:
# symbolic stuff
kerns = [dmatrix(), dmatrix()]
input = dmatrix()
# rng = np.random.RandomState(3423489)
# rng = np.random.default_rng(3423489)
# build actual input images
img2d = np.arange(bsize * np.prod(imshp)).reshape((bsize,) + imshp)
......@@ -184,7 +184,7 @@ class TestSP:
def test_maxpool(self):
# generate flatted images
maxpoolshps = ((2, 2), (3, 3), (4, 4), (5, 5), (6, 6))
imval = np.random.rand(4, 5, 10, 10)
imval = np.random.random((4, 5, 10, 10))
images = dmatrix()
for maxpoolshp in maxpoolshps:
......
tests/sparse/test_basic.py
浏览文件 @ 39c11472
......@@ -140,8 +140,8 @@ def random_lil(shape, dtype, nnz):
huge = 2 ** 30
for k in range(nnz):
# set non-zeros in random locations (row x, col y)
idx = np.random.randint(1, huge + 1, size=2) % shape
value = np.random.rand()
idx = np.random.default_rng().integers(1, huge + 1, size=2) % shape
value = np.random.random()
# if dtype *int*, value will always be zeros!
if dtype in sparse.integer_dtypes:
value = int(value * 100)
......@@ -201,11 +201,11 @@ def sparse_random_inputs(
if out_dtype in sparse.discrete_dtypes:
if not gap:
value = np.random.randint(50, size=shape)
value = np.random.default_rng().integers(50, size=shape)
elif len(gap) == 2:
value = np.random.randint(gap[0], gap[1], size=shape)
value = np.random.default_rng().integers(gap[0], gap[1], size=shape)
else:
value = np.random.randint(gap[0], size=shape)
value = np.random.default_rng().integers(gap[0], size=shape)
else:
if not gap:
value = np.random.random(shape)
......@@ -240,7 +240,7 @@ def sparse_random_inputs(
if explicit_zero:
for idx in range(n):
assert data[idx].nnz > 1, "can't make a sparse matrix with explicit 0"
d_idx = np.random.randint(data[idx].nnz)
d_idx = np.random.default_rng().integers(data[idx].nnz)
data[idx].data[d_idx] = 0
# numpy 1.5.0 with scipy 0.9.0 have sp.sparse.XXX_matrix return
......@@ -379,9 +379,6 @@ class TestVerifyGradSparse:
class TestTranspose:
def setup_method(self):
utt.seed_rng()
def test_transpose_csc(self):
spe = sp.sparse.csc_matrix(sp.sparse.eye(5, 3))
a = as_sparse_variable(spe)
......@@ -491,7 +488,7 @@ class TestSparseInferShape(utt.InferShapeTester):
[x + y],
[
sp.sparse.csr_matrix(random_lil((10, 40), config.floatX, 3)),
np.random.randn(10, 40).astype(config.floatX),
np.random.standard_normal((10, 40)).astype(config.floatX),
],
(AddSD, sparse.opt.AddSD_ccode),
)
......@@ -517,7 +514,7 @@ class TestSparseInferShape(utt.InferShapeTester):
[x * y],
[
sp.sparse.csr_matrix(random_lil((10, 40), config.floatX, 3)),
np.random.randn(10, 40).astype(config.floatX),
np.random.standard_normal((10, 40)).astype(config.floatX),
],
MulSD,
excluding=["local_mul_s_d"],
......@@ -621,7 +618,7 @@ class TestSparseInferShape(utt.InferShapeTester):
self._compile_and_check(
[x],
[csc_from_dense(x)],
[np.random.randn(10, 40).astype(config.floatX)],
[np.random.standard_normal((10, 40)).astype(config.floatX)],
csc_from_dense.__class__,
)
......@@ -636,8 +633,8 @@ class TestSparseInferShape(utt.InferShapeTester):
[out],
[
np.zeros((40, 10), dtype=config.floatX),
np.random.randn(12, 10).astype(config.floatX),
np.random.randint(low=0, high=40, size=(12,)),
np.random.standard_normal((12, 10)).astype(config.floatX),
np.random.default_rng().integers(low=0, high=40, size=(12,)),
],
ConstructSparseFromList,
)
......@@ -670,8 +667,8 @@ class TestConstructSparseFromList:
assert isinstance(g.owner.op, ConstructSparseFromList)
# Test the sparse grad
valm = np.random.rand(5, 4).astype(config.floatX)
valv = np.random.randint(0, 5, 10)
valm = np.random.random((5, 4)).astype(config.floatX)
valv = np.random.default_rng().integers(0, 5, 10)
m = matrix()
shared_v = aesara.shared(valv)
......@@ -884,13 +881,11 @@ class TestAddMul:
class TestComparison:
def setup_method(self):
utt.seed_rng()
# took from tensor basic_test.py
def _rand_ranged(self, min, max, shape):
return np.asarray(
np.random.rand(*shape) * (max - min) + min, dtype=config.floatX
np.random.random(shape) * (max - min) + min, dtype=config.floatX
)
tests = [
......@@ -1014,12 +1009,9 @@ class TestComparison:
class TestConversion:
def setup_method(self):
utt.seed_rng()
@pytest.mark.skip
def test_basic(self):
a = aet.as_tensor_variable(np.random.rand(5))
a = aet.as_tensor_variable(np.random.random((5)))
s = csc_from_dense(a)
val = eval_outputs([s])
assert str(val.dtype) == "float64"
......@@ -1027,7 +1019,7 @@ class TestConversion:
@pytest.mark.skip
def test_basic_1(self):
a = aet.as_tensor_variable(np.random.rand(5))
a = aet.as_tensor_variable(np.random.random((5)))
s = csr_from_dense(a)
val = eval_outputs([s])
assert str(val.dtype) == "float64"
......@@ -1078,9 +1070,6 @@ class TestConversion:
class TestCsmProperties:
def setup_method(self):
utt.seed_rng()
def test_csm_properties_grad(self):
sp_types = {"csc": sp.sparse.csc_matrix, "csr": sp.sparse.csr_matrix}
......@@ -1123,9 +1112,6 @@ class TestCsmProperties:
class TestCsm:
def setup_method(self):
utt.seed_rng()
def test_csm_grad(self):
sp_types = {"csc": sp.sparse.csc_matrix, "csr": sp.sparse.csr_matrix}
......@@ -1223,9 +1209,6 @@ class TestCsm:
class TestStructuredDot:
def setup_method(self):
utt.seed_rng()
def test_structureddot_csc_grad(self):
# shortcut: testing csc in float32, testing csr in float64
......@@ -1233,7 +1216,7 @@ class TestStructuredDot:
# allocate a random sparse matrix
spmat = sp.sparse.csc_matrix(random_lil((4, 3), "float32", 3))
mat = np.asarray(np.random.randn(3, 2), "float32")
mat = np.asarray(np.random.standard_normal((3, 2)), "float32")
verify_grad_sparse(structured_dot, [spmat, mat], structured=True)
......@@ -1249,7 +1232,7 @@ class TestStructuredDot:
# allocate a random sparse matrix
spmat = sp.sparse.csr_matrix(random_lil((4, 3), "float64", 3))
mat = np.asarray(np.random.randn(3, 2), "float64")
mat = np.asarray(np.random.standard_normal((3, 2)), "float64")
verify_grad_sparse(structured_dot, [spmat, mat], structured=True)
......@@ -1289,7 +1272,9 @@ class TestStructuredDot:
# The lil makes an intc on my computer when sparse_dtype
# is int32.
spmat.dtype = np.dtype(sparse_dtype)
mat = np.asarray(np.random.randn(N, K) * 9, dtype=dense_dtype)
mat = np.asarray(
np.random.standard_normal((N, K)) * 9, dtype=dense_dtype
)
# print 'DTYPES', sparse_dtype, dense_dtype
# print 'sym types', a.type, b.type
# print 'dtype strings', spmat.dtype, mat.dtype
......@@ -1316,9 +1301,9 @@ class TestStructuredDot:
spmat = sp.sparse.lil_matrix((4, 6), dtype="int64")
for i in range(5):
# set non-zeros in random locations (row x, col y)
x = np.floor(np.random.rand() * spmat.shape[0])
y = np.floor(np.random.rand() * spmat.shape[1])
spmat[x, y] = np.random.rand() * 10
x = np.floor(np.random.random() * spmat.shape[0])
y = np.floor(np.random.random() * spmat.shape[1])
spmat[x, y] = np.random.random() * 10
spmat = sp.sparse.csc_matrix(spmat)
images = TensorType(dtype="float32", broadcastable=[False, False])("images")
......@@ -1392,7 +1377,7 @@ class TestStructuredDot:
(400, 3000, 200, 6000),
]:
spmat = sp.sparse.csc_matrix(random_lil((M, N), sparse_dtype, nnz))
mat = np.asarray(np.random.randn(N, K), dense_dtype)
mat = np.asarray(np.random.standard_normal((N, K)), dense_dtype)
aesara_times = []
scipy_times = []
for i in range(5):
......@@ -1440,7 +1425,7 @@ class TestStructuredDot:
(400, 3000, 200, 6000),
]:
spmat = sp.sparse.csr_matrix(random_lil((M, N), sparse_dtype, nnz))
mat = np.asarray(np.random.randn(N, K), dense_dtype)
mat = np.asarray(np.random.standard_normal((N, K)), dense_dtype)
t0 = time.time()
aesara_result = f(spmat, mat)
t1 = time.time()
......@@ -1469,7 +1454,6 @@ class TestDots(utt.InferShapeTester):
super().setup_method()
x_size = (10, 100)
y_size = (100, 1000)
utt.seed_rng()
self.x_csr = sp.sparse.csr_matrix(
np.random.binomial(1, 0.5, x_size), dtype=aesara.config.floatX
......@@ -1604,7 +1588,9 @@ class TestDots(utt.InferShapeTester):
f = aesara.function(inputs=[I, C], outputs=y)
i = np.asarray([[4, 3, 7, 7], [2, 8, 4, 5]], dtype=intX)
a = np.asarray(np.random.randint(0, 100, (size, size)), dtype=intX)
a = np.asarray(
np.random.default_rng().integers(0, 100, (size, size)), dtype=intX
)
f(i, a)
def test_csr_dense_grad(self):
......@@ -1614,7 +1600,7 @@ class TestDots(utt.InferShapeTester):
# allocate a random sparse matrix
spmat = sp.sparse.csr_matrix(random_lil((4, 3), "float64", 3))
mat = np.asarray(np.random.randn(2, 4), "float64")
mat = np.asarray(np.random.standard_normal((2, 4)), "float64")
def buildgraph_T(mat):
return Dot()(mat, spmat)
......@@ -1632,7 +1618,7 @@ class TestUsmm:
y_size = (100, 200)
z_size = (x_size[0], y_size[1])
self.rng = np.random.RandomState(seed=utt.fetch_seed())
self.rng = np.random.default_rng(seed=utt.fetch_seed())
self.x = np.asarray(
self.rng.binomial(1, 0.5, x_size), dtype=aesara.config.floatX
)
......@@ -2305,7 +2291,7 @@ class TestRemove0(utt.InferShapeTester):
class TestGetItem:
def setup_method(self):
self.rng = np.random.RandomState(utt.fetch_seed())
self.rng = np.random.default_rng(utt.fetch_seed())
def test_GetItemList(self):
......@@ -2759,7 +2745,8 @@ class TestAddSSData(utt.InferShapeTester):
variable = getattr(aesara.sparse, format + "_matrix")
rand = np.array(
np.random.randint(1, 4, size=(3, 4)) - 1, dtype=aesara.config.floatX
np.random.default_rng(utt.fetch_seed()).integers(1, 4, size=(3, 4)) - 1,
dtype=aesara.config.floatX,
)
constant = as_sparse_format(rand, format)
......@@ -2833,7 +2820,6 @@ def elemwise_checker(
else:
self.gap_grad = gap
# Ensure the test's name is correct.
utt.seed_rng()
assert eval(self.__class__.__name__) is self.__class__
def test_op(self):
......@@ -3143,16 +3129,13 @@ ConjTester = elemwise_checker(sparse.conj, np.conj, grad_test=False)
class TestMulSV:
def setup_method(self):
utt.seed_rng()
def test_mul_s_v_grad(self):
sp_types = {"csc": sp.sparse.csc_matrix, "csr": sp.sparse.csr_matrix}
for format in ["csr", "csc"]:
for dtype in ["float32", "float64"]:
spmat = sp_types[format](random_lil((4, 3), dtype, 3))
mat = np.asarray(np.random.rand(3), dtype=dtype)
mat = np.asarray(np.random.random((3)), dtype=dtype)
verify_grad_sparse(mul_s_v, [spmat, mat], structured=True)
......@@ -3166,7 +3149,7 @@ class TestMulSV:
f = aesara.function([x, y], mul_s_v(x, y))
spmat = sp_types[format](random_lil((4, 3), dtype, 3))
mat = np.asarray(np.random.rand(3), dtype=dtype)
mat = np.asarray(np.random.random((3)), dtype=dtype)
out = f(spmat, mat)
......@@ -3174,16 +3157,13 @@ class TestMulSV:
class TestStructuredAddSV:
def setup_method(self):
utt.seed_rng()
def test_structured_add_s_v_grad(self):
sp_types = {"csc": sp.sparse.csc_matrix, "csr": sp.sparse.csr_matrix}
for format in ["csr", "csc"]:
for dtype in ["float32", "float64"]:
spmat = sp_types[format](random_lil((4, 3), dtype, 3))
mat = np.asarray(np.random.rand(3), dtype=dtype)
mat = np.asarray(np.random.random((3)), dtype=dtype)
verify_grad_sparse(structured_add_s_v, [spmat, mat], structured=True)
......@@ -3199,7 +3179,7 @@ class TestStructuredAddSV:
spmat = sp_types[format](random_lil((4, 3), dtype, 3))
spones = spmat.copy()
spones.data = np.ones_like(spones.data)
mat = np.asarray(np.random.rand(3), dtype=dtype)
mat = np.asarray(np.random.random((3)), dtype=dtype)
out = f(spmat, mat)
......@@ -3285,9 +3265,18 @@ class TestSamplingDot(utt.InferShapeTester):
x.append(sparse.csr_matrix())
# unsquare shape
a = [
np.array(np.random.randint(1, 6, size=(4, 3)) - 1, dtype=aesara.config.floatX),
np.array(np.random.randint(1, 6, size=(5, 3)) - 1, dtype=aesara.config.floatX),
np.array(np.random.randint(1, 3, size=(4, 5)) - 1, dtype=aesara.config.floatX),
np.array(
np.random.default_rng().integers(1, 6, size=(4, 3)) - 1,
dtype=aesara.config.floatX,
),
np.array(
np.random.default_rng().integers(1, 6, size=(5, 3)) - 1,
dtype=aesara.config.floatX,
),
np.array(
np.random.default_rng().integers(1, 3, size=(4, 5)) - 1,
dtype=aesara.config.floatX,
),
]
a[2] = sp.sparse.csr_matrix(a[2])
......
tests/tensor/test_sort.py
浏览文件 @ 39c11472
......@@ -34,9 +34,10 @@ _all_dtypes = integer_dtypes + float_dtypes
def gen_unique_vector(size, dtype):
rng = np.random.default_rng(utt.fetch_seed())
# generate a randomized vector with unique elements
retval = np.arange(size) * 3.0 + np.random.uniform(-1.0, 1.0)
return (retval[np.random.permutation(size)] - size * 1.5).astype(dtype)
retval = np.arange(size) * 3.0 + rng.uniform(-1.0, 1.0)
return (retval[rng.permutation(size)] - size * 1.5).astype(dtype)
class TestSort:
......@@ -97,81 +98,85 @@ class TestSort:
utt.assert_allclose(gv, gt)
def test_grad_vector(self):
data = np.random.random((10)).astype(aesara.config.floatX)
data = self.rng.random((10)).astype(aesara.config.floatX)
utt.verify_grad(sort, [data])
def test_grad_none_axis(self):
data = np.random.random((10)).astype(aesara.config.floatX)
data = self.rng.random((10)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, None), [data])
utt.verify_grad(lambda x: sort(x, 0), [data])
data = np.random.random((2, 3)).astype(aesara.config.floatX)
data = self.rng.random((2, 3)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, None), [data])
data = np.random.random((2, 3, 4)).astype(aesara.config.floatX)
data = self.rng.random((2, 3, 4)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, None), [data])
def test_grad_negative_axis_2d(self):
data = np.random.random((2, 3)).astype(aesara.config.floatX)
data = self.rng.random((2, 3)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, -1), [data])
data = np.random.random((2, 3)).astype(aesara.config.floatX)
data = self.rng.random((2, 3)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, -2), [data])
def test_grad_negative_axis_3d(self):
data = np.random.random((2, 3, 4)).astype(aesara.config.floatX)
data = self.rng.random((2, 3, 4)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, -1), [data])
data = np.random.random((2, 3, 4)).astype(aesara.config.floatX)
data = self.rng.random((2, 3, 4)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, -2), [data])
data = np.random.random((2, 3, 4)).astype(aesara.config.floatX)
data = self.rng.random((2, 3, 4)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, -3), [data])
def test_grad_negative_axis_4d(self):
data = np.random.random((2, 3, 4, 2)).astype(aesara.config.floatX)
data = self.rng.random((2, 3, 4, 2)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, -1), [data])
data = np.random.random((2, 3, 4, 2)).astype(aesara.config.floatX)
data = self.rng.random((2, 3, 4, 2)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, -2), [data])
data = np.random.random((2, 3, 4, 2)).astype(aesara.config.floatX)
data = self.rng.random((2, 3, 4, 2)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, -3), [data])
data = np.random.random((2, 3, 4, 2)).astype(aesara.config.floatX)
data = self.rng.random((2, 3, 4, 2)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, -4), [data])
def test_grad_nonnegative_axis_2d(self):
data = np.random.random((2, 3)).astype(aesara.config.floatX)
data = self.rng.random((2, 3)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, 0), [data])
data = np.random.random((2, 3)).astype(aesara.config.floatX)
data = self.rng.random((2, 3)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, 1), [data])
def test_grad_nonnegative_axis_3d(self):
data = np.random.random((2, 3, 4)).astype(aesara.config.floatX)
data = self.rng.random((2, 3, 4)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, 0), [data])
data = np.random.random((2, 3, 4)).astype(aesara.config.floatX)
data = self.rng.random((2, 3, 4)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, 1), [data])
data = np.random.random((2, 3, 4)).astype(aesara.config.floatX)
data = self.rng.random((2, 3, 4)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, 2), [data])
def test_grad_nonnegative_axis_4d(self):
data = np.random.random((2, 3, 4, 2)).astype(aesara.config.floatX)
data = self.rng.random((2, 3, 4, 2)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, 0), [data])
data = np.random.random((2, 3, 4, 2)).astype(aesara.config.floatX)
data = self.rng.random((2, 3, 4, 2)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, 1), [data])
data = np.random.random((2, 3, 4, 2)).astype(aesara.config.floatX)
data = self.rng.random((2, 3, 4, 2)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, 2), [data])
data = np.random.random((2, 3, 4, 2)).astype(aesara.config.floatX)
data = self.rng.random((2, 3, 4, 2)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: sort(x, 3), [data])
class TestSortInferShape(utt.InferShapeTester):
def setup_method(self):
self.rng = np.random.default_rng(seed=utt.fetch_seed())
super().setup_method()
def test_sort(self):
x = matrix()
self._compile_and_check(
[x],
[sort(x)],
[np.random.randn(10, 40).astype(aesara.config.floatX)],
[self.rng.standard_normal(size=(10, 40)).astype(aesara.config.floatX)],
SortOp,
)
self._compile_and_check(
[x],
[sort(x, axis=None)],
[np.random.randn(10, 40).astype(aesara.config.floatX)],
[self.rng.standard_normal(size=(10, 40)).astype(aesara.config.floatX)],
SortOp,
)
......@@ -238,14 +243,15 @@ def test_argsort():
def test_argsort_grad():
rng = np.random.default_rng(seed=utt.fetch_seed())
# Testing grad of argsort
data = np.random.random((2, 3)).astype(aesara.config.floatX)
data = rng.random((2, 3)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: argsort(x, axis=-1), [data])
data = np.random.random((2, 3, 4, 5)).astype(aesara.config.floatX)
data = rng.random((2, 3, 4, 5)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: argsort(x, axis=-3), [data])
data = np.random.random((2, 3, 3)).astype(aesara.config.floatX)
data = rng.random((2, 3, 3)).astype(aesara.config.floatX)
utt.verify_grad(lambda x: argsort(x, axis=2), [data])
......@@ -434,10 +440,9 @@ class TestTopK:
assert any(
[isinstance(n.op, self.op_class) for n in fn.maker.fgraph.apply_nodes]
)
xval = np.repeat(
np.random.uniform(-100.0, 100.0, size=size // 2).astype(dtype), 2
)
xval = xval[np.random.permutation(size)]
rng = np.random.default_rng(utt.fetch_seed())
xval = np.repeat(rng.uniform(-100.0, 100.0, size=size // 2).astype(dtype), 2)
xval = xval[rng.permutation(size)]
yval = fn(xval)
idx = slice(-k, None) if k > 0 else slice(-k)
goal = np.argsort(xval)[idx].astype("int32")
......
tests/typed_list/test_basic.py
浏览文件 @ 39c11472
......@@ -27,7 +27,6 @@ from aesara.typed_list.basic import (
make_list,
)
from aesara.typed_list.type import TypedListType
from tests import unittest_tools as utt
def rand_ranged_matrix(minimum, maximum, shape):
......@@ -55,9 +54,6 @@ def random_lil(shape, dtype, nnz):
class TestGetItem:
def setup_method(self):
utt.seed_rng()
def test_sanity_check_slice(self):
mySymbolicMatricesList = TypedListType(
......
tests/typed_list/test_opt.py
浏览文件 @ 39c11472
......@@ -7,7 +7,7 @@ from aesara.compile.io import In
from aesara.tensor.type import TensorType, matrix, scalar
from aesara.typed_list.basic import Append, Extend, Insert, Remove, Reverse
from aesara.typed_list.type import TypedListType
from tests.tensor.utils import rand_ranged
from tests.tensor.utils import random_ranged
class TestInplace:
......@@ -26,9 +26,9 @@ class TestInplace:
)
assert f.maker.fgraph.toposort()[0].op.inplace
x = rand_ranged(-1000, 1000, [100, 101])
x = random_ranged(-1000, 1000, [100, 101])
y = rand_ranged(-1000, 1000, [100, 101])
y = random_ranged(-1000, 1000, [100, 101])
assert np.array_equal(f([x, y]), [y, x])
......@@ -50,9 +50,9 @@ class TestInplace:
)
assert f.maker.fgraph.toposort()[0].op.inplace
x = rand_ranged(-1000, 1000, [100, 101])
x = random_ranged(-1000, 1000, [100, 101])
y = rand_ranged(-1000, 1000, [100, 101])
y = random_ranged(-1000, 1000, [100, 101])
assert np.array_equal(f([x], y), [x, y])
......@@ -77,9 +77,9 @@ class TestInplace:
)
assert f.maker.fgraph.toposort()[0].op.inplace
x = rand_ranged(-1000, 1000, [100, 101])
x = random_ranged(-1000, 1000, [100, 101])
y = rand_ranged(-1000, 1000, [100, 101])
y = random_ranged(-1000, 1000, [100, 101])
assert np.array_equal(f([x], [y]), [x, y])
......@@ -105,9 +105,9 @@ class TestInplace:
)
assert f.maker.fgraph.toposort()[0].op.inplace
x = rand_ranged(-1000, 1000, [100, 101])
x = random_ranged(-1000, 1000, [100, 101])
y = rand_ranged(-1000, 1000, [100, 101])
y = random_ranged(-1000, 1000, [100, 101])
assert np.array_equal(f([x], np.asarray(1, dtype="int64"), y), [x, y])
......@@ -129,9 +129,9 @@ class TestInplace:
)
assert f.maker.fgraph.toposort()[0].op.inplace
x = rand_ranged(-1000, 1000, [100, 101])
x = random_ranged(-1000, 1000, [100, 101])
y = rand_ranged(-1000, 1000, [100, 101])
y = random_ranged(-1000, 1000, [100, 101])
assert np.array_equal(f([x, y], y), [x])
......
tests/typed_list/test_type.py
浏览文件 @ 39c11472
......@@ -5,14 +5,10 @@ import aesara
from aesara.tensor.type import TensorType
from aesara.typed_list.basic import TypedListVariable
from aesara.typed_list.type import TypedListType
from tests import unittest_tools as utt
from tests.tensor.utils import rand_ranged
from tests.tensor.utils import random_ranged
class TestTypedListType:
def setup_method(self):
utt.seed_rng()
def test_wrong_input_on_creation(self):
# Typed list type should raises an
# error if the argument passed for
......@@ -63,7 +59,7 @@ class TestTypedListType:
myType = TypedListType(TensorType(aesara.config.floatX, (False, False)))
x = rand_ranged(-1000, 1000, [100, 100])
x = random_ranged(-1000, 1000, [100, 100])
assert np.array_equal(myType.filter([x]), [x])
......@@ -81,7 +77,7 @@ class TestTypedListType:
def test_load_alot(self):
myType = TypedListType(TensorType(aesara.config.floatX, (False, False)))
x = rand_ranged(-1000, 1000, [10, 10])
x = random_ranged(-1000, 1000, [10, 10])
testList = []
for i in range(10000):
testList.append(x)
......@@ -95,7 +91,7 @@ class TestTypedListType:
myType = TypedListType(myNestedType)
x = rand_ranged(-1000, 1000, [100, 100])
x = random_ranged(-1000, 1000, [100, 100])
assert np.array_equal(myType.filter([[x]]), [[x]])
......
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