Example #1
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def test_bad_init(barbell):

    with pytest.raises(TypeError):
        generator = AdjacencyPowerGenerator(None, num_powers=5)

    with pytest.raises(ValueError, match="num_powers: expected.*found -1"):
        generator = AdjacencyPowerGenerator(barbell, num_powers=-1)

    with pytest.raises(TypeError, match="num_powers: expected.*found float"):
        generator = AdjacencyPowerGenerator(barbell, num_powers=1.0)
Example #2
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def test_init(barbell):

    generator = AdjacencyPowerGenerator(barbell, num_powers=5)
    num_nodes = len(barbell.nodes())

    assert generator.num_powers == 5
    assert generator.Aadj_T.shape == (num_nodes, num_nodes)
    assert generator.transition_matrix_T.shape == (num_nodes, num_nodes)
Example #3
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def test_flow_batch_size(barbell, num_powers):

    batch_size = 2
    generator = AdjacencyPowerGenerator(barbell, num_powers=num_powers)
    for x, y in generator.flow(batch_size=batch_size).take(1):

        assert x[0].shape == (batch_size, )
        assert y.shape == (batch_size, 1, len(barbell.nodes()))
        assert x[1].shape == (batch_size, num_powers, len(barbell.nodes()))
Example #4
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def test_partial_powers(barbell, num_powers):

    Aadj = normalize_adj(barbell.to_adjacency_matrix(),
                         symmetric=False).todense()
    actual_powers = [Aadj]
    for _ in range(num_powers - 1):
        actual_powers.append(actual_powers[-1].dot(Aadj))

    generator = AdjacencyPowerGenerator(barbell, num_powers=num_powers)
    dataset = generator.flow(batch_size=1)
    for i, (x, y) in enumerate(dataset.take(barbell.number_of_nodes())):

        partial_powers = x[1].numpy()
        for j in range(num_powers):
            print(i, j)
            assert np.allclose(partial_powers[0, j, :], actual_powers[j][i, :])
Example #5
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def test_bad_flow(barbell):

    generator = AdjacencyPowerGenerator(barbell, num_powers=5)

    with pytest.raises(ValueError, match="batch_size: expected.*found 0"):
        generator.flow(0, num_parallel_calls=1)

    with pytest.raises(TypeError, match="batch_size: expected.*found float"):
        generator.flow(1.0, num_parallel_calls=1)

    with pytest.raises(ValueError,
                       match="num_parallel_calls: expected.*found 0"):
        generator.flow(1, num_parallel_calls=0)

    with pytest.raises(TypeError,
                       match="num_parallel_calls: expected.*found float"):
        generator.flow(1, num_parallel_calls=1.01)
Example #6
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def test_partial_powers(barbell, num_powers, weighted):

    raw_adj = barbell.to_adjacency_matrix(weighted=weighted)
    Aadj = normalize_adj(raw_adj, symmetric=False).todense()
    actual_powers = [Aadj]
    for _ in range(num_powers - 1):
        actual_powers.append(actual_powers[-1].dot(Aadj))

    generator = AdjacencyPowerGenerator(barbell,
                                        num_powers=num_powers,
                                        weighted=weighted)
    dataset = generator.flow(batch_size=1)
    for i, (x, y) in enumerate(dataset.take(barbell.number_of_nodes())):

        partial_powers = x[1].numpy()
        for j in range(num_powers):
            print(i, j)
            np.testing.assert_allclose(partial_powers[:, j, :],
                                       actual_powers[j][i, :],
                                       rtol=1e-5,
                                       atol=1e-8)
Example #7
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def test_flow(barbell):

    generator = AdjacencyPowerGenerator(barbell, num_powers=5)
    dataset = generator.flow(batch_size=1)
    assert tf.data.experimental.cardinality(dataset).numpy() == -1