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Copy pathtest_set_functions.py
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258 lines (240 loc) · 10 KB
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# TODO: disable if opted out, refactor things
import cmath
import math
from collections import Counter, defaultdict
import pytest
from hypothesis import assume, given
from . import _array_module as xp
from . import dtype_helpers as dh
from . import hypothesis_helpers as hh
from . import pytest_helpers as ph
from . import shape_helpers as sh
pytestmark = [pytest.mark.data_dependent_shapes, pytest.mark.unvectorized]
@given(hh.arrays(dtype=hh.all_dtypes, shape=hh.shapes(min_side=1)))
def test_unique_all(x):
repro_snippet = ph.format_snippet(f"xp.unique_all({x!r}")
try:
out = xp.unique_all(x)
assert hasattr(out, "values")
assert hasattr(out, "indices")
assert hasattr(out, "inverse_indices")
assert hasattr(out, "counts")
ph.assert_dtype(
"unique_all", in_dtype=x.dtype, out_dtype=out.values.dtype, repr_name="out.values.dtype"
)
ph.assert_default_index(
"unique_all", out.indices.dtype, repr_name="out.indices.dtype"
)
ph.assert_default_index(
"unique_all", out.inverse_indices.dtype, repr_name="out.inverse_indices.dtype"
)
ph.assert_default_index(
"unique_all", out.counts.dtype, repr_name="out.counts.dtype"
)
assert (
out.indices.shape == out.values.shape
), f"{out.indices.shape=}, but should be {out.values.shape=}"
ph.assert_shape(
"unique_all",
out_shape=out.inverse_indices.shape,
expected=x.shape,
repr_name="out.inverse_indices.shape",
)
assert (
out.counts.shape == out.values.shape
), f"{out.counts.shape=}, but should be {out.values.shape=}"
scalar_type = dh.get_scalar_type(out.values.dtype)
counts = defaultdict(int)
firsts = {}
for i, idx in enumerate(sh.ndindex(x.shape)):
val = scalar_type(x[idx])
if counts[val] == 0:
firsts[val] = i
counts[val] += 1
for idx in sh.ndindex(out.indices.shape):
val = scalar_type(out.values[idx])
if cmath.isnan(val):
break
i = int(out.indices[idx])
expected = firsts[val]
assert i == expected, (
f"out.values[{idx}]={val} and out.indices[{idx}]={i}, "
f"but first occurence of {val} is at {expected}"
)
for idx in sh.ndindex(out.inverse_indices.shape):
ridx = int(out.inverse_indices[idx])
val = out.values[ridx]
expected = x[idx]
msg = (
f"out.inverse_indices[{idx}]={ridx} results in out.values[{ridx}]={val}, "
f"but should result in x[{idx}]={expected}"
)
if dh.is_float_dtype(out.values.dtype) and xp.isnan(expected):
assert xp.isnan(val), msg
else:
assert val == expected, msg
vals_idx = {}
nans = 0
for idx in sh.ndindex(out.values.shape):
val = scalar_type(out.values[idx])
count = int(out.counts[idx])
if cmath.isnan(val):
nans += 1
assert count == 1, (
f"out.counts[{idx}]={count} for out.values[{idx}]={val}, "
"but count should be 1 as NaNs are distinct"
)
else:
expected = counts[val]
assert (
expected > 0
), f"out.values[{idx}]={val}, but {val} not in input array"
count = int(out.counts[idx])
assert count == expected, (
f"out.counts[{idx}]={count} for out.values[{idx}]={val}, "
f"but should be {expected}"
)
assert (
val not in vals_idx.keys()
), f"out[{idx}]={val}, but {val} is also in out[{vals_idx[val]}]"
vals_idx[val] = idx
if dh.is_float_dtype(out.values.dtype):
assume(math.prod(x.shape) <= 128) # may not be representable
expected = sum(v for k, v in counts.items() if cmath.isnan(k))
assert nans == expected, f"{nans} NaNs in out, but should be {expected}"
except Exception as exc:
ph.add_note(exc, repro_snippet)
raise
@given(hh.arrays(dtype=hh.all_dtypes, shape=hh.shapes(min_side=1)))
def test_unique_counts(x):
repro_snippet = ph.format_snippet(f"xp.unique_counts({x!r}")
try:
out = xp.unique_counts(x)
assert hasattr(out, "values")
assert hasattr(out, "counts")
ph.assert_dtype(
"unique_counts", in_dtype=x.dtype, out_dtype=out.values.dtype, repr_name="out.values.dtype"
)
ph.assert_default_index(
"unique_counts", out.counts.dtype, repr_name="out.counts.dtype"
)
assert (
out.counts.shape == out.values.shape
), f"{out.counts.shape=}, but should be {out.values.shape=}"
scalar_type = dh.get_scalar_type(out.values.dtype)
counts = Counter(scalar_type(x[idx]) for idx in sh.ndindex(x.shape))
vals_idx = {}
nans = 0
for idx in sh.ndindex(out.values.shape):
val = scalar_type(out.values[idx])
count = int(out.counts[idx])
if cmath.isnan(val):
nans += 1
assert count == 1, (
f"out.counts[{idx}]={count} for out.values[{idx}]={val}, "
"but count should be 1 as NaNs are distinct"
)
else:
expected = counts[val]
assert (
expected > 0
), f"out.values[{idx}]={val}, but {val} not in input array"
count = int(out.counts[idx])
assert count == expected, (
f"out.counts[{idx}]={count} for out.values[{idx}]={val}, "
f"but should be {expected}"
)
assert (
val not in vals_idx.keys()
), f"out[{idx}]={val}, but {val} is also in out[{vals_idx[val]}]"
vals_idx[val] = idx
if dh.is_float_dtype(out.values.dtype):
assume(math.prod(x.shape) <= 128) # may not be representable
expected = sum(v for k, v in counts.items() if cmath.isnan(k))
assert nans == expected, f"{nans} NaNs in out, but should be {expected}"
except Exception as exc:
ph.add_note(exc, repro_snippet)
raise
@given(hh.arrays(dtype=hh.all_dtypes, shape=hh.shapes(min_side=1)))
def test_unique_inverse(x):
repro_snippet = ph.format_snippet(f"xp.unique_inverse({x!r}")
try:
out = xp.unique_inverse(x)
assert hasattr(out, "values")
assert hasattr(out, "inverse_indices")
ph.assert_dtype(
"unique_inverse", in_dtype=x.dtype, out_dtype=out.values.dtype, repr_name="out.values.dtype"
)
ph.assert_default_index(
"unique_inverse",
out.inverse_indices.dtype,
repr_name="out.inverse_indices.dtype",
)
ph.assert_shape(
"unique_inverse",
out_shape=out.inverse_indices.shape,
expected=x.shape,
repr_name="out.inverse_indices.shape",
)
scalar_type = dh.get_scalar_type(out.values.dtype)
distinct = set(scalar_type(x[idx]) for idx in sh.ndindex(x.shape))
vals_idx = {}
nans = 0
for idx in sh.ndindex(out.values.shape):
val = scalar_type(out.values[idx])
if cmath.isnan(val):
nans += 1
else:
assert (
val in distinct
), f"out.values[{idx}]={val}, but {val} not in input array"
assert (
val not in vals_idx.keys()
), f"out.values[{idx}]={val}, but {val} is also in out[{vals_idx[val]}]"
vals_idx[val] = idx
for idx in sh.ndindex(out.inverse_indices.shape):
ridx = int(out.inverse_indices[idx])
val = out.values[ridx]
expected = x[idx]
msg = (
f"out.inverse_indices[{idx}]={ridx} results in out.values[{ridx}]={val}, "
f"but should result in x[{idx}]={expected}"
)
if dh.is_float_dtype(out.values.dtype) and xp.isnan(expected):
assert xp.isnan(val), msg
else:
assert val == expected, msg
if dh.is_float_dtype(out.values.dtype):
assume(math.prod(x.shape) <= 128) # may not be representable
expected = xp.sum(xp.astype(xp.isnan(x), xp.uint8))
assert nans == expected, f"{nans} NaNs in out.values, but should be {expected}"
except Exception as exc:
ph.add_note(exc, repro_snippet)
raise
@given(hh.arrays(dtype=hh.all_dtypes, shape=hh.shapes(min_side=1)))
def test_unique_values(x):
repro_snippet = ph.format_snippet(f"xp.unique_values({x!r}")
try:
out = xp.unique_values(x)
ph.assert_dtype("unique_values", in_dtype=x.dtype, out_dtype=out.dtype)
scalar_type = dh.get_scalar_type(x.dtype)
distinct = set(scalar_type(x[idx]) for idx in sh.ndindex(x.shape))
vals_idx = {}
nans = 0
for idx in sh.ndindex(out.shape):
val = scalar_type(out[idx])
if cmath.isnan(val):
nans += 1
else:
assert val in distinct, f"out[{idx}]={val}, but {val} not in input array"
assert (
val not in vals_idx.keys()
), f"out[{idx}]={val}, but {val} is also in out[{vals_idx[val]}]"
vals_idx[val] = idx
if dh.is_float_dtype(out.dtype):
assume(math.prod(x.shape) <= 128) # may not be representable
expected = xp.sum(xp.astype(xp.isnan(x), xp.uint8))
assert nans == expected, f"{nans} NaNs in out, but should be {expected}"
except Exception as exc:
ph.add_note(exc, repro_snippet)
raise
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