def transform(self, graphs):
"""Transform a list of networkx graphs into a sparse matrix.
Parameters
----------
graphs : list[graphs]
The input list of networkx graphs.
Returns
-------
data_matrix : array-like, shape = [n_samples, n_features]
Vector representation of input graphs.
>>> # transforming the same graph (with different node-ids).
>>> import networkx as nx
>>> def get_path_graph(length=4):
... g = nx.path_graph(length)
... for n,d in g.nodes(data=True):
... d['label'] = 'C'
... for a,b,d in g.edges(data=True):
... d['label'] = '1'
... return g
>>> g = get_path_graph(4)
>>> g2 = get_path_graph(5)
>>> g2.remove_node(0)
>>> g[1][2]['label']='2'
>>> g2[2][3]['label']='2'
>>> v = Vectorizer()
>>> def vec_to_hash(vec):
... return hash(tuple(vec.data + vec.indices))
>>> vec_to_hash(v.transform([g])) == vec_to_hash (v.transform([g2]))
True
"""
if self.n_jobs == 1:
return self._transform_serial(graphs)
if self.n_jobs == -1:
pool = mp.Pool(mp.cpu_count())
else:
pool = mp.Pool(self.n_jobs)
results = [apply_async(
pool, self._transform_serial,
args=([subset_graphs]))
for subset_graphs in chunks(graphs, self.block_size)]
for i, p in enumerate(results):
pos_data_matrix = p.get()
if i == 0:
data_matrix = pos_data_matrix
else:
data_matrix = vstack([data_matrix, pos_data_matrix])
pool.close()
pool.join()
return data_matrix
def transform(self, graphs):
"""Transform a list of networkx graphs into a sparse matrix.
Parameters
----------
graphs : list[graphs]
The input list of networkx graphs.
Returns
-------
data_matrix : array-like, shape = [n_samples, n_features]
Vector representation of input graphs.
>>> # transforming the same graph (with different node-ids).
>>> import networkx as nx
>>> def get_path_graph(length=4):
... g = nx.path_graph(length)
... for n,d in g.nodes(data=True):
... d['label'] = 'C'
... for a,b,d in g.edges(data=True):
... d['label'] = '1'
... return g
>>> g = get_path_graph(4)
>>> g2 = get_path_graph(5)
>>> g2.remove_node(0)
>>> g[1][2]['label']='2'
>>> g2[2][3]['label']='2'
>>> v = Vectorizer()
>>> def vec_to_hash(vec):
... return hash(tuple(vec.data + vec.indices))
>>> vec_to_hash(v.transform([g])) == vec_to_hash (v.transform([g2]))
True
"""
if self.n_jobs == 1:
return self._transform_serial(graphs)
if self.n_jobs == -1:
pool = multiprocessing.Pool(multiprocessing.cpu_count())
else:
pool = multiprocessing.Pool(self.n_jobs)
results = [
apply_async(pool, self._transform_serial, args=([subset_graphs]))
for subset_graphs in chunks(graphs, self.block_size)
]
for i, p in enumerate(results):
pos_data_matrix = p.get()
if i == 0:
data_matrix = pos_data_matrix
else:
data_matrix = vstack([data_matrix, pos_data_matrix])
pool.close()
pool.join()
return data_matrix
def vertex_transform(self, graphs):
"""Transform a list of networkx graphs into a list of sparse matrices.
Each matrix has dimension n_nodes x n_features, i.e. each vertex is
associated to a sparse vector that encodes the neighborhood of the
vertex up to radius + distance.
Parameters
----------
graphs : list[graphs]
The input list of networkx graphs.
Returns
-------
matrix_list : array-like, shape = [n_samples, [n_nodes, n_features]]
Vector representation of each vertex in the input graphs.
"""
if self.n_jobs == 1:
return self._vertex_transform_serial(graphs)
if self.n_jobs == -1:
pool = multiprocessing.Pool(multiprocessing.cpu_count())
else:
pool = multiprocessing.Pool(self.n_jobs)
results = [
apply_async(pool,
self._vertex_transform_serial,
args=([subset_graphs]))
for subset_graphs in chunks(graphs, self.block_size)
]
matrix_list = []
for i, p in enumerate(results):
matrix_list += p.get()
pool.close()
pool.join()
return matrix_list
def vertex_transform(self, graphs):
"""Transform a list of networkx graphs into a list of sparse matrices.
Each matrix has dimension n_nodes x n_features, i.e. each vertex is
associated to a sparse vector that encodes the neighborhood of the
vertex up to radius + distance.
Parameters
----------
graphs : list[graphs]
The input list of networkx graphs.
Returns
-------
matrix_list : array-like, shape = [n_samples, [n_nodes, n_features]]
Vector representation of each vertex in the input graphs.
"""
if self.n_jobs == 1:
return self._vertex_transform_serial(graphs)
if self.n_jobs == -1:
pool = mp.Pool(mp.cpu_count())
else:
pool = mp.Pool(self.n_jobs)
results = [apply_async(
pool, self._vertex_transform_serial,
args=([subset_graphs]))
for subset_graphs in chunks(graphs, self.block_size)]
matrix_list = []
for i, p in enumerate(results):
matrix_list += p.get()
pool.close()
pool.join()
return matrix_list
