File size: 10,457 Bytes
7885a28 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 |
import numpy as np
import pytest
from pandas._libs import groupby as libgroupby
from pandas._libs.groupby import (
group_cumprod,
group_cumsum,
group_mean,
group_sum,
group_var,
)
from pandas.core.dtypes.common import ensure_platform_int
from pandas import isna
import pandas._testing as tm
class GroupVarTestMixin:
def test_group_var_generic_1d(self):
prng = np.random.default_rng(2)
out = (np.nan * np.ones((5, 1))).astype(self.dtype)
counts = np.zeros(5, dtype="int64")
values = 10 * prng.random((15, 1)).astype(self.dtype)
labels = np.tile(np.arange(5), (3,)).astype("intp")
expected_out = (
np.squeeze(values).reshape((5, 3), order="F").std(axis=1, ddof=1) ** 2
)[:, np.newaxis]
expected_counts = counts + 3
self.algo(out, counts, values, labels)
assert np.allclose(out, expected_out, self.rtol)
tm.assert_numpy_array_equal(counts, expected_counts)
def test_group_var_generic_1d_flat_labels(self):
prng = np.random.default_rng(2)
out = (np.nan * np.ones((1, 1))).astype(self.dtype)
counts = np.zeros(1, dtype="int64")
values = 10 * prng.random((5, 1)).astype(self.dtype)
labels = np.zeros(5, dtype="intp")
expected_out = np.array([[values.std(ddof=1) ** 2]])
expected_counts = counts + 5
self.algo(out, counts, values, labels)
assert np.allclose(out, expected_out, self.rtol)
tm.assert_numpy_array_equal(counts, expected_counts)
def test_group_var_generic_2d_all_finite(self):
prng = np.random.default_rng(2)
out = (np.nan * np.ones((5, 2))).astype(self.dtype)
counts = np.zeros(5, dtype="int64")
values = 10 * prng.random((10, 2)).astype(self.dtype)
labels = np.tile(np.arange(5), (2,)).astype("intp")
expected_out = np.std(values.reshape(2, 5, 2), ddof=1, axis=0) ** 2
expected_counts = counts + 2
self.algo(out, counts, values, labels)
assert np.allclose(out, expected_out, self.rtol)
tm.assert_numpy_array_equal(counts, expected_counts)
def test_group_var_generic_2d_some_nan(self):
prng = np.random.default_rng(2)
out = (np.nan * np.ones((5, 2))).astype(self.dtype)
counts = np.zeros(5, dtype="int64")
values = 10 * prng.random((10, 2)).astype(self.dtype)
values[:, 1] = np.nan
labels = np.tile(np.arange(5), (2,)).astype("intp")
expected_out = np.vstack(
[
values[:, 0].reshape(5, 2, order="F").std(ddof=1, axis=1) ** 2,
np.nan * np.ones(5),
]
).T.astype(self.dtype)
expected_counts = counts + 2
self.algo(out, counts, values, labels)
tm.assert_almost_equal(out, expected_out, rtol=0.5e-06)
tm.assert_numpy_array_equal(counts, expected_counts)
def test_group_var_constant(self):
# Regression test from GH 10448.
out = np.array([[np.nan]], dtype=self.dtype)
counts = np.array([0], dtype="int64")
values = 0.832845131556193 * np.ones((3, 1), dtype=self.dtype)
labels = np.zeros(3, dtype="intp")
self.algo(out, counts, values, labels)
assert counts[0] == 3
assert out[0, 0] >= 0
tm.assert_almost_equal(out[0, 0], 0.0)
class TestGroupVarFloat64(GroupVarTestMixin):
__test__ = True
algo = staticmethod(group_var)
dtype = np.float64
rtol = 1e-5
def test_group_var_large_inputs(self):
prng = np.random.default_rng(2)
out = np.array([[np.nan]], dtype=self.dtype)
counts = np.array([0], dtype="int64")
values = (prng.random(10**6) + 10**12).astype(self.dtype)
values.shape = (10**6, 1)
labels = np.zeros(10**6, dtype="intp")
self.algo(out, counts, values, labels)
assert counts[0] == 10**6
tm.assert_almost_equal(out[0, 0], 1.0 / 12, rtol=0.5e-3)
class TestGroupVarFloat32(GroupVarTestMixin):
__test__ = True
algo = staticmethod(group_var)
dtype = np.float32
rtol = 1e-2
@pytest.mark.parametrize("dtype", ["float32", "float64"])
def test_group_ohlc(dtype):
obj = np.array(np.random.default_rng(2).standard_normal(20), dtype=dtype)
bins = np.array([6, 12, 20])
out = np.zeros((3, 4), dtype)
counts = np.zeros(len(out), dtype=np.int64)
labels = ensure_platform_int(np.repeat(np.arange(3), np.diff(np.r_[0, bins])))
func = libgroupby.group_ohlc
func(out, counts, obj[:, None], labels)
def _ohlc(group):
if isna(group).all():
return np.repeat(np.nan, 4)
return [group[0], group.max(), group.min(), group[-1]]
expected = np.array([_ohlc(obj[:6]), _ohlc(obj[6:12]), _ohlc(obj[12:])])
tm.assert_almost_equal(out, expected)
tm.assert_numpy_array_equal(counts, np.array([6, 6, 8], dtype=np.int64))
obj[:6] = np.nan
func(out, counts, obj[:, None], labels)
expected[0] = np.nan
tm.assert_almost_equal(out, expected)
def _check_cython_group_transform_cumulative(pd_op, np_op, dtype):
"""
Check a group transform that executes a cumulative function.
Parameters
----------
pd_op : callable
The pandas cumulative function.
np_op : callable
The analogous one in NumPy.
dtype : type
The specified dtype of the data.
"""
is_datetimelike = False
data = np.array([[1], [2], [3], [4]], dtype=dtype)
answer = np.zeros_like(data)
labels = np.array([0, 0, 0, 0], dtype=np.intp)
ngroups = 1
pd_op(answer, data, labels, ngroups, is_datetimelike)
tm.assert_numpy_array_equal(np_op(data), answer[:, 0], check_dtype=False)
@pytest.mark.parametrize("np_dtype", ["int64", "uint64", "float32", "float64"])
def test_cython_group_transform_cumsum(np_dtype):
# see gh-4095
dtype = np.dtype(np_dtype).type
pd_op, np_op = group_cumsum, np.cumsum
_check_cython_group_transform_cumulative(pd_op, np_op, dtype)
def test_cython_group_transform_cumprod():
# see gh-4095
dtype = np.float64
pd_op, np_op = group_cumprod, np.cumprod
_check_cython_group_transform_cumulative(pd_op, np_op, dtype)
def test_cython_group_transform_algos():
# see gh-4095
is_datetimelike = False
# with nans
labels = np.array([0, 0, 0, 0, 0], dtype=np.intp)
ngroups = 1
data = np.array([[1], [2], [3], [np.nan], [4]], dtype="float64")
actual = np.zeros_like(data)
actual.fill(np.nan)
group_cumprod(actual, data, labels, ngroups, is_datetimelike)
expected = np.array([1, 2, 6, np.nan, 24], dtype="float64")
tm.assert_numpy_array_equal(actual[:, 0], expected)
actual = np.zeros_like(data)
actual.fill(np.nan)
group_cumsum(actual, data, labels, ngroups, is_datetimelike)
expected = np.array([1, 3, 6, np.nan, 10], dtype="float64")
tm.assert_numpy_array_equal(actual[:, 0], expected)
# timedelta
is_datetimelike = True
data = np.array([np.timedelta64(1, "ns")] * 5, dtype="m8[ns]")[:, None]
actual = np.zeros_like(data, dtype="int64")
group_cumsum(actual, data.view("int64"), labels, ngroups, is_datetimelike)
expected = np.array(
[
np.timedelta64(1, "ns"),
np.timedelta64(2, "ns"),
np.timedelta64(3, "ns"),
np.timedelta64(4, "ns"),
np.timedelta64(5, "ns"),
]
)
tm.assert_numpy_array_equal(actual[:, 0].view("m8[ns]"), expected)
def test_cython_group_mean_datetimelike():
actual = np.zeros(shape=(1, 1), dtype="float64")
counts = np.array([0], dtype="int64")
data = (
np.array(
[np.timedelta64(2, "ns"), np.timedelta64(4, "ns"), np.timedelta64("NaT")],
dtype="m8[ns]",
)[:, None]
.view("int64")
.astype("float64")
)
labels = np.zeros(len(data), dtype=np.intp)
group_mean(actual, counts, data, labels, is_datetimelike=True)
tm.assert_numpy_array_equal(actual[:, 0], np.array([3], dtype="float64"))
def test_cython_group_mean_wrong_min_count():
actual = np.zeros(shape=(1, 1), dtype="float64")
counts = np.zeros(1, dtype="int64")
data = np.zeros(1, dtype="float64")[:, None]
labels = np.zeros(1, dtype=np.intp)
with pytest.raises(AssertionError, match="min_count"):
group_mean(actual, counts, data, labels, is_datetimelike=True, min_count=0)
def test_cython_group_mean_not_datetimelike_but_has_NaT_values():
actual = np.zeros(shape=(1, 1), dtype="float64")
counts = np.array([0], dtype="int64")
data = (
np.array(
[np.timedelta64("NaT"), np.timedelta64("NaT")],
dtype="m8[ns]",
)[:, None]
.view("int64")
.astype("float64")
)
labels = np.zeros(len(data), dtype=np.intp)
group_mean(actual, counts, data, labels, is_datetimelike=False)
tm.assert_numpy_array_equal(
actual[:, 0], np.array(np.divide(np.add(data[0], data[1]), 2), dtype="float64")
)
def test_cython_group_mean_Inf_at_begining_and_end():
# GH 50367
actual = np.array([[np.nan, np.nan], [np.nan, np.nan]], dtype="float64")
counts = np.array([0, 0], dtype="int64")
data = np.array(
[[np.inf, 1.0], [1.0, 2.0], [2.0, 3.0], [3.0, 4.0], [4.0, 5.0], [5, np.inf]],
dtype="float64",
)
labels = np.array([0, 1, 0, 1, 0, 1], dtype=np.intp)
group_mean(actual, counts, data, labels, is_datetimelike=False)
expected = np.array([[np.inf, 3], [3, np.inf]], dtype="float64")
tm.assert_numpy_array_equal(
actual,
expected,
)
@pytest.mark.parametrize(
"values, out",
[
([[np.inf], [np.inf], [np.inf]], [[np.inf], [np.inf]]),
([[np.inf], [np.inf], [-np.inf]], [[np.inf], [np.nan]]),
([[np.inf], [-np.inf], [np.inf]], [[np.inf], [np.nan]]),
([[np.inf], [-np.inf], [-np.inf]], [[np.inf], [-np.inf]]),
],
)
def test_cython_group_sum_Inf_at_begining_and_end(values, out):
# GH #53606
actual = np.array([[np.nan], [np.nan]], dtype="float64")
counts = np.array([0, 0], dtype="int64")
data = np.array(values, dtype="float64")
labels = np.array([0, 1, 1], dtype=np.intp)
group_sum(actual, counts, data, labels, None, is_datetimelike=False)
expected = np.array(out, dtype="float64")
tm.assert_numpy_array_equal(
actual,
expected,
)
|