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test_usm_ndarray_searchsorted.py
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import numpy as np
import pytest
from helper import get_queue_or_skip, skip_if_dtype_not_supported
import dpctl
import dpctl.tensor as dpt
import dpctl.utils as dpu
def _check(hay_stack, needles, needles_np):
assert hay_stack.dtype == needles.dtype
assert hay_stack.ndim == 1
info_ = dpt.__array_namespace_info__()
default_dts_dev = info_.default_dtypes(device=hay_stack.device)
index_dt = default_dts_dev["indexing"]
p_left = dpt.searchsorted(hay_stack, needles, side="left")
assert p_left.dtype == index_dt
hs_np = dpt.asnumpy(hay_stack)
ref_left = np.searchsorted(hs_np, needles_np, side="left")
assert dpt.all(p_left == dpt.asarray(ref_left))
p_right = dpt.searchsorted(hay_stack, needles, side="right")
assert p_right.dtype == index_dt
ref_right = np.searchsorted(hs_np, needles_np, side="right")
assert dpt.all(p_right == dpt.asarray(ref_right))
sorter = dpt.arange(hay_stack.size)
ps_left = dpt.searchsorted(hay_stack, needles, side="left", sorter=sorter)
assert ps_left.dtype == index_dt
assert dpt.all(ps_left == p_left)
ps_right = dpt.searchsorted(hay_stack, needles, side="right", sorter=sorter)
assert ps_right.dtype == index_dt
assert dpt.all(ps_right == p_right)
def test_searchsorted_contig_bool():
get_queue_or_skip()
dt = dpt.bool
hay_stack = dpt.arange(0, 1, dtype=dt)
needles_np = np.random.choice([True, False], size=1024)
needles = dpt.asarray(needles_np)
_check(hay_stack, needles, needles_np)
_check(
hay_stack,
dpt.reshape(needles, (32, 32)),
np.reshape(needles_np, (32, 32)),
)
def test_searchsorted_strided_bool():
get_queue_or_skip()
dt = dpt.bool
hay_stack = dpt.repeat(dpt.arange(0, 1, dtype=dt), 4)[::4]
needles_np = np.random.choice([True, False], size=2 * 1024)
needles = dpt.asarray(needles_np)
sl = slice(None, None, -2)
_check(hay_stack, needles[sl], needles_np[sl])
_check(
hay_stack,
dpt.reshape(needles[sl], (32, 32)),
np.reshape(needles_np[sl], (32, 32)),
)
@pytest.mark.parametrize(
"idt",
[
dpt.int8,
dpt.uint8,
dpt.int16,
dpt.uint16,
dpt.int32,
dpt.uint32,
dpt.int64,
dpt.uint64,
],
)
def test_searchsorted_contig_int(idt):
q = get_queue_or_skip()
skip_if_dtype_not_supported(idt, q)
dt = dpt.dtype(idt)
max_v = dpt.iinfo(dt).max
hay_stack = dpt.arange(0, min(max_v, 255), dtype=dt)
needles_np = np.random.randint(0, max_v, dtype=dt, size=1024)
needles = dpt.asarray(needles_np)
_check(hay_stack, needles, needles_np)
_check(
hay_stack,
dpt.reshape(needles, (32, 32)),
np.reshape(needles_np, (32, 32)),
)
@pytest.mark.parametrize(
"idt",
[
dpt.int8,
dpt.uint8,
dpt.int16,
dpt.uint16,
dpt.int32,
dpt.uint32,
dpt.int64,
dpt.uint64,
],
)
def test_searchsorted_strided_int(idt):
q = get_queue_or_skip()
skip_if_dtype_not_supported(idt, q)
dt = dpt.dtype(idt)
max_v = dpt.iinfo(dt).max
hay_stack = dpt.repeat(dpt.arange(0, min(max_v, 255), dtype=dt), 4)[1::4]
needles_np = np.random.randint(0, max_v, dtype=dt, size=2 * 1024)
needles = dpt.asarray(needles_np)
sl = slice(None, None, -2)
_check(hay_stack, needles[sl], needles_np[sl])
_check(
hay_stack,
dpt.reshape(needles[sl], (32, 32)),
np.reshape(needles_np[sl], (32, 32)),
)
def _add_extended_fp(array):
array[0] = -dpt.inf
array[-2] = dpt.inf
array[-1] = dpt.nan
@pytest.mark.parametrize("idt", [dpt.float16, dpt.float32, dpt.float64])
def test_searchsorted_contig_fp(idt):
q = get_queue_or_skip()
skip_if_dtype_not_supported(idt, q)
dt = dpt.dtype(idt)
hay_stack = dpt.linspace(0, 1, num=255, dtype=dt, endpoint=True)
_add_extended_fp(hay_stack)
needles_np = np.random.uniform(-0.1, 1.1, size=1024).astype(dt)
needles = dpt.asarray(needles_np)
_check(hay_stack, needles, needles_np)
_check(
hay_stack,
dpt.reshape(needles, (32, 32)),
np.reshape(needles_np, (32, 32)),
)
@pytest.mark.parametrize("idt", [dpt.float16, dpt.float32, dpt.float64])
def test_searchsorted_strided_fp(idt):
q = get_queue_or_skip()
skip_if_dtype_not_supported(idt, q)
dt = dpt.dtype(idt)
hay_stack = dpt.repeat(
dpt.linspace(0, 1, num=255, dtype=dt, endpoint=True), 4
)[1::4]
_add_extended_fp(hay_stack)
needles_np = np.random.uniform(-0.1, 1.1, size=3 * 1024).astype(dt)
needles = dpt.asarray(needles_np)
sl = slice(1, None, 3)
_check(hay_stack, needles[sl], needles_np[sl])
_check(
hay_stack,
dpt.reshape(needles[sl], (32, 32)),
np.reshape(needles_np[sl], (32, 32)),
)
def _add_extended_cfp(array):
dt = array.dtype
ev_li = [
complex(-dpt.inf, -1),
complex(-dpt.inf, -dpt.inf),
complex(-dpt.inf, dpt.inf),
complex(-dpt.inf, dpt.nan),
complex(0, -dpt.inf),
complex(0, -1),
complex(0, dpt.inf),
complex(0, dpt.nan),
complex(dpt.inf, -dpt.inf),
complex(dpt.inf, -1),
complex(dpt.inf, dpt.inf),
complex(dpt.inf, dpt.nan),
complex(dpt.nan, -dpt.inf),
complex(dpt.nan, -1),
complex(dpt.nan, dpt.inf),
complex(dpt.nan, dpt.nan),
]
ev = dpt.asarray(ev_li, dtype=dt, device=array.device)
return dpt.sort(dpt.concat((ev, array)))
@pytest.mark.parametrize("idt", [dpt.complex64, dpt.complex128])
def test_searchsorted_contig_cfp(idt):
q = get_queue_or_skip()
skip_if_dtype_not_supported(idt, q)
dt = dpt.dtype(idt)
hay_stack = dpt.linspace(0, 1, num=255, dtype=dt, endpoint=True)
hay_stack = _add_extended_cfp(hay_stack)
needles_np = np.random.uniform(-0.1, 1.1, size=1024).astype(dt)
needles = dpt.asarray(needles_np)
_check(hay_stack, needles, needles_np)
_check(
hay_stack,
dpt.reshape(needles, (32, 32)),
np.reshape(needles_np, (32, 32)),
)
@pytest.mark.parametrize("idt", [dpt.complex64, dpt.complex128])
def test_searchsorted_strided_cfp(idt):
q = get_queue_or_skip()
skip_if_dtype_not_supported(idt, q)
dt = dpt.dtype(idt)
hay_stack = dpt.repeat(
dpt.linspace(0, 1, num=255, dtype=dt, endpoint=True), 4
)[1::4]
needles_np = np.random.uniform(-0.1, 1.1, size=3 * 1024).astype(dt)
needles = dpt.asarray(needles_np)
sl = slice(1, None, 3)
_check(hay_stack, needles[sl], needles_np[sl])
_check(
hay_stack,
dpt.reshape(needles[sl], (32, 32)),
np.reshape(needles_np[sl], (32, 32)),
)
hay_stack = _add_extended_cfp(hay_stack)
_check(hay_stack, needles[sl], needles_np[sl])
_check(
hay_stack,
dpt.reshape(needles[sl], (32, 32)),
np.reshape(needles_np[sl], (32, 32)),
)
def test_searchsorted_coerce():
get_queue_or_skip()
x1_i4 = dpt.arange(5, dtype="i4")
x1_i8 = dpt.arange(5, dtype="i8")
x2_i8 = dpt.arange(5, dtype="i8")
p1 = dpt.searchsorted(x1_i4, x2_i8)
p2 = dpt.searchsorted(x1_i8, x2_i8)
assert dpt.all(p1 == p2)
def test_searchsorted_validation():
with pytest.raises(TypeError):
dpt.searchsorted(None, None)
try:
x1 = dpt.arange(10, dtype="i4")
except dpctl.SyclDeviceCreationError:
pytest.skip("Default device could not be created")
with pytest.raises(TypeError):
dpt.searchsorted(x1, None)
with pytest.raises(TypeError):
dpt.searchsorted(x1, x1, sorter=dict())
with pytest.raises(ValueError):
dpt.searchsorted(x1, x1, side="unknown")
def test_searchsorted_validation2():
try:
x1 = dpt.arange(10, dtype="i4")
sorter = dpt.arange(10, dtype="i4")
except dpctl.SyclDeviceCreationError:
pytest.skip("Default device could not be created")
d = x1.sycl_device
q2 = dpctl.SyclQueue(d, property="in_order")
x2 = dpt.ones(5, dtype=x1.dtype, sycl_queue=q2)
with pytest.raises(dpu.ExecutionPlacementError):
dpt.searchsorted(x1, x2)
with pytest.raises(dpu.ExecutionPlacementError):
dpt.searchsorted(x1, x2, sorter=sorter)
sorter = dpt.ones(x1.shape, dtype=dpt.bool)
# non-integral sorter.dtype raises
with pytest.raises(ValueError):
dpt.searchsorted(x1, x1, sorter=sorter)
# non-matching x1.shape and sorter.shape raises
with pytest.raises(ValueError):
dpt.searchsorted(x1, x1, sorter=sorter[:-1])
# x1 must be 1d, or ValueError is raised
with pytest.raises(ValueError):
dpt.searchsorted(x1[dpt.newaxis, :], x1)
def test_pw_linear_interpolation_example():
get_queue_or_skip()
bins = dpt.asarray([0.0, 0.05, 0.2, 0.25, 0.5, 0.8, 0.95, 1])
vals = dpt.asarray([0.1, 0.15, 0.3, 0.5, 0.7, 0.53, 0.37, 0.1])
assert vals.shape == bins.shape
data_np = np.random.uniform(0, 1, size=10000)
data = dpt.asarray(data_np)
p = dpt.searchsorted(bins, data)
w = (data - bins[p]) / (bins[p - 1] - bins[p])
assert dpt.min(w) >= 0
assert dpt.max(w) <= 1
interp_vals = vals[p - 1] * w + (1 - w) * vals[p]
assert interp_vals.shape == data.shape
assert dpt.min(interp_vals) >= dpt.zeros(tuple())
av = dpt.sum(interp_vals) / data.size
exp = dpt.vecdot(vals[1:] + vals[:-1], bins[1:] - bins[:-1]) / 2
assert dpt.abs(av - exp) < 0.1
def test_out_of_bound_sorter_values():
get_queue_or_skip()
x = dpt.asarray([1, 2, 0], dtype="i4")
n = x.shape[0]
# use out-of-bounds indices in sorter
sorter = dpt.asarray([2, 0 - n, 1 - n], dtype="i8")
x2 = dpt.arange(3, dtype=x.dtype)
p = dpt.searchsorted(x, x2, sorter=sorter)
# verify that they were applied with mode="wrap"
assert dpt.all(p == dpt.arange(3, dtype=p.dtype))