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"""Test of min-max 1D features of sparse array classes""" |
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import pytest |
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import numpy as np |
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from numpy.testing import assert_equal, assert_array_equal |
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from scipy.sparse import coo_array, csr_array, csc_array, bsr_array |
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from scipy.sparse import coo_matrix, csr_matrix, csc_matrix, bsr_matrix |
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from scipy.sparse._sputils import isscalarlike |
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def toarray(a): |
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if isinstance(a, np.ndarray) or isscalarlike(a): |
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return a |
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return a.toarray() |
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formats_for_minmax = [bsr_array, coo_array, csc_array, csr_array] |
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formats_for_minmax_supporting_1d = [coo_array, csr_array] |
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@pytest.mark.parametrize("spcreator", formats_for_minmax_supporting_1d) |
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class Test_MinMaxMixin1D: |
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def test_minmax(self, spcreator): |
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D = np.arange(5) |
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X = spcreator(D) |
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assert_equal(X.min(), 0) |
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assert_equal(X.max(), 4) |
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assert_equal((-X).min(), -4) |
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assert_equal((-X).max(), 0) |
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def test_minmax_axis(self, spcreator): |
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D = np.arange(50) |
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X = spcreator(D) |
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for axis in [0, -1]: |
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assert_array_equal( |
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toarray(X.max(axis=axis)), D.max(axis=axis, keepdims=True) |
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) |
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assert_array_equal( |
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toarray(X.min(axis=axis)), D.min(axis=axis, keepdims=True) |
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) |
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for axis in [-2, 1]: |
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with pytest.raises(ValueError, match="axis out of range"): |
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X.min(axis=axis) |
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with pytest.raises(ValueError, match="axis out of range"): |
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X.max(axis=axis) |
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def test_numpy_minmax(self, spcreator): |
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dat = np.array([0, 1, 2]) |
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datsp = spcreator(dat) |
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assert_array_equal(np.min(datsp), np.min(dat)) |
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assert_array_equal(np.max(datsp), np.max(dat)) |
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def test_argmax(self, spcreator): |
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D1 = np.array([-1, 5, 2, 3]) |
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D2 = np.array([0, 0, -1, -2]) |
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D3 = np.array([-1, -2, -3, -4]) |
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D4 = np.array([1, 2, 3, 4]) |
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D5 = np.array([1, 2, 0, 0]) |
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for D in [D1, D2, D3, D4, D5]: |
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mat = spcreator(D) |
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assert_equal(mat.argmax(), np.argmax(D)) |
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assert_equal(mat.argmin(), np.argmin(D)) |
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assert_equal(mat.argmax(axis=0), np.argmax(D, axis=0)) |
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assert_equal(mat.argmin(axis=0), np.argmin(D, axis=0)) |
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D6 = np.empty((0,)) |
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for axis in [None, 0]: |
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mat = spcreator(D6) |
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with pytest.raises(ValueError, match="to an empty matrix"): |
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mat.argmin(axis=axis) |
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with pytest.raises(ValueError, match="to an empty matrix"): |
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mat.argmax(axis=axis) |
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@pytest.mark.parametrize("spcreator", formats_for_minmax) |
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class Test_ShapeMinMax2DWithAxis: |
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def test_minmax(self, spcreator): |
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dat = np.array([[-1, 5, 0, 3], [0, 0, -1, -2], [0, 0, 1, 2]]) |
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datsp = spcreator(dat) |
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for (spminmax, npminmax) in [ |
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(datsp.min, np.min), |
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(datsp.max, np.max), |
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(datsp.nanmin, np.nanmin), |
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(datsp.nanmax, np.nanmax), |
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]: |
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for ax, result_shape in [(0, (4,)), (1, (3,))]: |
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assert_equal(toarray(spminmax(axis=ax)), npminmax(dat, axis=ax)) |
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assert_equal(spminmax(axis=ax).shape, result_shape) |
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assert spminmax(axis=ax).format == "coo" |
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for spminmax in [datsp.argmin, datsp.argmax]: |
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for ax in [0, 1]: |
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assert isinstance(spminmax(axis=ax), np.ndarray) |
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spmat_form = { |
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'coo': coo_matrix, |
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'csr': csr_matrix, |
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'csc': csc_matrix, |
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'bsr': bsr_matrix, |
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} |
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datspm = spmat_form[datsp.format](dat) |
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for spm, npm in [ |
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(datspm.min, np.min), |
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(datspm.max, np.max), |
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(datspm.nanmin, np.nanmin), |
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(datspm.nanmax, np.nanmax), |
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]: |
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for ax, result_shape in [(0, (1, 4)), (1, (3, 1))]: |
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assert_equal(toarray(spm(axis=ax)), npm(dat, axis=ax, keepdims=True)) |
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assert_equal(spm(axis=ax).shape, result_shape) |
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assert spm(axis=ax).format == "coo" |
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for spminmax in [datspm.argmin, datspm.argmax]: |
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for ax in [0, 1]: |
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assert isinstance(spminmax(axis=ax), np.ndarray) |
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