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_load_data_ndarray_data():
data = np.array([[0.0, 0.0], [1.0, 1.0]])
t = Topology()
t.load_data(data)
assert_array_equal(t.number_data, data)
def test_load_data_text_data_columns_text_data_diff_columns():
data = np.array([[0.0, 0.0], [1.0, 1.0]])
text_data = np.array([["data1-1", "data1-2"], ["data2-1", "data2-2"]])
text_data_columns = ["columns1"]
t = Topology()
with pytest.raises(ValueError):
t.load_data(data, text_data=text_data, text_data_columns=text_data_columns)
def test_load_data_number_data_columns_ndarray():
data = np.array([[0.0, 0.0], [1.0, 1.0]])
data_columns = np.array(["data1", "data2"])
t = Topology()
t.load_data(data, number_data_columns=data_columns)
assert_array_equal(t.number_data_columns, data_columns)
def test_load_data_text_data_ndarray():
data = np.array([[0.0, 0.0], [1.0, 1.0]])
text_data = np.array([["data1"], ["data2"]])
t = Topology()
t.load_data(data, text_data=text_data)
assert_array_equal(t.text_data, text_data)
def test_load_data_text_data_columns_text_data_is_none():
data = np.array([[0.0, 0.0], [1.0, 1.0]])
text_data = None
text_data_columns = ["columns1", "columns2"]
t = Topology()
with pytest.raises(ValueError):
t.load_data(data, text_data=text_data, text_data_columns=text_data_columns)
def test_load_data_standardize_true():
data = np.array([[0.0, 0.0], [1.0, 1.0]])
t = Topology()
t.load_data(data, standardize=True)
scaler = preprocessing.StandardScaler()
test_data = scaler.fit_transform(data)
assert_array_equal(t.std_number_data, test_data)
def test_load_data_text_data_columns():
data = np.array([[0.0, 0.0], [1.0, 1.0]])
text_data = np.array([["data1-1", "data1-2"], ["data2-1", "data2-2"]])
text_data_columns = ["columns1", "columns2"]
t = Topology()
t.load_data(data, text_data=text_data, text_data_columns=text_data_columns)
assert_array_equal(t.text_data_columns, np.array(text_data_columns))
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_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 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_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_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_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 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()
def test_load_data_none():
data = None
t = Topology()
with pytest.raises(ValueError):
t.load_data(data)
def test_search_target_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.]])
columns = np.array(["columns1", "columns2"])
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"]])
text_data_columns = np.array(["text_columns"])
t = Topology()
t.load_data(data, number_data_columns=columns, text_data=text_data, text_data_columns=text_data_columns)
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": -1,
"value": 2
}]
t.search_from_values(search_dicts=search_dicts, target=target, search_type="and")
test_color = ['#cccccc', '#cccccc', '#b20000']
assert t.hex_colors == test_color
def test_load_data_not_2d_array():
data = [[[0.0, 0.0], [1.0, 1.0]]]
t = Topology()
with pytest.raises(ValueError):
t.load_data(data)
def create():
try:
# get request params
data = json.loads(request.params.data)
file_id = int(data["file_id"])
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)
target_index = data["target_index"]
mode = int(data["mode"])
clustering_algorithm = int(data["clustering_algorithm"])
train_size = float(data["train_size"])
k = int(data["k"])
eps = float(data["eps"])
min_samples = int(data["min_samples"])
resolution = int(data["resolution"])
overlap = float(data["overlap"])
topology.point_cloud = np.array(data["point_cloud"])
visualize_mode = int(data["visualize_mode"])
if mode == 0:
# scatter plot
colors = []
for i in range(topology.number_data.shape[1]):
t = topology.number_data[:, i]
scaler = preprocessing.MinMaxScaler()
t = scaler.fit_transform(t.reshape(-1, 1))
topology.color_point_cloud(target=t, normalize=False)
colors.append(topology.point_cloud_hex_colors)
elif mode == 1:
# unsupervised_clusterings
# 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))
clusters = [
cluster.KMeans(n_clusters=k),
cluster.DBSCAN(eps=eps, min_samples=min_samples)
]
topology.unsupervised_clustering_point_cloud(
clusterer=clusters[clustering_algorithm])
if target_index != '':
topology.number_data = _concat_target(topology.number_data,
target,
int(target_index))
colors = []
for i in range(topology.number_data.shape[1]):
colors.append(topology.point_cloud_hex_colors)
elif mode == 2:
# supervised_clusterings
# 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))
clusters = [neighbors.KNeighborsClassifier(n_neighbors=k)]
topology.supervised_clustering_point_cloud(
clusterer=clusters[clustering_algorithm],
target=target,
train_size=train_size)
if target_index != '':
topology.number_data = _concat_target(topology.number_data,
target,
int(target_index))
colors = []
for i in range(topology.number_data.shape[1]):
colors.append(topology.point_cloud_hex_colors)
elif mode == 3:
# tda
topology.map(resolution=resolution,
overlap=overlap,
eps=eps,
min_samples=min_samples)
if visualize_mode == 2:
presenter = SpectralPresenter(fig_size=(10, 10),
node_size=5,
edge_width=1)
pos = presenter._get_position(topology.nodes, topology.edges)
topology.nodes = np.array(list(pos.values()))
print("spectral")
colors = []
for i in range(topology.number_data.shape[1]):
t = topology.number_data[:, i]
scaler = preprocessing.MinMaxScaler()
t = scaler.fit_transform(t.reshape(-1, 1))
topology.color(target=t)
colors.append(topology.hex_colors)
if mode < 3:
hypercubes = _ndarray_to_list(
np.arange(len(topology.point_cloud)).reshape(-1, 1))
nodes = _ndarray_to_list(topology.point_cloud)
edges = []
node_sizes = [MIN_NODE_SIZE] * len(topology.point_cloud)
colors = colors
elif mode == 3:
hypercubes = _convert_hypercubes_to_array(topology.hypercubes)
nodes = _ndarray_to_list(topology.nodes)
edges = _ndarray_to_list(topology.edges)
scaler = preprocessing.MinMaxScaler(feature_range=(MIN_NODE_SIZE,
MAX_NODE_SIZE))
node_sizes = _ndarray_to_list(
scaler.fit_transform(topology.node_sizes))
colors = colors
body = {
"hypercubes": hypercubes,
"nodes": nodes,
"edges": edges,
"node_sizes": node_sizes,
"colors": colors,
"train_index": _ndarray_to_list(topology.train_index)
}
r = create_response(body)
return r
except Exception as e:
body = json.dumps({"error_msg": e.args[0]})
r = create_response(body)
return r
def test_search_multiple_values():
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": 0.3
}, {
"data_type": "text",
"operator": "like",
"column": 0,
"value": "a"
}]
t.search_from_values(search_dicts=search_dicts, target=None, search_type="and")
test_color = ['#0000b2', '#cccccc', '#cccccc']
assert t.hex_colors == test_color
def search():
try:
data = json.loads(request.params.data)
topology = Topology(verbose=0)
file_id = int(data["file_id"])
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)
target_index = data["target_index"]
mode = int(data["mode"])
clustering_algorithm = int(data["clustering_algorithm"])
train_size = float(data["train_size"])
k = int(data["k"])
eps = float(data["eps"])
min_samples = int(data["min_samples"])
topology.point_cloud = np.array(data["point_cloud"])
hypercubes = data["hypercubes"]
topology.hypercubes = _convert_array_to_hypercubes(hypercubes)
topology.nodes = np.array(data["nodes"])
topology.edges = np.array(data["edges"])
search_type = data["search_type"]
search_dicts = []
for condition in data["search_conditions"]:
c = {
"data_type": condition["data_type"],
"column": condition["column"],
"operator": condition["operator"],
"value": _decode_txt(condition["value"])
}
search_dicts.append(c)
colors = []
if mode == 0:
for i in range(topology.number_data.shape[1]):
t = topology.number_data[:, i]
scaler = preprocessing.MinMaxScaler()
t = scaler.fit_transform(t.reshape(-1, 1))
topology.color_point_cloud(target=t)
if len(search_dicts) > 0:
topology.search_point_cloud(search_dicts=search_dicts,
search_type=search_type)
colors.append(topology.point_cloud_hex_colors)
elif mode == 1:
# unsupervised_clusterings
if target_index != '':
topology.number_data, target = _split_target(
topology.number_data, int(target_index))
clusters = [
cluster.KMeans(n_clusters=k),
cluster.DBSCAN(eps=eps, min_samples=min_samples)
]
topology.unsupervised_clustering_point_cloud(
clusterer=clusters[clustering_algorithm])
if target_index != '':
topology.number_data = _concat_target(topology.number_data,
target,
int(target_index))
if len(search_dicts) > 0:
topology.search_point_cloud(search_dicts=search_dicts,
search_type=search_type)
colors = []
for i in range(topology.number_data.shape[1]):
colors.append(topology.point_cloud_hex_colors)
elif mode == 2:
# supervised_clusterings
if target_index != '':
topology.number_data, target = _split_target(
topology.number_data, int(target_index))
clusters = [neighbors.KNeighborsClassifier(n_neighbors=k)]
topology.supervised_clustering_point_cloud(
clusterer=clusters[clustering_algorithm],
target=target,
train_size=train_size)
if target_index != '':
topology.number_data = _concat_target(topology.number_data,
target,
int(target_index))
if len(search_dicts) > 0:
topology.search_point_cloud(search_dicts=search_dicts,
search_type=search_type)
colors = []
for i in range(topology.number_data.shape[1]):
colors.append(topology.point_cloud_hex_colors)
elif mode == 3:
# tda
topology.graph_util = GraphUtil(point_cloud=topology.point_cloud,
hypercubes=topology.hypercubes)
colors = []
for i in range(topology.number_data.shape[1]):
t = topology.number_data[:, i]
scaler = preprocessing.MinMaxScaler()
t = scaler.fit_transform(t.reshape(-1, 1))
topology.color(target=t)
if len(search_dicts) > 0:
topology.search(search_dicts=search_dicts,
search_type=search_type)
colors.append(topology.hex_colors)
body = {
"colors": colors,
}
r = create_response(body)
return r
except Exception as e:
body = json.dumps({"error_msg": e.args[0]})
r = create_response(body)
return r
def test_map_point_cloud_none():
t = Topology()
with pytest.raises(Exception):
t.map()
