def test_where(self):
# cases to test
# no x and y
a = ht.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], split=None)
cond = a > 3
wh = ht.where(cond)
self.assertEqual(wh.gshape, (6, 2))
self.assertEqual(wh.dtype, ht.int64)
self.assertEqual(wh.split, None)
# split
a = ht.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], split=1)
cond = a > 3
wh = ht.where(cond)
self.assertEqual(wh.gshape, (6, 2))
self.assertEqual(wh.dtype, ht.int64)
self.assertEqual(wh.split, 0)
# not split cond
a = ht.array([[0.0, 1.0, 2.0], [0.0, 2.0, 4.0], [0.0, 3.0, 6.0]],
split=None)
res = ht.array([[0.0, 1.0, 2.0], [0.0, 2.0, -1.0], [0.0, 3.0, -1.0]],
split=None)
wh = ht.where(a < 4.0, a, -1)
self.assertTrue(
ht.equal(a[ht.nonzero(a < 4)],
ht.array([0.0, 1.0, 2.0, 0.0, 2.0, 0.0, 3.0])))
self.assertTrue(ht.equal(wh, res))
self.assertEqual(wh.gshape, (3, 3))
self.assertEqual(wh.dtype, ht.float64)
# split cond
a = ht.array([[0.0, 1.0, 2.0], [0.0, 2.0, 4.0], [0.0, 3.0, 6.0]],
split=0)
res = ht.array([[0.0, 1.0, 2.0], [0.0, 2.0, -1.0], [0.0, 3.0, -1.0]],
split=0)
wh = ht.where(a < 4.0, a, -1)
self.assertTrue(ht.all(wh[ht.nonzero(a >= 4)] == -1))
self.assertTrue(ht.equal(wh, res))
self.assertEqual(wh.gshape, (3, 3))
self.assertEqual(wh.dtype, ht.float64)
self.assertEqual(wh.split, 0)
a = ht.array([[0.0, 1.0, 2.0], [0.0, 2.0, 4.0], [0.0, 3.0, 6.0]],
split=1)
res = ht.array([[0.0, 1.0, 2.0], [0.0, 2.0, -1.0], [0.0, 3.0, -1.0]],
split=1)
wh = ht.where(a < 4.0, a, -1.0)
self.assertTrue(ht.equal(wh, res))
self.assertEqual(wh.gshape, (3, 3))
self.assertEqual(wh.dtype, ht.float)
self.assertEqual(wh.split, 1)
with self.assertRaises(TypeError):
ht.where(cond, a)
with self.assertRaises(NotImplementedError):
ht.where(cond, ht.ones((3, 3), split=0), ht.ones((3, 3), split=1))
def test_nonzero(self):
# cases to test:
# not split
a = ht.array([[1, 2, 3], [4, 5, 2], [7, 8, 9]], split=None)
cond = a > 3
nz = ht.nonzero(cond)
self.assertEqual(nz.gshape, (5, 2))
self.assertEqual(nz.dtype, ht.int64)
self.assertEqual(nz.split, None)
# split
a = ht.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], split=1)
cond = a > 3
nz = cond.nonzero()
self.assertEqual(nz.gshape, (6, 2))
self.assertEqual(nz.dtype, ht.int64)
self.assertEqual(nz.split, 0)
a[nz] = 10.0
self.assertEqual(ht.all(a[nz] == 10), 1)
def test_all(self):
array_len = 9
# check all over all float elements of 1d tensor locally
ones_noaxis = ht.ones(array_len, device=ht_device)
x = (ones_noaxis == 1).all()
self.assertIsInstance(x, ht.DNDarray)
self.assertEqual(x.shape, (1, ))
self.assertEqual(x.lshape, (1, ))
self.assertEqual(x.dtype, ht.bool)
self.assertEqual(x._DNDarray__array.dtype, torch.bool)
self.assertEqual(x.split, None)
self.assertEqual(x._DNDarray__array, 1)
out_noaxis = ht.zeros((1, ), device=ht_device)
ht.all(ones_noaxis, out=out_noaxis)
self.assertEqual(out_noaxis._DNDarray__array, 1)
# check all over all float elements of split 1d tensor
ones_noaxis_split = ht.ones(array_len, split=0, device=ht_device)
floats_is_one = ones_noaxis_split.all()
self.assertIsInstance(floats_is_one, ht.DNDarray)
self.assertEqual(floats_is_one.shape, (1, ))
self.assertEqual(floats_is_one.lshape, (1, ))
self.assertEqual(floats_is_one.dtype, ht.bool)
self.assertEqual(floats_is_one._DNDarray__array.dtype, torch.bool)
self.assertEqual(floats_is_one.split, None)
self.assertEqual(floats_is_one._DNDarray__array, 1)
out_noaxis = ht.zeros((1, ), device=ht_device)
ht.all(ones_noaxis_split, out=out_noaxis)
self.assertEqual(out_noaxis._DNDarray__array, 1)
# check all over all integer elements of 1d tensor locally
ones_noaxis_int = ht.ones(array_len, device=ht_device).astype(ht.int)
int_is_one = ones_noaxis_int.all()
self.assertIsInstance(int_is_one, ht.DNDarray)
self.assertEqual(int_is_one.shape, (1, ))
self.assertEqual(int_is_one.lshape, (1, ))
self.assertEqual(int_is_one.dtype, ht.bool)
self.assertEqual(int_is_one._DNDarray__array.dtype, torch.bool)
self.assertEqual(int_is_one.split, None)
self.assertEqual(int_is_one._DNDarray__array, 1)
out_noaxis = ht.zeros((1, ), device=ht_device)
ht.all(ones_noaxis_int, out=out_noaxis)
self.assertEqual(out_noaxis._DNDarray__array, 1)
# check all over all integer elements of split 1d tensor
ones_noaxis_split_int = ht.ones(array_len, split=0,
device=ht_device).astype(ht.int)
split_int_is_one = ones_noaxis_split_int.all()
self.assertIsInstance(split_int_is_one, ht.DNDarray)
self.assertEqual(split_int_is_one.shape, (1, ))
self.assertEqual(split_int_is_one.lshape, (1, ))
self.assertEqual(split_int_is_one.dtype, ht.bool)
self.assertEqual(split_int_is_one._DNDarray__array.dtype, torch.bool)
self.assertEqual(split_int_is_one.split, None)
self.assertEqual(split_int_is_one._DNDarray__array, 1)
out_noaxis = ht.zeros((1, ), device=ht_device)
ht.all(ones_noaxis_split_int, out=out_noaxis)
self.assertEqual(out_noaxis._DNDarray__array, 1)
# check all over all float elements of 3d tensor locally
ones_noaxis_volume = ht.ones((3, 3, 3), device=ht_device)
volume_is_one = ones_noaxis_volume.all()
self.assertIsInstance(volume_is_one, ht.DNDarray)
self.assertEqual(volume_is_one.shape, (1, ))
self.assertEqual(volume_is_one.lshape, (1, ))
self.assertEqual(volume_is_one.dtype, ht.bool)
self.assertEqual(volume_is_one._DNDarray__array.dtype, torch.bool)
self.assertEqual(volume_is_one.split, None)
self.assertEqual(volume_is_one._DNDarray__array, 1)
out_noaxis = ht.zeros((1, ), device=ht_device)
ht.all(ones_noaxis_volume, out=out_noaxis)
self.assertEqual(out_noaxis._DNDarray__array, 1)
# check sequence is not all one
sequence = ht.arange(array_len, device=ht_device)
sequence_is_one = sequence.all()
self.assertIsInstance(sequence_is_one, ht.DNDarray)
self.assertEqual(sequence_is_one.shape, (1, ))
self.assertEqual(sequence_is_one.lshape, (1, ))
self.assertEqual(sequence_is_one.dtype, ht.bool)
self.assertEqual(sequence_is_one._DNDarray__array.dtype, torch.bool)
self.assertEqual(sequence_is_one.split, None)
self.assertEqual(sequence_is_one._DNDarray__array, 0)
out_noaxis = ht.zeros((1, ), device=ht_device)
ht.all(sequence, out=out_noaxis)
self.assertEqual(out_noaxis._DNDarray__array, 0)
# check all over all float elements of split 3d tensor
ones_noaxis_split_axis = ht.ones((3, 3, 3), split=0, device=ht_device)
float_volume_is_one = ones_noaxis_split_axis.all(axis=0)
self.assertIsInstance(float_volume_is_one, ht.DNDarray)
self.assertEqual(float_volume_is_one.shape, (3, 3))
self.assertEqual(float_volume_is_one.all(axis=1).dtype, ht.bool)
self.assertEqual(float_volume_is_one._DNDarray__array.dtype,
torch.bool)
self.assertEqual(float_volume_is_one.split, None)
out_noaxis = ht.zeros((3, 3), device=ht_device)
ht.all(ones_noaxis_split_axis, axis=0, out=out_noaxis)
# check all over all float elements of split 3d tensor with tuple axis
ones_noaxis_split_axis = ht.ones((3, 3, 3), split=0, device=ht_device)
float_volume_is_one = ones_noaxis_split_axis.all(axis=(0, 1))
self.assertIsInstance(float_volume_is_one, ht.DNDarray)
self.assertEqual(float_volume_is_one.shape, (3, ))
self.assertEqual(float_volume_is_one.all(axis=0).dtype, ht.bool)
self.assertEqual(float_volume_is_one._DNDarray__array.dtype,
torch.bool)
self.assertEqual(float_volume_is_one.split, None)
# check all over all float elements of split 5d tensor with negative axis
ones_noaxis_split_axis_neg = ht.zeros((1, 2, 3, 4, 5),
split=1,
device=ht_device)
float_5d_is_one = ones_noaxis_split_axis_neg.all(axis=-2)
self.assertIsInstance(float_5d_is_one, ht.DNDarray)
self.assertEqual(float_5d_is_one.shape, (1, 2, 3, 5))
self.assertEqual(float_5d_is_one.dtype, ht.bool)
self.assertEqual(float_5d_is_one._DNDarray__array.dtype, torch.bool)
self.assertEqual(float_5d_is_one.split, 1)
out_noaxis = ht.zeros((1, 2, 3, 5), device=ht_device)
ht.all(ones_noaxis_split_axis_neg, axis=-2, out=out_noaxis)
# exceptions
with self.assertRaises(ValueError):
ht.ones(array_len, device=ht_device).all(axis=1)
with self.assertRaises(ValueError):
ht.ones(array_len, device=ht_device).all(axis=-2)
with self.assertRaises(ValueError):
ht.ones((4, 4), device=ht_device).all(axis=0, out=out_noaxis)
with self.assertRaises(TypeError):
ht.ones(array_len, device=ht_device).all(axis="bad_axis_type")
def test_matmul(self):
with self.assertRaises(ValueError):
ht.matmul(ht.ones((25, 25)), ht.ones((42, 42)))
# cases to test:
n, m = 21, 31
j, k = m, 45
a_torch = torch.ones((n, m), device=self.device.torch_device)
a_torch[0] = torch.arange(1, m + 1, device=self.device.torch_device)
a_torch[:, -1] = torch.arange(1,
n + 1,
device=self.device.torch_device)
b_torch = torch.ones((j, k), device=self.device.torch_device)
b_torch[0] = torch.arange(1, k + 1, device=self.device.torch_device)
b_torch[:, 0] = torch.arange(1, j + 1, device=self.device.torch_device)
# splits None None
a = ht.ones((n, m), split=None)
b = ht.ones((j, k), split=None)
a[0] = ht.arange(1, m + 1)
a[:, -1] = ht.arange(1, n + 1)
b[0] = ht.arange(1, k + 1)
b[:, 0] = ht.arange(1, j + 1)
ret00 = ht.matmul(a, b)
self.assertEqual(ht.all(ret00 == ht.array(a_torch @ b_torch)), 1)
self.assertIsInstance(ret00, ht.DNDarray)
self.assertEqual(ret00.shape, (n, k))
self.assertEqual(ret00.dtype, ht.float)
self.assertEqual(ret00.split, None)
self.assertEqual(a.split, None)
self.assertEqual(b.split, None)
# splits None None
a = ht.ones((n, m), split=None)
b = ht.ones((j, k), split=None)
a[0] = ht.arange(1, m + 1)
a[:, -1] = ht.arange(1, n + 1)
b[0] = ht.arange(1, k + 1)
b[:, 0] = ht.arange(1, j + 1)
ret00 = ht.matmul(a, b, allow_resplit=True)
self.assertEqual(ht.all(ret00 == ht.array(a_torch @ b_torch)), 1)
self.assertIsInstance(ret00, ht.DNDarray)
self.assertEqual(ret00.shape, (n, k))
self.assertEqual(ret00.dtype, ht.float)
self.assertEqual(ret00.split, None)
self.assertEqual(a.split, 0)
self.assertEqual(b.split, None)
if a.comm.size > 1:
# splits 00
a = ht.ones((n, m), split=0, dtype=ht.float64)
b = ht.ones((j, k), split=0)
a[0] = ht.arange(1, m + 1)
a[:, -1] = ht.arange(1, n + 1)
b[0] = ht.arange(1, k + 1)
b[:, 0] = ht.arange(1, j + 1)
ret00 = a @ b
ret_comp00 = ht.array(a_torch @ b_torch, split=0)
self.assertTrue(ht.equal(ret00, ret_comp00))
self.assertIsInstance(ret00, ht.DNDarray)
self.assertEqual(ret00.shape, (n, k))
self.assertEqual(ret00.dtype, ht.float64)
self.assertEqual(ret00.split, 0)
# splits 00 (numpy)
a = ht.array(np.ones((n, m)), split=0)
b = ht.array(np.ones((j, k)), split=0)
a[0] = ht.arange(1, m + 1)
a[:, -1] = ht.arange(1, n + 1)
b[0] = ht.arange(1, k + 1)
b[:, 0] = ht.arange(1, j + 1)
ret00 = a @ b
ret_comp00 = ht.array(a_torch @ b_torch, split=0)
self.assertTrue(ht.equal(ret00, ret_comp00))
self.assertIsInstance(ret00, ht.DNDarray)
self.assertEqual(ret00.shape, (n, k))
self.assertEqual(ret00.dtype, ht.float64)
self.assertEqual(ret00.split, 0)
# splits 01
a = ht.ones((n, m), split=0)
b = ht.ones((j, k), split=1, dtype=ht.float64)
a[0] = ht.arange(1, m + 1)
a[:, -1] = ht.arange(1, n + 1)
b[0] = ht.arange(1, k + 1)
b[:, 0] = ht.arange(1, j + 1)
ret00 = ht.matmul(a, b)
ret_comp01 = ht.array(a_torch @ b_torch, split=0)
self.assertTrue(ht.equal(ret00, ret_comp01))
self.assertIsInstance(ret00, ht.DNDarray)
self.assertEqual(ret00.shape, (n, k))
self.assertEqual(ret00.dtype, ht.float64)
self.assertEqual(ret00.split, 0)
# splits 10
a = ht.ones((n, m), split=1)
b = ht.ones((j, k), split=0)
a[0] = ht.arange(1, m + 1)
a[:, -1] = ht.arange(1, n + 1)
b[0] = ht.arange(1, k + 1)
b[:, 0] = ht.arange(1, j + 1)
ret00 = ht.matmul(a, b)
ret_comp10 = ht.array(a_torch @ b_torch, split=1)
self.assertTrue(ht.equal(ret00, ret_comp10))
self.assertIsInstance(ret00, ht.DNDarray)
self.assertEqual(ret00.shape, (n, k))
self.assertEqual(ret00.dtype, ht.float)
self.assertEqual(ret00.split, 1)
# splits 11
a = ht.ones((n, m), split=1)
b = ht.ones((j, k), split=1)
a[0] = ht.arange(1, m + 1)
a[:, -1] = ht.arange(1, n + 1)
b[0] = ht.arange(1, k + 1)
b[:, 0] = ht.arange(1, j + 1)
ret00 = ht.matmul(a, b)
ret_comp11 = ht.array(a_torch @ b_torch, split=1)
self.assertTrue(ht.equal(ret00, ret_comp11))
self.assertIsInstance(ret00, ht.DNDarray)
self.assertEqual(ret00.shape, (n, k))
self.assertEqual(ret00.dtype, ht.float)
self.assertEqual(ret00.split, 1)
# splits 11 (torch)
a = ht.array(torch.ones((n, m), device=self.device.torch_device),
split=1)
b = ht.array(torch.ones((j, k), device=self.device.torch_device),
split=1)
a[0] = ht.arange(1, m + 1)
a[:, -1] = ht.arange(1, n + 1)
b[0] = ht.arange(1, k + 1)
b[:, 0] = ht.arange(1, j + 1)
ret00 = ht.matmul(a, b)
ret_comp11 = ht.array(a_torch @ b_torch, split=1)
self.assertTrue(ht.equal(ret00, ret_comp11))
self.assertIsInstance(ret00, ht.DNDarray)
self.assertEqual(ret00.shape, (n, k))
self.assertEqual(ret00.dtype, ht.float)
self.assertEqual(ret00.split, 1)
# splits 0 None
a = ht.ones((n, m), split=0)
b = ht.ones((j, k), split=None)
a[0] = ht.arange(1, m + 1)
a[:, -1] = ht.arange(1, n + 1)
b[0] = ht.arange(1, k + 1)
b[:, 0] = ht.arange(1, j + 1)
ret00 = ht.matmul(a, b)
ret_comp0 = ht.array(a_torch @ b_torch, split=0)
self.assertTrue(ht.equal(ret00, ret_comp0))
self.assertIsInstance(ret00, ht.DNDarray)
self.assertEqual(ret00.shape, (n, k))
self.assertEqual(ret00.dtype, ht.float)
self.assertEqual(ret00.split, 0)
# splits 1 None
a = ht.ones((n, m), split=1)
b = ht.ones((j, k), split=None)
a[0] = ht.arange(1, m + 1)
a[:, -1] = ht.arange(1, n + 1)
b[0] = ht.arange(1, k + 1)
b[:, 0] = ht.arange(1, j + 1)
ret00 = ht.matmul(a, b)
ret_comp1 = ht.array(a_torch @ b_torch, split=1)
self.assertTrue(ht.equal(ret00, ret_comp1))
self.assertIsInstance(ret00, ht.DNDarray)
self.assertEqual(ret00.shape, (n, k))
self.assertEqual(ret00.dtype, ht.float)
self.assertEqual(ret00.split, 1)
# splits None 0
a = ht.ones((n, m), split=None)
b = ht.ones((j, k), split=0)
a[0] = ht.arange(1, m + 1)
a[:, -1] = ht.arange(1, n + 1)
b[0] = ht.arange(1, k + 1)
b[:, 0] = ht.arange(1, j + 1)
ret00 = ht.matmul(a, b)
ret_comp = ht.array(a_torch @ b_torch, split=0)
self.assertTrue(ht.equal(ret00, ret_comp))
self.assertIsInstance(ret00, ht.DNDarray)
self.assertEqual(ret00.shape, (n, k))
self.assertEqual(ret00.dtype, ht.float)
self.assertEqual(ret00.split, 0)
# splits None 1
a = ht.ones((n, m), split=None)
b = ht.ones((j, k), split=1)
a[0] = ht.arange(1, m + 1)
a[:, -1] = ht.arange(1, n + 1)
b[0] = ht.arange(1, k + 1)
b[:, 0] = ht.arange(1, j + 1)
ret00 = ht.matmul(a, b)
ret_comp = ht.array(a_torch @ b_torch, split=1)
self.assertTrue(ht.equal(ret00, ret_comp))
self.assertIsInstance(ret00, ht.DNDarray)
self.assertEqual(ret00.shape, (n, k))
self.assertEqual(ret00.dtype, ht.float)
self.assertEqual(ret00.split, 1)
# vector matrix mult:
# a -> vector
a_torch = torch.ones((m), device=self.device.torch_device)
b_torch = torch.ones((j, k), device=self.device.torch_device)
b_torch[0] = torch.arange(1,
k + 1,
device=self.device.torch_device)
b_torch[:, 0] = torch.arange(1,
j + 1,
device=self.device.torch_device)
# splits None None
a = ht.ones((m), split=None)
b = ht.ones((j, k), split=None)
b[0] = ht.arange(1, k + 1)
b[:, 0] = ht.arange(1, j + 1)
ret00 = ht.matmul(a, b)
ret_comp = ht.array(a_torch @ b_torch, split=None)
self.assertTrue(ht.equal(ret00, ret_comp))
self.assertIsInstance(ret00, ht.DNDarray)
self.assertEqual(ret00.shape, (k, ))
self.assertEqual(ret00.dtype, ht.float)
self.assertEqual(ret00.split, None)
# splits None 0
a = ht.ones((m), split=None)
b = ht.ones((j, k), split=0)
b[0] = ht.arange(1, k + 1)
b[:, 0] = ht.arange(1, j + 1)
ret00 = ht.matmul(a, b)
ret_comp = ht.array(a_torch @ b_torch, split=None)
self.assertTrue(ht.equal(ret00, ret_comp))
self.assertIsInstance(ret00, ht.DNDarray)
self.assertEqual(ret00.shape, (k, ))
self.assertEqual(ret00.dtype, ht.float)
self.assertEqual(ret00.split, 0)
# splits None 1
a = ht.ones((m), split=None)
b = ht.ones((j, k), split=1)
b[0] = ht.arange(1, k + 1)
b[:, 0] = ht.arange(1, j + 1)
ret00 = ht.matmul(a, b)
ret_comp = ht.array(a_torch @ b_torch, split=0)
self.assertTrue(ht.equal(ret00, ret_comp))
self.assertIsInstance(ret00, ht.DNDarray)
self.assertEqual(ret00.shape, (k, ))
self.assertEqual(ret00.dtype, ht.float)
self.assertEqual(ret00.split, 0)
# splits 0 None
a = ht.ones((m), split=None)
b = ht.ones((j, k), split=0)
b[0] = ht.arange(1, k + 1)
b[:, 0] = ht.arange(1, j + 1)
ret00 = ht.matmul(a, b)
ret_comp = ht.array(a_torch @ b_torch, split=None)
self.assertTrue(ht.equal(ret00, ret_comp))
self.assertIsInstance(ret00, ht.DNDarray)
self.assertEqual(ret00.shape, (k, ))
self.assertEqual(ret00.dtype, ht.float)
self.assertEqual(ret00.split, 0)
# splits 0 0
a = ht.ones((m), split=0)
b = ht.ones((j, k), split=0)
b[0] = ht.arange(1, k + 1)
b[:, 0] = ht.arange(1, j + 1)
ret00 = ht.matmul(a, b)
ret_comp = ht.array(a_torch @ b_torch, split=None)
self.assertTrue(ht.equal(ret00, ret_comp))
self.assertIsInstance(ret00, ht.DNDarray)
self.assertEqual(ret00.shape, (k, ))
self.assertEqual(ret00.dtype, ht.float)
self.assertEqual(ret00.split, 0)
# splits 0 1
a = ht.ones((m), split=0)
b = ht.ones((j, k), split=1)
b[0] = ht.arange(1, k + 1)
b[:, 0] = ht.arange(1, j + 1)
ret00 = ht.matmul(a, b)
ret_comp = ht.array(a_torch @ b_torch, split=None)
self.assertTrue(ht.equal(ret00, ret_comp))
self.assertIsInstance(ret00, ht.DNDarray)
self.assertEqual(ret00.shape, (k, ))
self.assertEqual(ret00.dtype, ht.float)
self.assertEqual(ret00.split, 0)
# b -> vector
a_torch = torch.ones((n, m), device=self.device.torch_device)
a_torch[0] = torch.arange(1,
m + 1,
device=self.device.torch_device)
a_torch[:, -1] = torch.arange(1,
n + 1,
device=self.device.torch_device)
b_torch = torch.ones((j), device=self.device.torch_device)
# splits None None
a = ht.ones((n, m), split=None)
b = ht.ones((j), split=None)
a[0] = ht.arange(1, m + 1)
a[:, -1] = ht.arange(1, n + 1)
ret00 = ht.matmul(a, b)
ret_comp = ht.array(a_torch @ b_torch, split=None)
self.assertTrue(ht.equal(ret00, ret_comp))
self.assertIsInstance(ret00, ht.DNDarray)
self.assertEqual(ret00.shape, (n, ))
self.assertEqual(ret00.dtype, ht.float)
self.assertEqual(ret00.split, None)
# splits 0 None
a = ht.ones((n, m), split=0)
b = ht.ones((j), split=None)
a[0] = ht.arange(1, m + 1)
a[:, -1] = ht.arange(1, n + 1)
ret00 = ht.matmul(a, b)
ret_comp = ht.array((a_torch @ b_torch), split=None)
self.assertTrue(ht.equal(ret00, ret_comp))
self.assertIsInstance(ret00, ht.DNDarray)
self.assertEqual(ret00.shape, (n, ))
self.assertEqual(ret00.dtype, ht.float)
self.assertEqual(ret00.split, 0)
# splits 1 None
a = ht.ones((n, m), split=1)
b = ht.ones((j), split=None)
a[0] = ht.arange(1, m + 1)
a[:, -1] = ht.arange(1, n + 1)
ret00 = ht.matmul(a, b)
ret_comp = ht.array((a_torch @ b_torch), split=None)
self.assertTrue(ht.equal(ret00, ret_comp))
self.assertIsInstance(ret00, ht.DNDarray)
self.assertEqual(ret00.shape, (n, ))
self.assertEqual(ret00.dtype, ht.float)
self.assertEqual(ret00.split, 0)
# splits None 0
a = ht.ones((n, m), split=None)
b = ht.ones((j), split=0)
a[0] = ht.arange(1, m + 1)
a[:, -1] = ht.arange(1, n + 1)
ret00 = ht.matmul(a, b)
ret_comp = ht.array((a_torch @ b_torch), split=None)
self.assertTrue(ht.equal(ret00, ret_comp))
self.assertIsInstance(ret00, ht.DNDarray)
self.assertEqual(ret00.shape, (n, ))
self.assertEqual(ret00.dtype, ht.float)
self.assertEqual(ret00.split, 0)
# splits 0 0
a = ht.ones((n, m), split=0)
b = ht.ones((j), split=0)
a[0] = ht.arange(1, m + 1)
a[:, -1] = ht.arange(1, n + 1)
ret00 = ht.matmul(a, b)
ret_comp = ht.array((a_torch @ b_torch), split=None)
self.assertTrue(ht.equal(ret00, ret_comp))
self.assertIsInstance(ret00, ht.DNDarray)
self.assertEqual(ret00.shape, (n, ))
self.assertEqual(ret00.dtype, ht.float)
self.assertEqual(ret00.split, 0)
# splits 1 0
a = ht.ones((n, m), split=1)
b = ht.ones((j), split=0)
a[0] = ht.arange(1, m + 1)
a[:, -1] = ht.arange(1, n + 1)
ret00 = ht.matmul(a, b)
ret_comp = ht.array((a_torch @ b_torch), split=None)
self.assertTrue(ht.equal(ret00, ret_comp))
self.assertIsInstance(ret00, ht.DNDarray)
self.assertEqual(ret00.shape, (n, ))
self.assertEqual(ret00.dtype, ht.float)
self.assertEqual(ret00.split, 0)
with self.assertRaises(NotImplementedError):
a = ht.zeros((3, 3, 3), split=2)
b = a.copy()
a @ b
def __partial_fit(self,
X,
y,
classes=None,
_refit=False,
sample_weight=None):
"""
Actual implementation of Gaussian NB fitting. Adapted to HeAT from scikit-learn.
Parameters
----------
X : ht.tensor of shape (n_samples, n_features)
Training set, where n_samples is the number of samples and
n_features is the number of features.
y : ht.tensor of shape (n_samples,)
Labels for training set.
classes : ht.tensor of shape (n_classes,), optional (default=None)
List of all the classes that can possibly appear in the y vector.
Must be provided at the first call to partial_fit, can be omitted
in subsequent calls.
_refit : bool, optional (default=False)
If true, act as though this were the first time __partial_fit is called
(ie, throw away any past fitting and start over).
sample_weight : ht.tensor of shape (n_samples,), optional (default=None)
Weights applied to individual samples (1. for unweighted).
Returns
-------
self : object
"""
# TODO: sanitize X and y shape: sanitation/validation module, cf. #468
n_samples = X.shape[0]
if X.numdims != 2:
raise ValueError("expected X to be a 2-D tensor, is {}-D".format(
X.numdims))
if y.shape[0] != n_samples:
raise ValueError(
"y.shape[0] must match number of samples {}, is {}".format(
n_samples, y.shape[0]))
# TODO: sanitize sample_weight: sanitation/validation module, cf. #468
if sample_weight is not None:
if sample_weight.numdims != 1:
raise ValueError("Sample weights must be 1D tensor")
if sample_weight.shape != (n_samples, ):
raise ValueError(
"sample_weight.shape == {}, expected {}!".format(
sample_weight.shape, (n_samples, )))
# If the ratio of data variance between dimensions is too small, it
# will cause numerical errors. To address this, we artificially
# boost the variance by epsilon, a small fraction of the standard
# deviation of the largest dimension.
self.epsilon_ = self.var_smoothing * ht.var(X, axis=0).max()
if _refit:
self.classes_ = None
if self.__check_partial_fit_first_call(classes):
# This is the first call to partial_fit:
# initialize various cumulative counters
n_features = X.shape[1]
n_classes = len(self.classes_)
self.theta_ = ht.zeros((n_classes, n_features),
dtype=X.dtype,
device=X.device)
self.sigma_ = ht.zeros((n_classes, n_features),
dtype=X.dtype,
device=X.device)
self.class_count_ = ht.zeros((n_classes, ),
dtype=ht.float64,
device=X.device)
# Initialise the class prior
# Take into account the priors
if self.priors is not None:
if not isinstance(self.priors, ht.DNDarray):
priors = ht.array(self.priors,
dtype=X.dtype,
split=None,
device=X.device)
else:
priors = self.priors
# Check that the provide prior match the number of classes
if len(priors) != n_classes:
raise ValueError("Number of priors must match number of"
" classes.")
# Check that the sum is 1
if not ht.isclose(priors.sum(),
ht.array(1.0, dtype=priors.dtype)):
raise ValueError("The sum of the priors should be 1.")
# Check that the prior are non-negative
if (priors < 0).any():
raise ValueError("Priors must be non-negative.")
self.class_prior_ = priors
else:
# Initialize the priors to zeros for each class
self.class_prior_ = ht.zeros(len(self.classes_),
dtype=ht.float64,
split=None,
device=X.device)
else:
if X.shape[1] != self.theta_.shape[1]:
raise ValueError(
"Number of features {} does not match previous data {}.".
format(X.shape[1], self.theta_.shape[1]))
# Put epsilon back in each time
self.sigma_[:, :] -= self.epsilon_
classes = self.classes_
unique_y = ht.unique(y, sorted=True)
if unique_y.split is not None:
unique_y = ht.resplit(unique_y, axis=None)
unique_y_in_classes = ht.eq(unique_y, classes)
if not ht.all(unique_y_in_classes):
raise ValueError("The target label(s) {} in y do not exist in the "
"initial classes {}".format(
unique_y[~unique_y_in_classes], classes))
for y_i in unique_y:
# assuming classes.split is None
if y_i in classes:
i = ht.where(classes == y_i).item()
else:
classes_ext = torch.cat((classes._DNDarray__array,
y_i._DNDarray__array.unsqueeze(0)))
i = torch.argsort(classes_ext)[-1].item()
where_y_i = ht.where(y == y_i)._DNDarray__array.tolist()
X_i = X[where_y_i, :]
if sample_weight is not None:
sw_i = sample_weight[where_y_i]
if 0 not in sw_i.shape:
N_i = sw_i.sum()
else:
N_i = 0.0
sw_i = None
else:
sw_i = None
N_i = X_i.shape[0]
new_theta, new_sigma = self.__update_mean_variance(
self.class_count_[i], self.theta_[i, :], self.sigma_[i, :],
X_i, sw_i)
self.theta_[i, :] = new_theta
self.sigma_[i, :] = new_sigma
self.class_count_[i] += N_i
self.sigma_[:, :] += self.epsilon_
# Update if only no priors is provided
if self.priors is None:
# Empirical prior, with sample_weight taken into account
self.class_prior_ = self.class_count_ / self.class_count_.sum()
return self
def __partial_fit(
self,
x: DNDarray,
y: DNDarray,
classes: Optional[DNDarray] = None,
_refit: bool = False,
sample_weight: Optional[DNDarray] = None,
):
"""
Actual implementation of Gaussian NB fitting. Adapted to HeAT from scikit-learn.
Parameters
----------
x : DNDarray
Training set, where n_samples is the number of samples and
n_features is the number of features. Shape = (n_samples, n_features)
y : DNDarray
Labels for training set. Shape = (n_samples,)
classes : DNDarray, optional
List of all the classes that can possibly appear in the y vector.
Must be provided at the first call to :func:`partial_fit`, can be omitted
in subsequent calls. Shape = (n_classes,)
_refit : bool, optional
If ``True``, act as though this were the first time :func:`__partial_fit` is called
(ie, throw away any past fitting and start over).
sample_weight : DNDarray, optional
Weights applied to individual samples (1. for unweighted). Shape = (n_samples,)
"""
# TODO: sanitize x and y shape: sanitation/validation module, cf. #468
n_samples = x.shape[0]
if x.ndim != 2:
raise ValueError("expected x to be a 2-D tensor, is {}-D".format(
x.ndim))
if y.shape[0] != n_samples:
raise ValueError(
"y.shape[0] must match number of samples {}, is {}".format(
n_samples, y.shape[0]))
# TODO: sanitize sample_weight: sanitation/validation module, cf. #468
if sample_weight is not None:
if sample_weight.ndim != 1:
raise ValueError("Sample weights must be 1D tensor")
if sample_weight.shape != (n_samples, ):
raise ValueError(
"sample_weight.shape == {}, expected {}!".format(
sample_weight.shape, (n_samples, )))
# If the ratio of data variance between dimensions is too small, it
# will cause numerical errors. To address this, we artificially
# boost the variance by epsilon, a small fraction of the standard
# deviation of the largest dimension.
self.epsilon_ = self.var_smoothing * ht.var(x, axis=0).max()
if _refit:
self.classes_ = None
if self.__check_partial_fit_first_call(classes):
# This is the first call to partial_fit:
# initialize various cumulative counters
n_features = x.shape[1]
n_classes = len(self.classes_)
self.theta_ = ht.zeros((n_classes, n_features),
dtype=x.dtype,
device=x.device)
self.sigma_ = ht.zeros((n_classes, n_features),
dtype=x.dtype,
device=x.device)
self.class_count_ = ht.zeros((x.comm.size, n_classes),
dtype=ht.float64,
device=x.device,
split=0)
# Initialise the class prior
# Take into account the priors
if self.priors is not None:
if not isinstance(self.priors, ht.DNDarray):
priors = ht.array(self.priors,
dtype=x.dtype,
split=None,
device=x.device)
else:
priors = self.priors
# Check that the provide prior match the number of classes
if len(priors) != n_classes:
raise ValueError("Number of priors must match number of"
" classes.")
# Check that the sum is 1
if not ht.isclose(priors.sum(),
ht.array(1.0, dtype=priors.dtype)):
raise ValueError("The sum of the priors should be 1.")
# Check that the prior are non-negative
if (priors < 0).any():
raise ValueError("Priors must be non-negative.")
self.class_prior_ = priors
else:
# Initialize the priors to zeros for each class
self.class_prior_ = ht.zeros(len(self.classes_),
dtype=ht.float64,
split=None,
device=x.device)
else:
if x.shape[1] != self.theta_.shape[1]:
raise ValueError(
"Number of features {} does not match previous data {}.".
format(x.shape[1], self.theta_.shape[1]))
# Put epsilon back in each time
self.sigma_[:, :] -= self.epsilon_
classes = self.classes_
unique_y = ht.unique(y, sorted=True).resplit_(None)
unique_y_in_classes = ht.eq(unique_y, classes)
if not ht.all(unique_y_in_classes):
raise ValueError("The target label(s) {} in y do not exist in the "
"initial classes {}".format(
unique_y[~unique_y_in_classes], classes))
# from now on: extract torch tensors for local operations
# DNDarrays for distributed operations only
for y_i in unique_y.larray:
# assuming classes.split is None
if y_i in classes.larray:
i = torch.where(classes.larray == y_i)[0].item()
else:
classes_ext = torch.cat(
(classes.larray, y_i.larray.unsqueeze(0)))
i = torch.argsort(classes_ext)[-1].item()
where_y_i = torch.where(y.larray == y_i)[0]
X_i = x[where_y_i, :]
if sample_weight is not None:
sw_i = sample_weight[where_y_i]
if 0 not in sw_i.shape:
N_i = sw_i.sum().item()
else:
N_i = 0.0
sw_i = None
else:
sw_i = None
N_i = X_i.shape[0]
new_theta, new_sigma = self.__update_mean_variance(
self.class_count_.larray[:, i].item(),
self.theta_[i, :],
self.sigma_[i, :],
X_i,
sw_i,
)
self.theta_[i, :] = new_theta
self.sigma_[i, :] = new_sigma
self.class_count_.larray[:, i] += N_i
self.sigma_[:, :] += self.epsilon_
# Update only if no priors are provided
if self.priors is None:
# distributed class_count_: sum along distribution axis
self.class_count_ = self.class_count_.sum(axis=0, keepdim=True)
# Empirical prior, with sample_weight taken into account
self.class_prior_ = (self.class_count_ /
self.class_count_.sum()).squeeze(0)
return self
