def test_node_color_function_must_be_np_function(self, sc):
mapper = KeplerMapper()
with pytest.raises(
AttributeError,
match=r".*must be a function available on `numpy` class.*"):
mapper.visualize(sc, node_color_function=["yinz"])
def test_complete_pipeline(self, CoverClass):
# TODO: add a mock that asserts the cover was called appropriately.. or test number of cubes etc.
data, _ = datasets.make_circles()
data = data.astype(np.float64)
mapper = KeplerMapper()
graph = mapper.map(data, cover=CoverClass())
mapper.visualize(graph)
def test_BasicCover():
# TODO: add a mock that asserts the cover was called appropriately.. or test number of cubes etc.
data, _ = datasets.make_circles()
data = data.astype(np.float64)
mapper = KeplerMapper()
graph = mapper.map(data)
mapper.visualize(graph)
def test_lens_names(self):
mapper = KeplerMapper()
data = np.random.rand(100, 5)
lens = mapper.fit_transform(data)
graph = mapper.map(lens, data)
template = mapper.visualize(graph=graph)
template2 = mapper.visualize(graph=graph, lens_names=[])
assert template == template2
def test_visualize_one_color_function(self):
mapper = KeplerMapper()
data, labels = make_circles(1000, random_state=0)
lens = mapper.fit_transform(data, projection=[0])
graph = mapper.map(lens, data)
color_values = lens[:, 0]
mapper.visualize(graph,
color_values=color_values,
color_function_name=["hotdog"])
def test_visualize_multiple_node_color_functions(self):
""" convenience test for generating a vis with multiple node_color_values but 1d color_values"""
mapper = KeplerMapper()
data, labels = make_circles(1000, random_state=0)
lens = mapper.fit_transform(data, projection=[0])
graph = mapper.map(lens, data)
color_values = lens[:, 0]
mapper.visualize(
graph,
color_values=color_values,
color_function_name="hotdog",
node_color_function=["mean", "std", "median", "max"],
)
def test_visualize_multiple_color_functions(self):
""" convenience test for generating a vis with multiple color_values"""
mapper = KeplerMapper()
data, labels = make_circles(1000, random_state=0)
lens = mapper.fit_transform(data, projection=[0])
graph = mapper.map(lens, data)
color_values = lens[:, 0]
cv1 = np.array(lens)
cv2 = np.flip(cv1)
cv = np.column_stack([cv1, cv2])
mapper.visualize(graph,
color_values=cv,
color_function_name=["hotdog", "hotdiggitydog"])
def test_visualize_standalone_same(self, tmpdir):
"""ensure that the visualization is not dependent on the actual mapper object."""
mapper = KeplerMapper()
file = tmpdir.join("output.html")
data = np.random.rand(1000, 10)
lens = mapper.fit_transform(data, projection=[0])
graph = mapper.map(lens, data)
viz1 = mapper.visualize(graph, path_html=file.strpath)
new_mapper = KeplerMapper()
viz2 = new_mapper.visualize(graph, path_html=file.strpath)
assert viz1 == viz2
def test_visualize_standalone_same(self, tmpdir):
""" ensure that the visualization is not dependent on the actual mapper object.
"""
mapper = KeplerMapper()
file = tmpdir.join('output.html')
data = np.random.rand(1000, 10)
lens = mapper.fit_transform(data, projection=[0])
graph = mapper.map(lens, data)
viz1 = mapper.visualize(graph, path_html=file.strpath)
new_mapper = KeplerMapper()
viz2 = new_mapper.visualize(graph, path_html=file.strpath)
assert viz1 == viz2
def test_visualize_graph_with_cluster_stats_above_below(self):
mapper = KeplerMapper()
X = np.ones((1000, 3))
ids = np.random.choice(20, 1000)
X[ids, 0] = 10
lens = mapper.fit_transform(X, projection=[0])
graph = mapper.map(lens, X)
output = mapper.visualize(graph, X=X, lens=X)
def test_visualize_search_bar(self):
""" convenience test for generating a vis with a search bar (and also with multiple color_values _and_ multiple node_color_values)"""
mapper = KeplerMapper()
data, labels = make_circles(1000, random_state=0)
lens = mapper.fit_transform(data, projection=[0])
graph = mapper.map(lens, data)
color_values = lens[:, 0]
cv1 = np.array(lens)
cv2 = np.flip(cv1)
cv = np.column_stack([cv1, cv2])
mapper.visualize(
graph,
color_values=cv,
node_color_function=["mean", "std"],
color_function_name=["hotdog", "hotdiggitydog"],
include_searchbar=True,
)
def test_visualize_standalone(self):
# visualize will run on a fresh mapper object
mapper = KeplerMapper()
data = np.random.rand(100, 10)
lens = mapper.fit_transform(data, projection=[0])
graph = mapper.map(lens, data)
new_mapper = KeplerMapper()
viz = new_mapper.visualize(graph)
def test_file_not_written(self, tmpdir):
mapper = KeplerMapper(verbose=1)
file = tmpdir.join("output.html")
data = np.random.rand(1000, 10)
lens = mapper.fit_transform(data, projection=[0])
graph = mapper.map(lens, data)
viz = mapper.visualize(graph, path_html=file.strpath, save_file=False)
assert len(tmpdir.listdir()) == 0, "file was never written to"
def test_file_not_written(self, tmpdir):
mapper = KeplerMapper()
file = tmpdir.join('output.html')
data = np.random.rand(1000, 10)
lens = mapper.fit_transform(data, projection=[0])
graph = mapper.map(lens, data)
viz = mapper.visualize(graph, path_html=file.strpath, save_file=False)
assert len(tmpdir.listdir()) == 0, "file was never written to"
def test_file_written(self, tmpdir):
mapper = KeplerMapper()
file = tmpdir.join("output.html")
data = np.random.rand(1000, 2)
lens = mapper.fit_transform(data, projection=[0])
graph = mapper.map(lens, data)
viz = mapper.visualize(graph, path_html=file.strpath)
assert file.read() == viz
assert len(tmpdir.listdir()) == 1, "file was written to"
def test_no_color_values_many_color_function_exception(self):
mapper = KeplerMapper()
data, labels = make_circles(1000, random_state=0)
lens = mapper.fit_transform(data, projection=[0])
graph = mapper.map(lens, data)
with pytest.raises(Exception) as excinfo:
color_values = mapper.visualize(
graph,
color_values=None,
color_function_name=["hotdog", "hotdog", "hotdiggitydog"],
)
assert "Refusing to proceed" in str(excinfo.value)
def test_color_function_deprecated_replaced(self, default_colorscale):
mapper = KeplerMapper()
data, labels = make_circles(1000, random_state=0)
lens = mapper.fit_transform(data, projection=[0])
graph = mapper.map(lens, data)
color_values = lens[:, 0]
node_color_function = "mean"
inverse_X = data
projected_X = lens
projected_X_names = [
"projected_%s" % (i) for i in range(projected_X.shape[1])
]
inverse_X_names = [
"inverse_%s" % (i) for i in range(inverse_X.shape[1])
]
custom_tooltips = np.array(["customized_%s" % (l) for l in labels])
# https://docs.python.org/3/library/warnings.html#testing-warnings
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
# kmapper.visualize
html = mapper.visualize(graph,
color_function=lens,
color_function_name="lens[:, 0]")
_test_raised_deprecation_warning(w)
# visuals._format_mapper_data
graph_data = _format_mapper_data(
graph=graph,
color_function=color_values,
node_color_function=node_color_function,
X=inverse_X,
X_names=inverse_X_names,
lens=projected_X,
lens_names=projected_X_names,
custom_tooltips=custom_tooltips,
)
_test_raised_deprecation_warning(w)
# visuals._graph_data_distribution
histogram = _graph_data_distribution(
graph,
color_function=lens,
node_color_function=node_color_function,
colorscale=default_colorscale,
)
_test_raised_deprecation_warning(w)
def test_no_color_values_one_color_function_no_exception_yes_warning(self):
mapper = KeplerMapper()
data, labels = make_circles(1000, random_state=0)
lens = mapper.fit_transform(data, projection=[0])
graph = mapper.map(lens, data)
with warnings.catch_warnings(record=True) as w:
# Cause all warnings to always be triggered.
warnings.simplefilter("always")
color_values = mapper.visualize(graph,
color_values=None,
color_function_name=["hotdog"])
assert "unexpected" in str(w[-1].message)
def test_color_function_names_unequal_exception(self):
mapper = KeplerMapper()
data, labels = make_circles(1000, random_state=0)
lens = mapper.fit_transform(data, projection=[0])
graph = mapper.map(lens, data)
color_values = lens[:, 0]
cv1 = np.array(lens)
cv2 = np.flip(cv1)
cv = np.column_stack([cv1, cv2])
with pytest.raises(Exception) as excinfo:
mapper.visualize(graph,
color_values=cv,
color_function_name="hotdog")
assert "Must be equal" in str(excinfo.value)
with pytest.raises(Exception) as excinfo:
color_values = mapper.visualize(
graph,
color_values=cv,
color_function_name=["hotdog", "hotdog", "hotdiggitydog"],
)
assert "Must be equal" in str(excinfo.value)
def test_empty_graph_warning(self):
mapper = KeplerMapper()
graph = {"nodes": {}}
with pytest.raises(Exception):
mapper.visualize(graph)
def test_BasicCover():
# TODO: add a mock that asserts the cover was called appropriately.. or test number of cubes etc.
data, _ = datasets.make_circles()
mapper = KeplerMapper()
graph = mapper.map(data)
mapper.visualize(graph)