def test_cloning_available(self):
gop = generator(integers())
res = gop ** 2
shared = theano.shared(floatX(10))
res1 = theano.clone(res, {gop: shared})
f = theano.function([], res1)
assert f() == np.float32(100)
def test_cloning_available(self):
gop = generator(integers())
res = gop ** 2
shared = theano.shared(floatX(10))
res1 = theano.clone(res, {gop: shared})
f = theano.function([], res1)
assert f() == np.float32(100)
def test_density_scaling_with_genarator(self):
# We have different size generators
def true_dens():
g = gen1()
for i, point in enumerate(g):
yield stats.norm.logpdf(point).sum() * 10
t = true_dens()
# We have same size models
with pm.Model() as model1:
Normal("n", observed=gen1(), total_size=100)
p1 = aesara.function([], model1.logpt)
with pm.Model() as model2:
gen_var = generator(gen2())
Normal("n", observed=gen_var, total_size=100)
p2 = aesara.function([], model2.logpt)
for i in range(10):
_1, _2, _t = p1(), p2(), next(t)
decimals = select_by_precision(float64=7, float32=2)
np.testing.assert_almost_equal(
_1, _t, decimal=decimals) # Value O(-50,000)
np.testing.assert_almost_equal(_1, _2)
def test_gen_cloning_with_shape_change(self, datagen):
gen = generator(datagen)
gen_r = tt_rng().normal(size=gen.shape).T
X = gen.dot(gen_r)
res, _ = theano.scan(lambda x: x.sum(), X, n_steps=X.shape[0])
assert res.eval().shape == (50,)
shared = theano.shared(datagen.data.astype(gen.dtype))
res2 = theano.clone(res, {gen: shared**2})
assert res2.eval().shape == (1000,)
def test_gen_cloning_with_shape_change(self, datagen):
gen = generator(datagen)
gen_r = tt_rng().normal(size=gen.shape).T
X = gen.dot(gen_r)
res, _ = theano.scan(lambda x: x.sum(), X, n_steps=X.shape[0])
assert res.eval().shape == (50,)
shared = theano.shared(datagen.data.astype(gen.dtype))
res2 = theano.clone(res, {gen: shared**2})
assert res2.eval().shape == (1000,)
def test_default_value(self):
def gen():
for i in range(2):
yield floatX(np.ones((10, 10)) * i)
gop = generator(gen(), np.ones((10, 10)) * 10)
f = theano.function([], gop)
np.testing.assert_equal(np.ones((10, 10)) * 0, f())
np.testing.assert_equal(np.ones((10, 10)) * 1, f())
np.testing.assert_equal(np.ones((10, 10)) * 10, f())
with pytest.raises(ValueError):
gop.set_default(1)
def test_default_value(self):
def gen():
for i in range(2):
yield floatX(np.ones((10, 10)) * i)
gop = generator(gen(), np.ones((10, 10)) * 10)
f = theano.function([], gop)
np.testing.assert_equal(np.ones((10, 10)) * 0, f())
np.testing.assert_equal(np.ones((10, 10)) * 1, f())
np.testing.assert_equal(np.ones((10, 10)) * 10, f())
with pytest.raises(ValueError):
gop.set_default(1)
def test_set_gen_and_exc(self):
def gen():
for i in range(2):
yield floatX(np.ones((10, 10)) * i)
gop = generator(gen())
f = theano.function([], gop)
np.testing.assert_equal(np.ones((10, 10)) * 0, f())
np.testing.assert_equal(np.ones((10, 10)) * 1, f())
with pytest.raises(StopIteration):
f()
gop.set_gen(gen())
np.testing.assert_equal(np.ones((10, 10)) * 0, f())
np.testing.assert_equal(np.ones((10, 10)) * 1, f())
def test_set_gen_and_exc(self):
def gen():
for i in range(2):
yield floatX(np.ones((10, 10)) * i)
gop = generator(gen())
f = theano.function([], gop)
np.testing.assert_equal(np.ones((10, 10)) * 0, f())
np.testing.assert_equal(np.ones((10, 10)) * 1, f())
with pytest.raises(StopIteration):
f()
gop.set_gen(gen())
np.testing.assert_equal(np.ones((10, 10)) * 0, f())
np.testing.assert_equal(np.ones((10, 10)) * 1, f())
def test_gradient_with_scaling(self):
with pm.Model() as model1:
genvar = generator(gen1())
m = Normal('m')
Normal('n', observed=genvar, total_size=1000)
grad1 = theano.function([m], tt.grad(model1.logpt, m))
with pm.Model() as model2:
m = Normal('m')
shavar = theano.shared(np.ones((1000, 100)))
Normal('n', observed=shavar)
grad2 = theano.function([m], tt.grad(model2.logpt, m))
for i in range(10):
shavar.set_value(np.ones((100, 100)) * i)
g1 = grad1(1)
g2 = grad2(1)
np.testing.assert_almost_equal(g1, g2)
def test_gradient_with_scaling(self):
with pm.Model() as model1:
genvar = generator(gen1())
m = Normal('m')
Normal('n', observed=genvar, total_size=1000)
grad1 = theano.function([m], tt.grad(model1.logpt, m))
with pm.Model() as model2:
m = Normal('m')
shavar = theano.shared(np.ones((1000, 100)))
Normal('n', observed=shavar)
grad2 = theano.function([m], tt.grad(model2.logpt, m))
for i in range(10):
shavar.set_value(np.ones((100, 100)) * i)
g1 = grad1(1)
g2 = grad2(1)
np.testing.assert_almost_equal(g1, g2)
def test_gradient_with_scaling(self):
with pm.Model() as model1:
genvar = generator(gen1())
m = Normal("m")
Normal("n", observed=genvar, total_size=1000)
grad1 = aesara.function([m.tag.value_var],
at.grad(model1.logpt, m.tag.value_var))
with pm.Model() as model2:
m = Normal("m")
shavar = aesara.shared(np.ones((1000, 100)))
Normal("n", observed=shavar)
grad2 = aesara.function([m.tag.value_var],
at.grad(model2.logpt, m.tag.value_var))
for i in range(10):
shavar.set_value(np.ones((100, 100)) * i)
g1 = grad1(1)
g2 = grad2(1)
np.testing.assert_almost_equal(g1, g2)
def test_density_scaling_with_genarator(self):
# We have different size generators
def true_dens():
g = gen1()
for i, point in enumerate(g):
yield stats.norm.logpdf(point).sum() * 10
t = true_dens()
# We have same size models
with pm.Model() as model1:
Normal('n', observed=gen1(), total_size=100)
p1 = theano.function([], model1.logpt)
with pm.Model() as model2:
gen_var = generator(gen2())
Normal('n', observed=gen_var, total_size=100)
p2 = theano.function([], model2.logpt)
for i in range(10):
_1, _2, _t = p1(), p2(), next(t)
decimals = select_by_precision(float64=7, float32=2)
np.testing.assert_almost_equal(_1, _t, decimal=decimals) # Value O(-50,000)
np.testing.assert_almost_equal(_1, _2)
def test_pickling(self, datagen):
gen = generator(datagen)
pickle.loads(pickle.dumps(gen))
bad_gen = generator(integers())
with pytest.raises(Exception):
pickle.dumps(bad_gen)
def test_pickling(self, datagen):
gen = generator(datagen)
pickle.loads(pickle.dumps(gen))
bad_gen = generator(integers())
with pytest.raises(Exception):
pickle.dumps(bad_gen)