test_som_manager

Complexity

Total Complexity

6

Size/Duplication

Total Lines	76
Duplicated Lines	0 %

Importance

Changes

0

4 Functions

Rating	Name	Duplication	Size	Complexity
A	map_manager()	0	3	1
A	test_map_manager_get_map_method()	0	38	2
A	identical_map_ids()	0	6	1
A	test_data()	0	18	2

import pytest


@pytest.fixture
def map_manager(somagic):
    return somagic.map.manager


@pytest.fixture
def identical_map_ids():
    def assert_map_ids_are_the_same(map_id1, map_id2):
        assert str(map_id1) == str(map_id2)
        assert dict(map_id1) == dict(map_id2)
    return assert_map_ids_are_the_same


@pytest.fixture
def test_data(test_dataset):
    from collections import OrderedDict
    from so_magic.som.manager import MapId
    map_specs = OrderedDict([
        ('nb-cols', 4),
        ('nb-rows', 5),
        ('initialization', 'pca'),
        ('maptype', 'toroid'),
        ('gridtype', 'hexagonal'),
    ])
    return type('TestData', (object,), {
        'map_parameters': type('SomModelParameters', (object,), {
            'args': (test_dataset, map_specs['nb-cols'], map_specs['nb-rows']),
            'kwargs': {k: map_specs[k] for k in ('initialization', 'maptype', 'gridtype')}
        }),
        'get_runtime_map_id': lambda x: MapId.from_self_organizing_map(x),
        'expected_map_id': MapId(*[test_dataset.name] + list(map_specs.values())),
    })


def test_map_manager_get_map_method(map_manager, test_data, identical_map_ids):
    # assert the get_map method returns the same object when invoked with already seen arguments
    som1 = map_manager.get_map(*test_data.map_parameters.args, **test_data.map_parameters.kwargs)
    som2 = map_manager.get_map(*test_data.map_parameters.args, **test_data.map_parameters.kwargs)
    assert id(som1) == id(som2)

    map_id = test_data.get_runtime_map_id(som1)
    identical_map_ids(map_id, test_data.expected_map_id)
    assert som1.get_map_id() == str(map_id)

    assert som1.nb_clusters == 0

    with pytest.raises(TypeError, match="'NoneType' object is not subscriptable"):
        _ = som1.visual_umatrix

    # tightly depends on the current implementation that requires to invoke the
    # 'cluster' method of a SelfOrganisingMap instance to do 'clustering' on the
    # output of the self-organising map training/learning algorithm
    som1.cluster(4, random_state=1)
    assert som1.nb_clusters == 4

    umatrix_str_representation = som1.visual_umatrix

    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'
    assert umatrix_str_representation == '3 3 3 3\n' \
                                         '2 0 0 0\n' \
                                         '2 2 0 0\n' \
                                         '1 1 1 1\n' \
                                         '1 1 1 1\n'
    assert umatrix_str_representation == '\
3 3 3 3\n\
2 0 0 0\n\
2 2 0 0\n\
1 1 1 1\n\
1 1 1 1\n\
'

    assert som1.datapoint_coordinates(0) == (2, 1)