def test_search_number_data():
data = np.array([[0., 0.],
[0.1, 0.1],
[0.2, 0.2],
[0.2, 0.8],
[0.1, 0.9],
[0., 1.],
[0.8, 0.8],
[0.9, 0.9],
[1., 1.]])
target = np.array([[0], [0], [0],
[1], [1], [1],
[2], [2], [2]])
text_data = np.array([["a"], ["a"], ["a"],
["b"], ["b"], ["b"],
["c"], ["c"], ["c"]])
t = Topology()
t.load_data(data, text_data=text_data)
t.fit_transform(metric=None, lens=None)
t.map(resolution=2, overlap=0.3)
t.color(target, color_method="mean", color_type="rgb", normalize=True)
search_dicts = [{
"data_type": "number",
"operator": ">",
"column": 0,
"value": 0.7
}]
t.search_from_values(search_dicts=search_dicts, target=None, search_type="and")
test_color = ['#cccccc', '#cccccc', '#b20000']
assert t.hex_colors == test_color
def test_map():
data = np.array([[0., 0.],
[0.25, 0.25],
[0.5, 0.5],
[0.75, 0.75],
[1., 1.],
[1., 0.],
[0.25, 0.75],
[0.75, 0.25],
[0., 1.]])
t = Topology()
t.load_data(data)
t.fit_transform(metric=None, lens=None)
t.map(resolution=2, overlap=0.3, eps=0.3, min_samples=3)
test_nodes = np.array([[0.25, 0.25],
[0.25, 0.75],
[0.75, 0.25],
[0.75, 0.75]])
test_edges = np.array([[0, 1],
[0, 2],
[0, 3],
[1, 2],
[1, 3],
[2, 3]])
assert_array_equal(t.nodes, test_nodes)
assert_array_equal(t.edges, test_edges)
def test_color_mean_gray():
data = np.array([[0., 0.],
[0.1, 0.1],
[0.2, 0.2],
[0.2, 0.8],
[0.1, 0.9],
[0., 1.],
[0.8, 0.8],
[0.9, 0.9],
[1., 1.]])
target = np.array([[0], [0], [0],
[1], [1.1], [0.9],
[2], [2], [2]])
t = Topology()
t.load_data(data)
t.fit_transform(metric=None, lens=None)
t.map(resolution=2, overlap=0.3, eps=0.2, min_samples=3)
t.color(target, color_method="mean", color_type="gray", normalize=True)
test_color = ['#dcdcdc', '#787878', '#141414']
assert t.hex_colors == test_color
def test_transform_multi_lens():
data = np.array([[0., 0.], [0., 1.], [1., 1.]])
t = Topology()
t.load_data(data)
metric = "hamming"
lens = [L1Centrality(), GaussianDensity(h=0.25)]
t.fit_transform(metric=metric, lens=lens)
test_data = np.array([[1., 0.], [0., 1.], [1., 0.]])
assert_array_equal(t.point_cloud, test_data)
def test_transform_none_none():
data = np.array([[0., 0.], [1., 1.]])
t = Topology()
t.load_data(data)
metric = None
lens = None
t.fit_transform(metric=metric, lens=lens)
test_data = np.array([[0., 0.], [1., 1.]])
assert_array_equal(t.point_cloud, test_data)
def test_transform_none_pca():
data = np.array([[0., 1.], [1., 0.]])
t = Topology()
t.load_data(data)
metric = None
lens = [PCA(components=[0])]
t.fit_transform(metric=metric, lens=lens)
test_data = np.array([0., 1.])
test_data = test_data.reshape(test_data.shape[0], 1)
assert_array_equal(t.point_cloud, test_data)
def test_map_min_samples_under_zero():
data = np.array([[0., 0.],
[0.25, 0.25],
[0.5, 0.5],
[0.75, 0.75],
[1., 1.],
[1., 0.],
[0.25, 0.75],
[0.75, 0.25],
[0., 1.]])
t = Topology()
t.load_data(data)
t.fit_transform(metric=None, lens=None)
with pytest.raises(Exception):
t.map(min_samples=-1)
def test_color_different_size_input():
data = np.array([[0., 0.],
[0.1, 0.1],
[0.2, 0.2],
[0.2, 0.8],
[0.1, 0.9],
[0., 1.],
[0.8, 0.8],
[0.9, 0.9],
[1., 1.]])
target = np.array([0, 1, 2])
t = Topology()
t.load_data(data)
t.fit_transform()
t.map()
with pytest.raises(Exception):
t.color(target)
def test_color_ctype():
data = np.array([[0., 0.],
[0.1, 0.1],
[0.2, 0.2],
[0.2, 0.8],
[0.1, 0.9],
[0., 1.],
[0.8, 0.8],
[0.9, 0.9],
[1., 1.]])
target = np.array([[0], [0], [0],
[1], [1], [1],
[2], [2], [2]])
t = Topology()
t.load_data(data)
t.fit_transform()
t.map(resolution=2, overlap=0.3)
with pytest.raises(Exception):
t.color(target, color_type="hoge")
def reduction():
try:
# get request params
file_id = request.params.file_id
target_index = request.params.target_index
algorithm = int(request.params.algorithm)
# get file name
file_name = _get_file_name_from_id(file_id)
file_path = os.path.join(DATA_DIR, file_name)
# create topology instance
topology = Topology(verbose=0)
loader = CSVLoader(file_path)
topology.load(loader=loader, standardize=True)
# If target index isn't exists, use all data to calculate
if target_index != '':
topology.number_data, target = _split_target(
topology.number_data, int(target_index))
# transform & scaling data
scaler = preprocessing.MinMaxScaler(feature_range=(0.05, 0.95))
topology.fit_transform(lens=[REDUCTIONS[algorithm]], scaler=scaler)
body = {
"point_cloud": _ndarray_to_list(topology.point_cloud),
}
r = create_response(body)
r.set_header('Cache-Control', 'max-age=86400')
return r
except Exception as e:
body = json.dumps({"error_msg": e.args[0]})
r = create_response(body)
return r
def test_transform_data_none():
t = Topology()
with pytest.raises(Exception):
t.fit_transform()
