def test_searchsorted(side, obj_class, vals_class):
nelem = 1000
column_data = gen_rand("float64", nelem)
column_mask = random_bitmask(nelem)
values_data = gen_rand("float64", nelem)
values_mask = random_bitmask(nelem)
sr = cudf.Series.from_masked_array(column_data, column_mask)
vals = cudf.Series.from_masked_array(values_data, values_mask)
sr = sr.sort_values()
# Reference object can be Series, Index, or Column
if obj_class == "index":
sr.reset_index(drop=True)
elif obj_class == "column":
sr = sr._column
# Values can be Series or Index
if vals_class == "index":
vals.reset_index(drop=True)
psr = sr.to_pandas()
pvals = vals.to_pandas()
expect = psr.searchsorted(pvals, side)
got = sr.searchsorted(vals, side)
assert_eq(expect, cupy.asnumpy(got))
def test_sum_decimal(dtype, nelem):
np.random.seed(0)
data = [str(x) for x in gen_rand("int64", nelem) / 100]
expected = pd.Series([Decimal(x) for x in data]).sum()
got = cudf.Series(data).astype(dtype).sum()
assert_eq(expected, got)
def test_sum_of_squares_decimal(dtype):
np.random.seed(0)
data = [str(x) for x in gen_rand("int8", 3) / 10]
expected = pd.Series([Decimal(x) for x in data]).pow(2).sum()
got = cudf.Series(data).astype(dtype).sum_of_squares()
assert_eq(expected, got)
def test_product_decimal(dtype):
np.random.seed(0)
data = [str(x) for x in gen_rand("int8", 3) / 10]
expected = pd.Series([Decimal(x) for x in data]).product()
got = cudf.Series(data).astype(dtype).product()
assert_eq(expected, got)
def test_sum(dtype, nelem):
dtype = cudf.dtype(dtype).type
data = gen_rand(dtype, nelem)
sr = Series(data)
got = sr.sum()
expect = data.sum()
significant = 4 if dtype == np.float32 else 6
np.testing.assert_approx_equal(expect, got, significant=significant)
def test_max(dtype, nelem):
dtype = cudf.dtype(dtype).type
data = gen_rand(dtype, nelem)
sr = Series(data)
got = sr.max()
expect = dtype(data.max())
assert expect == got
def test_cummin(dtype, nelem):
if dtype == np.int8:
# to keep data in range
data = gen_rand(dtype, nelem, low=-2, high=2)
else:
data = gen_rand(dtype, nelem)
decimal = 4 if dtype == np.float32 else 6
# series
gs = cudf.Series(data)
ps = pd.Series(data)
np.testing.assert_array_almost_equal(gs.cummin().to_numpy(),
ps.cummin(),
decimal=decimal)
# dataframe series (named series)
gdf = cudf.DataFrame()
gdf["a"] = cudf.Series(data)
pdf = pd.DataFrame()
pdf["a"] = pd.Series(data)
np.testing.assert_array_almost_equal(gdf.a.cummin().to_numpy(),
pdf.a.cummin(),
decimal=decimal)
def test_sum_masked(nelem):
dtype = np.float64
data = gen_rand(dtype, nelem)
mask = utils.random_bitmask(nelem)
bitmask = utils.expand_bits_to_bytes(mask)[:nelem]
null_count = utils.count_zero(bitmask)
sr = Series.from_masked_array(data, mask, null_count)
got = sr.sum()
res_mask = np.asarray(bitmask, dtype=np.bool_)[: data.size]
expect = data[res_mask].sum()
significant = 4 if dtype == np.float32 else 6
np.testing.assert_approx_equal(expect, got, significant=significant)
def test_product(dtype, nelem):
np.random.seed(0)
dtype = cudf.dtype(dtype).type
if cudf.dtype(dtype).kind in {"u", "i"}:
data = np.ones(nelem, dtype=dtype)
# Set at most 30 items to [0..2) to keep the value within 2^32
for _ in range(30):
data[np.random.randint(low=0, high=nelem, size=1)] = (
np.random.uniform() * 2
)
else:
data = gen_rand(dtype, nelem)
sr = Series(data)
got = sr.product()
expect = pd.Series(data).product()
significant = 4 if dtype == np.float32 else 6
np.testing.assert_approx_equal(expect, got, significant=significant)
def test_sum_of_squares(dtype, nelem):
dtype = cudf.dtype(dtype).type
data = gen_rand(dtype, nelem)
sr = Series(data)
df = cudf.DataFrame(sr)
got = sr.sum_of_squares()
got_df = df.sum_of_squares()
expect = (data ** 2).sum()
if cudf.dtype(dtype).kind in {"u", "i"}:
if 0 <= expect <= np.iinfo(dtype).max:
np.testing.assert_array_almost_equal(expect, got)
np.testing.assert_array_almost_equal(expect, got_df.iloc[0])
else:
print("overflow, passing")
else:
np.testing.assert_approx_equal(
expect, got, significant=accuracy_for_dtype[dtype]
)
np.testing.assert_approx_equal(
expect, got_df.iloc[0], significant=accuracy_for_dtype[dtype]
)
def math_op_test(dtype,
fn,
nelem=128,
test_df=False,
positive_only=False,
check_dtype=True):
np.random.seed(0)
randvals = gen_rand(dtype, nelem, positive_only=positive_only)
h_series = pd.Series(randvals.astype(dtype))
d_series = cudf.Series(h_series)
if test_df:
d_in = cudf.DataFrame()
d_in[0] = d_series
h_in = pd.DataFrame()
h_in[0] = h_series
else:
d_in = d_series
h_in = h_series
expect = fn(h_in)
got = fn(d_in)
assert_eq(expect, got, check_dtype=check_dtype)
