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import pytest |
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3
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4
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@pytest.fixture |
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5
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def map_manager(somagic): |
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return somagic.map.manager |
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8
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9
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@pytest.fixture |
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def map_id_class(): |
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from so_magic.som.manager import MapId |
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return MapId |
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15
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@pytest.fixture |
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def identical_map_ids(): |
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def assert_map_ids_are_the_same(map_id1, map_id2): |
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assert str(map_id1) == str(map_id2) |
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assert dict(map_id1) == dict(map_id2) |
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return assert_map_ids_are_the_same |
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22
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23
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def test_map_manager_get_map_method(map_manager, test_dataset, map_id_class, identical_map_ids): |
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# assert the get_map method returns the same object when invoked with already seen arguments |
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som1 = map_manager.get_map(test_dataset, 4, 5, initialization='pca', maptype='toroid', gridtype='hexagonal') |
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som2 = map_manager.get_map(test_dataset, 4, 5, initialization='pca', maptype='toroid', gridtype='hexagonal') |
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assert id(som1) == id(som2) |
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map_id = map_id_class.from_self_organizing_map(som1) |
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identical_map_ids(map_id, map_id_class( |
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test_dataset.name, |
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4, |
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5, |
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'pca', |
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'toroid', |
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'hexagonal' |
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)) |
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assert som1.get_map_id() == str(map_id) |
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assert som1.nb_clusters == 0 |
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with pytest.raises(TypeError, match="'NoneType' object is not subscriptable"): |
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_ = som1.visual_umatrix |
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# tightly depends on the current implementation that requires to invoke the |
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# 'cluster' method of a SelfOrganisingMap instance to do 'clustering' on the |
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# output of the self-organising map training/learning algorithm |
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som1.cluster(4, random_state=1) |
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assert som1.nb_clusters == 4 |
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51
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umatrix_str_representation = som1.visual_umatrix |
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assert umatrix_str_representation == '3 3 3 3\n2 0 0 0\n2 2 0 0\n1 1 1 1\n1 1 1 1\n' |
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assert umatrix_str_representation == '3 3 3 3\n' \ |
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'2 0 0 0\n' \ |
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'2 2 0 0\n' \ |
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'1 1 1 1\n' \ |
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'1 1 1 1\n' |
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assert umatrix_str_representation == '\ |
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3 3 3 3\n\ |
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2 0 0 0\n\ |
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2 2 0 0\n\ |
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1 1 1 1\n\ |
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1 1 1 1\n\ |
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' |
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assert som1.datapoint_coordinates(0) == (2, 1) |
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boromir674 /
so-magic
