def test_approx_radius(self):
adjacency = test_graph()
n = adjacency.shape[0]
spring = Spring(approx_radius=1.)
layout = spring.fit_transform(adjacency)
self.assertEqual((n, 2), layout.shape)
def test_errors(self):
adjacency = test_graph()
with self.assertRaises(ValueError):
Spring(position_init='toto')
Spring().fit(adjacency, position_init=np.ones(2, 2))
with self.assertRaises(TypeError):
# noinspection PyTypeChecker
Spring().fit(adjacency, position_init='toto')
def test_pos_init(self):
adjacency = test_graph()
n = adjacency.shape[0]
spring = Spring(strength=0.1, position_init='spectral', tol=1e3)
layout = spring.fit_transform(adjacency)
self.assertEqual((n, 2), layout.shape)
layout = spring.fit_transform(adjacency, position_init=layout)
self.assertEqual((n, 2), layout.shape)
def test_shape(self):
for adjacency in [test_graph(), test_digraph()]:
n = adjacency.shape[0]
spring = Spring()
layout = spring.fit_transform(adjacency)
self.assertEqual((n, 2), layout.shape)
spring = Spring(n_components=3)
layout = spring.fit_transform(adjacency)
self.assertEqual((n, 3), layout.shape)
def test_undirected(self):
adjacency = test_graph()
n = adjacency.shape[0]
method = Spring()
embedding = method.fit_transform(adjacency)
self.assertEqual(embedding.shape, (n, 2))
pred1 = method.predict(adjacency[0])
pred2 = method.predict(adjacency[0].toarray())
self.assertEqual(pred1.shape, (2, ))
self.assertAlmostEqual(np.linalg.norm(pred1 - pred2), 0)
pred1 = method.predict(adjacency)
pred2 = method.predict(adjacency.toarray())
self.assertTupleEqual(pred1.shape, (n, 2))
self.assertAlmostEqual(np.linalg.norm(pred1 - pred2), 0)
def test_undirected(self):
adjacency = test_graph()
n = adjacency.shape[0]
for method in self.methods:
with self.assertRaises(ValueError):
method.predict(adjacency[0])
embedding = method.fit_transform(adjacency)
self.assertEqual(embedding.shape, (n, 2))
ref = embedding[0]
pred1 = method.predict(adjacency[0])
pred2 = method.predict(adjacency[0].toarray())
self.assertEqual(pred1.shape, (2, ))
self.assertAlmostEqual(np.linalg.norm(pred1 - pred2), 0)
self.assertAlmostEqual(np.linalg.norm(pred1 - ref), 0)
pred1 = method.predict(adjacency)
pred2 = method.predict(adjacency.toarray())
self.assertTupleEqual(pred1.shape, (n, 2))
self.assertAlmostEqual(np.linalg.norm(pred1 - pred2), 0)
self.assertAlmostEqual(np.linalg.norm(pred1 - embedding), 0)
method = Spring()
embedding = method.fit_transform(adjacency)
self.assertEqual(embedding.shape, (n, 2))
pred1 = method.predict(adjacency[0])
pred2 = method.predict(adjacency[0].toarray())
self.assertEqual(pred1.shape, (2, ))
self.assertAlmostEqual(np.linalg.norm(pred1 - pred2), 0)
pred1 = method.predict(adjacency)
pred2 = method.predict(adjacency.toarray())
self.assertTupleEqual(pred1.shape, (n, 2))
self.assertAlmostEqual(np.linalg.norm(pred1 - pred2), 0)
def svg_graph(adjacency: Optional[sparse.csr_matrix] = None, position: Optional[np.ndarray] = None,
names: Optional[np.ndarray] = None,
labels: Optional[Iterable] = None, scores: Optional[Iterable] = None,
membership: Optional[sparse.csr_matrix] = None,
seeds: Union[list, dict] = None, width: Optional[float] = 400, height: Optional[float] = 300,
margin: float = 20, margin_text: float = 3, scale: float = 1, node_order: Optional[np.ndarray] = None,
node_size: float = 7, node_size_min: float = 1, node_size_max: float = 20,
display_node_weight: bool = False, node_weights: Optional[np.ndarray] = None, node_width: float = 1,
node_width_max: float = 3, node_color: str = 'gray',
display_edges: bool = True, edge_labels: Optional[list] = None,
edge_width: float = 1, edge_width_min: float = 0.5,
edge_width_max: float = 20, display_edge_weight: bool = True,
edge_color: Optional[str] = None, label_colors: Optional[Iterable] = None,
font_size: int = 12, directed: bool = False, filename: Optional[str] = None) -> str:
"""Return SVG image of a graph.
Parameters
----------
adjacency :
Adjacency matrix of the graph.
position :
Positions of the nodes.
names :
Names of the nodes.
labels :
Labels of the nodes (negative values mean no label).
scores :
Scores of the nodes (measure of importance).
membership :
Membership of the nodes (label distribution).
seeds :
Nodes to be highlighted (if dict, only keys are considered).
width :
Width of the image.
height :
Height of the image.
margin :
Margin of the image.
margin_text :
Margin between node and text.
scale :
Multiplicative factor on the dimensions of the image.
node_order :
Order in which nodes are displayed.
node_size :
Size of nodes.
node_size_min :
Minimum size of a node.
node_size_max:
Maximum size of a node.
node_width :
Width of node circle.
node_width_max :
Maximum width of node circle.
node_color :
Default color of nodes (svg color).
display_node_weight :
If ``True``, display node weights through node size.
node_weights :
Node weights (used only if **display_node_weight** is ``True``).
display_edges :
If ``True``, display edges.
edge_labels :
Labels of the edges, as a list of tuples (source, destination, label)
edge_width :
Width of edges.
edge_width_min :
Minimum width of edges.
edge_width_max :
Maximum width of edges.
display_edge_weight :
If ``True``, display edge weights through edge widths.
edge_color :
Default color of edges (svg color).
label_colors:
Colors of the labels (svg colors).
font_size :
Font size.
directed :
If ``True``, considers the graph as directed.
filename :
Filename for saving image (optional).
Returns
-------
image : str
SVG image.
Example
-------
>>> from sknetwork.data import karate_club
>>> graph = karate_club(True)
>>> adjacency = graph.adjacency
>>> position = graph.position
>>> from sknetwork.visualization import svg_graph
>>> image = svg_graph(adjacency, position)
>>> image[1:4]
'svg'
"""
# check adjacency
if adjacency is None:
if position is None:
raise ValueError("You must specify either adjacency or position.")
else:
n = position.shape[0]
adjacency = sparse.csr_matrix((n, n)).astype(int)
else:
n = adjacency.shape[0]
# node order
if node_order is None:
node_order = np.arange(n)
# position
if position is None:
spring = Spring()
position = spring.fit_transform(adjacency)
# node colors
node_colors = get_node_colors(n, labels, scores, membership, node_color, label_colors)
# node sizes
if node_weights is None:
node_weights = adjacency.T.dot(np.ones(n))
node_sizes = get_node_sizes(node_weights, node_size, node_size_min, node_size_max, display_node_weight)
# node widths
node_widths = get_node_widths(n, seeds, node_width, node_width_max)
# rescaling
position, width, height = rescale(position, width, height, margin, node_size, node_size_max, display_node_weight)
if names is not None:
text_length = np.max(np.array([len(str(name)) for name in names]))
width += text_length * font_size * .5
# scaling
position *= scale
height *= scale
width *= scale
svg = """<svg width="{}" height="{}" xmlns="http://www.w3.org/2000/svg">\n""".format(width, height)
# edges
if display_edges:
adjacency_coo = sparse.coo_matrix(adjacency)
if edge_color is None:
if names is None:
edge_color = 'black'
else:
edge_color = 'gray'
if directed:
svg += """<defs><marker id="arrow" markerWidth="10" markerHeight="10" refX="9" refY="3" orient="auto" >\n"""
svg += """<path d="M0,0 L0,6 L9,3 z" fill="{}"/></marker></defs>\n""".format(edge_color)
edge_colors, edge_order, edge_colors_residual = get_edge_colors(adjacency, edge_labels, edge_color,
label_colors)
edge_widths = get_edge_widths(adjacency_coo, edge_width, edge_width_min, edge_width_max, display_edge_weight)
for ix in edge_order:
i = adjacency_coo.row[ix]
j = adjacency_coo.col[ix]
color = edge_colors[ix]
if directed:
svg += svg_edge_directed(pos_1=position[i], pos_2=position[j], edge_width=edge_widths[ix],
edge_color=color, node_size=node_sizes[j])
else:
svg += svg_edge(pos_1=position[i], pos_2=position[j],
edge_width=edge_widths[ix], edge_color=color)
for i, j, color in edge_colors_residual:
if directed:
svg += svg_edge_directed(pos_1=position[i], pos_2=position[j], edge_width=edge_width,
edge_color=color, node_size=node_sizes[j])
else:
svg += svg_edge(pos_1=position[i], pos_2=position[j],
edge_width=edge_width, edge_color=color)
# nodes
for i in node_order:
if membership is None:
svg += svg_node(position[i], node_sizes[i], node_colors[i], node_widths[i])
else:
if membership[i].nnz == 1:
index = membership[i].indices[0]
svg += svg_node(position[i], node_sizes[i], node_colors[index], node_widths[i])
else:
svg += svg_pie_chart_node(position[i], node_sizes[i], membership[i].todense(),
node_colors, node_widths[i])
# text
if names is not None:
for i in range(n):
svg += svg_text(position[i] + node_sizes[i] + (margin_text, 0), names[i], font_size)
svg += """</svg>\n"""
if filename is not None:
with open(filename + '.svg', 'w') as f:
f.write(svg)
return svg
def svg_graph(adjacency: sparse.csr_matrix, position: Optional[np.ndarray] = None, names: Optional[np.ndarray] = None,
labels: Optional[Union[dict, np.ndarray]] = None, scores: Optional[np.ndarray] = None,
seeds: Union[list, dict] = None, width: float = 400, height: float = 300,
margin: float = 20, margin_text: float = 3, scale: float = 1, node_order: Optional[np.ndarray] = None,
node_size: float = 7, node_size_min: float = 1, node_size_max: float = 20,
display_node_weight: bool = False, node_weights: Optional[np.ndarray] = None, node_width: float = 1,
node_width_max: float = 3, node_color: str = 'gray',
display_edges: bool = True, edge_width: float = 1, edge_width_min: float = 0.5,
edge_width_max: float = 20, edge_weight: bool = True, edge_color: Optional[str] = None,
font_size: int = 12, directed: bool = False, filename: Optional[str] = None) -> str:
"""Return SVG image of a graph.
Parameters
----------
adjacency :
Adjacency matrix of the graph.
position :
Positions of the nodes.
names :
Names of the nodes.
labels :
Labels of the nodes (negative values mean no label).
scores :
Scores of the nodes (measure of importance).
seeds :
Nodes to be highlighted (if dict, only keys are considered).
width :
Width of the image.
height :
Height of the image.
margin :
Margin of the image.
margin_text :
Margin between node and text.
scale :
Multiplicative factor on the dimensions of the image.
node_order :
Order in which nodes are displayed.
node_size :
Size of nodes.
node_size_min :
Minimum size of a node.
node_size_max:
Maximum size of a node.
node_width :
Width of node circle.
node_width_max :
Maximum width of node circle.
node_color :
Default color of nodes (svg color).
display_node_weight :
Display node weights by node size.
node_weights :
Node weights (used only if **display_node_weight** is ``True``).
display_edges :
If ``True``, display edges.
edge_width :
Width of edges.
edge_width_min :
Minimum width of edges.
edge_width_max :
Maximum width of edges.
edge_weight :
Display edge weights with edge widths.
edge_color :
Default color of edges (svg color).
font_size :
Font size.
directed :
If ``True``, considers the graph as directed.
filename :
Filename for saving image (optional).
Returns
-------
image : str
SVG image.
Example
-------
>>> from sknetwork.data import karate_club
>>> graph = karate_club(True)
>>> adjacency = graph.adjacency
>>> position = graph.position
>>> from sknetwork.visualization import svg_graph
>>> image = svg_graph(adjacency, position)
>>> image[1:4]
'svg'
"""
n = adjacency.shape[0]
# node order
if node_order is None:
node_order = np.arange(n)
# position
if position is None:
spring = Spring()
position = spring.fit_transform(adjacency)
# colors
colors = get_colors(n, labels, scores, node_color)
if edge_color is None:
if names is None:
edge_color = 'black'
else:
edge_color = 'gray'
# node sizes
if node_weights is None:
node_weights = adjacency.dot(np.ones(n))
node_sizes = get_node_sizes(node_weights, node_size, node_size_min, node_size_max, display_node_weight)
# node widths
node_widths = get_node_widths(n, seeds, node_width, node_width_max)
# edge widths
adjacency_ = sparse.coo_matrix(adjacency)
edge_widths = get_edge_widths(adjacency_.data, edge_width, edge_width_min, edge_width_max, edge_weight)
# rescaling
position, width, height = rescale(position, width, height, margin, node_size_max, display_node_weight)
if names is not None:
text_length = np.max(np.array([len(str(name)) for name in names]))
width += text_length * font_size * .5
# scaling
position *= scale
height *= scale
width *= scale
svg = """<svg width="{}" height="{}" xmlns="http://www.w3.org/2000/svg">""".format(width, height)
if directed:
svg += """<defs><marker id="arrow" markerWidth="10" markerHeight="10" refX="9" refY="3" orient="auto" >"""
svg += """<path d="M0,0 L0,6 L9,3 z" fill="{}"/></marker></defs>""".format(edge_color)
# edges
if display_edges:
n_edges = len(adjacency_.row)
for ix in range(n_edges):
i = adjacency_.row[ix]
j = adjacency_.col[ix]
if directed:
svg += svg_edge_directed(pos_1=position[i], pos_2=position[j], stroke_width=edge_widths[ix],
stroke_color=edge_color, node_size=node_sizes[j])
else:
svg += svg_edge(pos_1=position[i], pos_2=position[j], stroke_width=edge_widths[ix], stroke_color=edge_color)
# nodes
for i in node_order:
svg += svg_node(position[i], node_sizes[i], colors[i], node_widths[i])
# text
if names is not None:
for i in range(n):
svg += svg_text(position[i] + node_sizes[i] + (margin_text, 0), names[i], font_size)
svg += """</svg>"""
if filename is not None:
with open(filename + '.svg', 'w') as f:
f.write(svg)
return svg