Beispiel #1
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def test_exp_biases(temporal_graph):
    rw = TemporalRandomWalk(temporal_graph)
    times = np.array([1, 2, 3])
    t_0 = 1
    expected = np.exp(t_0 - times) / sum(np.exp(t_0 - times))
    biases = rw._exp_biases(times, t_0, decay=True)
    assert np.allclose(biases, expected)
Beispiel #2
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def test_not_progressing_enough(temporal_graph):

    rw = TemporalRandomWalk(temporal_graph)
    cw_size = 5  # no valid temporal walks of this size

    with pytest.raises(RuntimeError, match=r".* discarded .*"):
        rw.run(num_cw=1, cw_size=cw_size, max_walk_length=cw_size, seed=None)
Beispiel #3
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def test_exp_biases_extreme(temporal_graph):
    rw = TemporalRandomWalk(temporal_graph)

    large_times = [100000, 100001]
    biases = rw._exp_biases(large_times, t_0=0, decay=True)
    assert sum(biases) == pytest.approx(1)

    small_times = [0.000001, 0.000002]
    biases = rw._exp_biases(small_times, t_0=0, decay=True)
    assert sum(biases) == pytest.approx(1)
Beispiel #4
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def test_temporal_walks(temporal_graph):
    """
    valid time respecting walks (node -[time]-> node):

        1 -[2]-> 2 -[10]-> 4
        2 -[10]-> 4 -[12]-> 6
        3 -[2]-> 2 -[10]-> 4
        5 -[4]-> 4 -[12]-> 6
        1 -[2]-> 2 -[10]-> 4 -[12]-> 6
        3 -[2]-> 2 -[10]-> 4 -[12] -> 6
    """
    expected = {(1, 2, 4), (2, 4, 6), (3, 2, 4), (5, 4, 6), (1, 2, 4, 6),
                (3, 2, 4, 6)}

    rw = TemporalRandomWalk(temporal_graph)
    num_cw = 20  # how many walks to be sure we're getting valid temporal walks

    for walk in rw.run(num_cw=num_cw, cw_size=3, max_walk_length=4, seed=None):
        assert tuple(walk) in expected
Beispiel #5
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def test_cw_size_and_walk_length(temporal_graph, cw_size):
    rw = TemporalRandomWalk(temporal_graph)
    num_cw = 5
    max_walk_length = 3

    def run():
        return rw.run(num_cw=num_cw, cw_size=cw_size, max_walk_length=max_walk_length)

    if cw_size < 2:
        with pytest.raises(ValueError, match=r".* context window size .*"):
            run()
    elif max_walk_length < cw_size:
        with pytest.raises(ValueError, match=r".* maximum walk length .*"):
            run()
    else:
        walks = run()
        num_cw_obtained = sum([len(walk) - cw_size + 1 for walk in walks])
        assert num_cw == num_cw_obtained
        assert max(map(len, walks)) <= max_walk_length
Beispiel #6
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def test_init_parameters(temporal_graph):

    num_cw = 5
    cw_size = 3
    max_walk_length = 3
    seed = 0

    rw = TemporalRandomWalk(
        temporal_graph, cw_size=cw_size, max_walk_length=max_walk_length, seed=seed
    )
    rw_no_params = TemporalRandomWalk(temporal_graph)

    run_1 = rw.run(num_cw=num_cw)
    run_2 = rw_no_params.run(
        num_cw=num_cw, cw_size=cw_size, max_walk_length=max_walk_length, seed=seed
    )

    assert np.array_equal(run_1, run_2)
def test_init_parameters(temporal_graph):

    num_cw = 5
    cw_size = 3
    max_walk_length = 3
    seed = 0

    rw = TemporalRandomWalk(temporal_graph,
                            cw_size=cw_size,
                            max_walk_length=max_walk_length,
                            seed=seed)
    rw_no_params = TemporalRandomWalk(temporal_graph)

    assert rw.run(num_cw=num_cw) == rw_no_params.run(
        num_cw=num_cw,
        cw_size=cw_size,
        max_walk_length=max_walk_length,
        seed=seed)