def test_ApproxPredictorCaching(graph):
pred = network_hotspot.Predictor(None, graph)
pred.kernel = mock.Mock()
pred.kernel.return_value = 1.0
pred = network_hotspot.ApproxPredictorCaching(pred)
risks = np.asarray([0, 0, 0, 0], dtype=np.float)
pred.add_edge(risks, 0, None, 1)
np.testing.assert_allclose(risks, [1, 1, 1, 1])
def test_Predictor_with_time_kernel(graph, netpoints):
pred = network_hotspot.Predictor(netpoints, graph)
pred.kernel = network_hotspot.TriangleKernel(0.2)
pred.time_kernel = network_hotspot.ExponentialTimeKernel(1)
result = pred.predict()
assert result.graph is pred.graph
assert result.risks[0] == pytest.approx(1 * np.exp(-1 / 24))
assert result.risks[1] == pytest.approx(1)
def test_Predictor(graph, netpoints):
pred = network_hotspot.Predictor(netpoints, graph)
pred.kernel = network_hotspot.TriangleKernel(0.2)
pred.time_kernel = network_hotspot.ConstantTimeKernel()
result = pred.predict()
assert result.graph is pred.graph
assert result.risks[0] == pytest.approx(1)
assert result.risks[1] == pytest.approx(1)
def test_ApproxPredictorCaching2(graph2):
sq2 = np.sqrt(2)
pred = network_hotspot.Predictor(None, graph2)
pred.kernel = mock.Mock()
pred.kernel.return_value = 1.0
pred = network_hotspot.ApproxPredictorCaching(pred)
risks = np.asarray([0] * 9, dtype=np.float)
pred.add_edge(risks, 0, None, 1)
np.testing.assert_allclose(
risks,
[1, sq2 / 2, 1 / 4, sq2 / 4, sq2 / 2, 1 / 4, sq2 / 4, 2 / 4, 1 / 8])
def test_Predictor_add_split(graph2):
pred = network_hotspot.Predictor(None, graph2)
pred.kernel = mock.Mock()
pred.kernel.integrate.return_value = 1.0
pred.kernel.cutoff = 3
risks = np.zeros(9)
pred.add(risks, 0, 1, 0)
np.testing.assert_allclose(risks, [1, 0.5, 0.25, 0, 0.5, 0.25, 0, 0.5, 0])
risks = np.zeros(9)
pred.add(risks, 7, -1, 0.5)
np.testing.assert_allclose(risks, [0.25, 0.25, 0, 0, 0.5, 0.5, 0.5, 1, 0])
def test_ApproxPredictor(graph):
pred = network_hotspot.Predictor(None, graph)
pred.kernel = mock.Mock()
pred.kernel.return_value = 1.0
pred = network_hotspot.ApproxPredictor(pred)
risks = np.asarray([0, 0, 0, 0], dtype=np.float)
pred.add_edge(risks, 0, None, 1)
np.testing.assert_allclose(risks, [1, 1, 1, 1])
assert pred.kernel.call_args_list == [
mock.call(0),
mock.call(1.0),
mock.call(2.0),
mock.call(1.0)
]
def test_Predictor_add(graph):
pred = network_hotspot.Predictor(None, graph)
pred.kernel = network_hotspot.TriangleKernel(5)
risks = [0, 0, 0, 0]
pred.add(risks, 0, 1, 0.2)
assert risks[0] == pytest.approx(0.4 * (0.4 - 0.8 / 25))
assert risks[1] == pytest.approx(pred.kernel.integrate(0.8, 1.8))
assert risks[2] == pytest.approx(pred.kernel.integrate(1.8, 2.8))
assert risks[3] == pytest.approx(pred.kernel.integrate(2.8, 3.8))
risks = [0, 0, 0, 0]
pred.add(risks, 0, -1, 0.8)
assert risks[0] == pytest.approx(pred.kernel.integrate(0, 0.2))
assert risks[3] == pytest.approx(pred.kernel.integrate(0.2, 1.2))
assert risks[2] == pytest.approx(pred.kernel.integrate(1.2, 2.2))
assert risks[1] == pytest.approx(pred.kernel.integrate(2.2, 3.2))