62 lines
1.8 KiB
Python
62 lines
1.8 KiB
Python
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import numpy as np
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from numpy.testing import assert_array_almost_equal
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from scipy.sparse import csr_matrix
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from scipy.sparse.csgraph import csgraph_from_dense, csgraph_to_dense
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def test_csgraph_from_dense():
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np.random.seed(1234)
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G = np.random.random((10, 10))
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some_nulls = (G < 0.4)
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all_nulls = (G < 0.8)
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for null_value in [0, np.nan, np.inf]:
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G[all_nulls] = null_value
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with np.errstate(invalid="ignore"):
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G_csr = csgraph_from_dense(G, null_value=0)
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G[all_nulls] = 0
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assert_array_almost_equal(G, G_csr.toarray())
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for null_value in [np.nan, np.inf]:
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G[all_nulls] = 0
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G[some_nulls] = null_value
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with np.errstate(invalid="ignore"):
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G_csr = csgraph_from_dense(G, null_value=0)
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G[all_nulls] = 0
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assert_array_almost_equal(G, G_csr.toarray())
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def test_csgraph_to_dense():
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np.random.seed(1234)
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G = np.random.random((10, 10))
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nulls = (G < 0.8)
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G[nulls] = np.inf
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G_csr = csgraph_from_dense(G)
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for null_value in [0, 10, -np.inf, np.inf]:
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G[nulls] = null_value
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assert_array_almost_equal(G, csgraph_to_dense(G_csr, null_value))
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def test_multiple_edges():
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# create a random sqare matrix with an even number of elements
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np.random.seed(1234)
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X = np.random.random((10, 10))
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Xcsr = csr_matrix(X)
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# now double-up every other column
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Xcsr.indices[::2] = Xcsr.indices[1::2]
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# normal sparse toarray() will sum the duplicated edges
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Xdense = Xcsr.toarray()
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assert_array_almost_equal(Xdense[:, 1::2],
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X[:, ::2] + X[:, 1::2])
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# csgraph_to_dense chooses the minimum of each duplicated edge
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Xdense = csgraph_to_dense(Xcsr)
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assert_array_almost_equal(Xdense[:, 1::2],
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np.minimum(X[:, ::2], X[:, 1::2]))
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