projektAI/venv/Lib/site-packages/sklearn/utils/tests/test_cython_blas.py
2021-06-06 22:13:05 +02:00

230 lines
6.9 KiB
Python

import pytest
import numpy as np
from sklearn.utils._testing import assert_allclose
from sklearn.utils._cython_blas import _dot_memview
from sklearn.utils._cython_blas import _asum_memview
from sklearn.utils._cython_blas import _axpy_memview
from sklearn.utils._cython_blas import _nrm2_memview
from sklearn.utils._cython_blas import _copy_memview
from sklearn.utils._cython_blas import _scal_memview
from sklearn.utils._cython_blas import _rotg_memview
from sklearn.utils._cython_blas import _rot_memview
from sklearn.utils._cython_blas import _gemv_memview
from sklearn.utils._cython_blas import _ger_memview
from sklearn.utils._cython_blas import _gemm_memview
from sklearn.utils._cython_blas import RowMajor, ColMajor
from sklearn.utils._cython_blas import Trans, NoTrans
def _numpy_to_cython(dtype):
cython = pytest.importorskip("cython")
if dtype == np.float32:
return cython.float
elif dtype == np.float64:
return cython.double
RTOL = {np.float32: 1e-6, np.float64: 1e-12}
ORDER = {RowMajor: 'C', ColMajor: 'F'}
def _no_op(x):
return x
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
def test_dot(dtype):
dot = _dot_memview[_numpy_to_cython(dtype)]
rng = np.random.RandomState(0)
x = rng.random_sample(10).astype(dtype, copy=False)
y = rng.random_sample(10).astype(dtype, copy=False)
expected = x.dot(y)
actual = dot(x, y)
assert_allclose(actual, expected, rtol=RTOL[dtype])
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
def test_asum(dtype):
asum = _asum_memview[_numpy_to_cython(dtype)]
rng = np.random.RandomState(0)
x = rng.random_sample(10).astype(dtype, copy=False)
expected = np.abs(x).sum()
actual = asum(x)
assert_allclose(actual, expected, rtol=RTOL[dtype])
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
def test_axpy(dtype):
axpy = _axpy_memview[_numpy_to_cython(dtype)]
rng = np.random.RandomState(0)
x = rng.random_sample(10).astype(dtype, copy=False)
y = rng.random_sample(10).astype(dtype, copy=False)
alpha = 2.5
expected = alpha * x + y
axpy(alpha, x, y)
assert_allclose(y, expected, rtol=RTOL[dtype])
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
def test_nrm2(dtype):
nrm2 = _nrm2_memview[_numpy_to_cython(dtype)]
rng = np.random.RandomState(0)
x = rng.random_sample(10).astype(dtype, copy=False)
expected = np.linalg.norm(x)
actual = nrm2(x)
assert_allclose(actual, expected, rtol=RTOL[dtype])
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
def test_copy(dtype):
copy = _copy_memview[_numpy_to_cython(dtype)]
rng = np.random.RandomState(0)
x = rng.random_sample(10).astype(dtype, copy=False)
y = np.empty_like(x)
expected = x.copy()
copy(x, y)
assert_allclose(y, expected, rtol=RTOL[dtype])
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
def test_scal(dtype):
scal = _scal_memview[_numpy_to_cython(dtype)]
rng = np.random.RandomState(0)
x = rng.random_sample(10).astype(dtype, copy=False)
alpha = 2.5
expected = alpha * x
scal(alpha, x)
assert_allclose(x, expected, rtol=RTOL[dtype])
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
def test_rotg(dtype):
rotg = _rotg_memview[_numpy_to_cython(dtype)]
rng = np.random.RandomState(0)
a = dtype(rng.randn())
b = dtype(rng.randn())
c, s = 0.0, 0.0
def expected_rotg(a, b):
roe = a if abs(a) > abs(b) else b
if a == 0 and b == 0:
c, s, r, z = (1, 0, 0, 0)
else:
r = np.sqrt(a**2 + b**2) * (1 if roe >= 0 else -1)
c, s = a/r, b/r
z = s if roe == a else (1 if c == 0 else 1 / c)
return r, z, c, s
expected = expected_rotg(a, b)
actual = rotg(a, b, c, s)
assert_allclose(actual, expected, rtol=RTOL[dtype])
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
def test_rot(dtype):
rot = _rot_memview[_numpy_to_cython(dtype)]
rng = np.random.RandomState(0)
x = rng.random_sample(10).astype(dtype, copy=False)
y = rng.random_sample(10).astype(dtype, copy=False)
c = dtype(rng.randn())
s = dtype(rng.randn())
expected_x = c * x + s * y
expected_y = c * y - s * x
rot(x, y, c, s)
assert_allclose(x, expected_x)
assert_allclose(y, expected_y)
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
@pytest.mark.parametrize("opA, transA",
[(_no_op, NoTrans), (np.transpose, Trans)],
ids=["NoTrans", "Trans"])
@pytest.mark.parametrize("order", [RowMajor, ColMajor],
ids=["RowMajor", "ColMajor"])
def test_gemv(dtype, opA, transA, order):
gemv = _gemv_memview[_numpy_to_cython(dtype)]
rng = np.random.RandomState(0)
A = np.asarray(opA(rng.random_sample((20, 10)).astype(dtype, copy=False)),
order=ORDER[order])
x = rng.random_sample(10).astype(dtype, copy=False)
y = rng.random_sample(20).astype(dtype, copy=False)
alpha, beta = 2.5, -0.5
expected = alpha * opA(A).dot(x) + beta * y
gemv(transA, alpha, A, x, beta, y)
assert_allclose(y, expected, rtol=RTOL[dtype])
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
@pytest.mark.parametrize("order", [RowMajor, ColMajor],
ids=["RowMajor", "ColMajor"])
def test_ger(dtype, order):
ger = _ger_memview[_numpy_to_cython(dtype)]
rng = np.random.RandomState(0)
x = rng.random_sample(10).astype(dtype, copy=False)
y = rng.random_sample(20).astype(dtype, copy=False)
A = np.asarray(rng.random_sample((10, 20)).astype(dtype, copy=False),
order=ORDER[order])
alpha = 2.5
expected = alpha * np.outer(x, y) + A
ger(alpha, x, y, A)
assert_allclose(A, expected, rtol=RTOL[dtype])
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
@pytest.mark.parametrize("opB, transB",
[(_no_op, NoTrans), (np.transpose, Trans)],
ids=["NoTrans", "Trans"])
@pytest.mark.parametrize("opA, transA",
[(_no_op, NoTrans), (np.transpose, Trans)],
ids=["NoTrans", "Trans"])
@pytest.mark.parametrize("order", [RowMajor, ColMajor],
ids=["RowMajor", "ColMajor"])
def test_gemm(dtype, opA, transA, opB, transB, order):
gemm = _gemm_memview[_numpy_to_cython(dtype)]
rng = np.random.RandomState(0)
A = np.asarray(opA(rng.random_sample((30, 10)).astype(dtype, copy=False)),
order=ORDER[order])
B = np.asarray(opB(rng.random_sample((10, 20)).astype(dtype, copy=False)),
order=ORDER[order])
C = np.asarray(rng.random_sample((30, 20)).astype(dtype, copy=False),
order=ORDER[order])
alpha, beta = 2.5, -0.5
expected = alpha * opA(A).dot(opB(B)) + beta * C
gemm(transA, transB, alpha, A, B, beta, C)
assert_allclose(C, expected, rtol=RTOL[dtype])