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