def transform(self, X):
"""Calculate the kernel matrix, between given and fitted dataset.
Parameters
----------
X : iterable
Each element must be an iterable with at most three features and at
least one. The first that is obligatory is a valid graph structure
(adjacency matrix or edge_dictionary) while the second is
node_labels and the third edge_labels (that fitting the given graph
format). If None the kernel matrix is calculated upon fit data.
The test samples.
Returns
-------
K : numpy array, shape = [n_targets, n_input_graphs]
corresponding to the kernel matrix, a calculation between
all pairs of graphs between target an features
"""
self._method_calling = 3
# Check is fit had been called
check_is_fitted(self, ['X'])
# Input validation and parsing
if X is None:
raise ValueError('`transform` input cannot be None')
else:
if not isinstance(X, collections.Iterable):
raise TypeError('input must be an iterable\n')
i = 0
out = list()
for (idx, x) in enumerate(iter(X)):
is_iter = isinstance(x, collections.Iterable)
if is_iter:
x = list(x)
if is_iter and len(x) in [0, 1, 2, 3]:
if len(x) == 0:
warnings.warn('Ignoring empty element on index: '
+ str(idx))
continue
elif len(x) == 1:
warnings.warn(
'Ignoring empty element on index: '
+ str(i) + '\nLabels must be provided.')
else:
x = Graph(x[0], x[1], {}, self._graph_format)
vertices = list(x.get_vertices(purpose="any"))
Labels = x.get_labels(purpose="any")
elif type(x) is Graph:
vertices = list(x.get_vertices(purpose="any"))
Labels = x.get_labels(purpose="any")
else:
raise TypeError('each element of X must be either '
'a graph object or a list with at '
'least a graph like object and '
'node labels dict \n')
# Hash based on the labels of fit
new_labels = {v: self._labels_hash_dict.get(l, None)
for v, l in iteritems(Labels)}
# Radix sort the other
g = ((vertices, new_labels) +
({n: x.neighbors(n, purpose="any")
for n in vertices},))
gr = {0: self.NH_(g)}
for r in range(1, self.R):
gr[r] = self.NH_(gr[r-1])
# save the output for all levels
out.append(gr)
i += 1
if i == 0:
raise ValueError('parsed input is empty')
# Transform - calculate kernel matrix
# Output is always normalized
km = self._calculate_kernel_matrix(out)
self._is_transformed = True
return km
def fit(self, X, y=None):
"""Fit a dataset, for a transformer.
Parameters
----------
X : iterable
Each element must be an iterable with at most three features and at
least one. The first that is obligatory is a valid graph structure
(adjacency matrix or edge_dictionary) while the second is
node_labels and the third edge_labels (that fitting the given graph
format). The train samples.
y : None
There is no need of a target in a transformer, yet the pipeline API
requires this parameter.
Returns
-------
self : object
Returns self.
"""
self._method_calling = 1
self._is_transformed = False
# Input validation and parsing
self.initialize()
if X is None:
raise ValueError('`fit` input cannot be None')
else:
if not isinstance(X, collections.Iterable):
raise TypeError('input must be an iterable\n')
i = 0
out = list()
gs = list()
self._labels_hash_dict, labels_hash_set = dict(), set()
for (idx, x) in enumerate(iter(X)):
is_iter = isinstance(x, collections.Iterable)
if is_iter:
x = list(x)
if is_iter and len(x) in [0, 1, 2, 3]:
if len(x) == 0:
warnings.warn('Ignoring empty element on index: '
+ str(idx))
continue
elif len(x) == 1:
warnings.warn(
'Ignoring empty element on index: '
+ str(i) + '\nLabels must be provided.')
else:
x = Graph(x[0], x[1], {}, self._graph_format)
vertices = list(x.get_vertices(purpose="any"))
Labels = x.get_labels(purpose="any")
elif type(x) is Graph:
vertices = list(x.get_vertices(purpose="any"))
Labels = x.get_labels(purpose="any")
else:
raise TypeError('each element of X must be either '
'a graph object or a list with at '
'least a graph like object and '
'node labels dict \n')
g = (vertices, Labels,
{n: x.neighbors(n, purpose="any") for n in vertices})
# collect all the labels
labels_hash_set |= set(itervalues(Labels))
gs.append(g)
i += 1
if i == 0:
raise ValueError('parsed input is empty')
# Hash labels
if len(labels_hash_set) > self._max_number:
warnings.warn('Number of labels is smaller than'
'the biggest possible.. '
'Collisions will appear on the '
'new labels.')
# If labels exceed the biggest possible size
nl, nrl = list(), len(labels_hash_set)
while nrl > self._max_number:
nl += self.random_state_.choice(self._max_number,
self._max_number,
replace=False).tolist()
nrl -= self._max_number
if nrl > 0:
nl += self.random_state_.choice(self._max_number,
nrl,
replace=False).tolist()
# unify the collisions per element.
else:
# else draw n random numbers.
nl = self.random_state_.choice(self._max_number, len(labels_hash_set),
replace=False).tolist()
self._labels_hash_dict = dict(zip(labels_hash_set, nl))
# for all graphs
for vertices, labels, neighbors in gs:
new_labels = {v: self._labels_hash_dict[l]
for v, l in iteritems(labels)}
g = (vertices, new_labels, neighbors,)
gr = {0: self.NH_(g)}
for r in range(1, self.R):
gr[r] = self.NH_(gr[r-1])
# save the output for all levels
out.append(gr)
self.X = out
# Return the transformer
return self
def parse_input(self, X):
"""Parse and create features for the NSPD kernel.
Parameters
----------
X : iterable
For the input to pass the test, we must have:
Each element must be an iterable with at most three features and at
least one. The first that is obligatory is a valid graph structure
(adjacency matrix or edge_dictionary) while the second is
node_labels and the third edge_labels (that correspond to the given
graph format). A valid input also consists of graph type objects.
Returns
-------
M : dict
A dictionary with keys all the distances from 0 to self.d
and values the the np.arrays with rows corresponding to the
non-null input graphs and columns to the enumerations of tuples
consisting of pairs of hash values and radius, from all the given
graphs of the input (plus the fitted one's on transform).
"""
if not isinstance(X, collections.Iterable):
raise TypeError('input must be an iterable\n')
else:
# Hold the number of graphs
ng = 0
# Holds all the data for combinations of r, d
data = collections.defaultdict(dict)
# Index all keys for combinations of r, d
all_keys = collections.defaultdict(dict)
for (idx, x) in enumerate(iter(X)):
is_iter = False
if isinstance(x, collections.Iterable):
is_iter, x = True, list(x)
if is_iter and len(x) in [0, 3]:
if len(x) == 0:
warnings.warn('Ignoring empty element' +
' on index: ' + str(idx))
continue
else:
g = Graph(x[0], x[1], x[2])
g.change_format("adjacency")
elif type(x) is Graph:
g = Graph(
x.get_adjacency_matrix(),
x.get_labels(purpose="adjacency", label_type="vertex"),
x.get_labels(purpose="adjacency", label_type="edge"))
else:
raise TypeError('each element of X must have either ' +
'a graph with labels for node and edge ' +
'or 3 elements consisting of a graph ' +
'type object, labels for vertices and ' +
'labels for edges.')
# Bring to the desired format
g.change_format(self._graph_format)
# Take the vertices
vertices = set(g.get_vertices(purpose=self._graph_format))
# Extract the dicitionary
ed = g.get_edge_dictionary()
# Convert edges to tuples
edges = {(j, k) for j in ed.keys() for k in ed[j].keys()}
# Extract labels for nodes
Lv = g.get_labels(purpose=self._graph_format)
# and for edges
Le = g.get_labels(purpose=self._graph_format,
label_type="edge")
# Produce all the neighborhoods and the distance pairs
# up to the desired radius and maximum distance
N, D, D_pair = g.produce_neighborhoods(self.r,
purpose="dictionary",
with_distances=True,
d=self.d)
# Hash all the neighborhoods
H = self._hash_neighborhoods(vertices, edges, Lv, Le, N,
D_pair)
if self._method_calling == 1:
for d in filterfalse(lambda x: x not in D,
range(self.d + 1)):
for (A, B) in D[d]:
for r in range(self.r + 1):
key = (H[r, A], H[r, B])
keys = all_keys[r, d]
idx = keys.get(key, None)
if idx is None:
idx = len(keys)
keys[key] = idx
data[r, d][ng, idx] = data[r, d].get(
(ng, idx), 0) + 1
elif self._method_calling == 3:
for d in filterfalse(lambda x: x not in D,
range(self.d + 1)):
for (A, B) in D[d]:
# Based on the edges of the bidirected graph
for r in range(self.r + 1):
keys = all_keys[r, d]
fit_keys = self._fit_keys[r, d]
key = (H[r, A], H[r, B])
idx = fit_keys.get(key, None)
if idx is None:
idx = keys.get(key, None)
if idx is None:
idx = len(keys) + len(fit_keys)
keys[key] = idx
data[r, d][ng, idx] = data[r, d].get(
(ng, idx), 0) + 1
ng += 1
if ng == 0:
raise ValueError('parsed input is empty')
if self._method_calling == 1:
# A feature matrix for all levels
M = dict()
for (key, d) in filterfalse(lambda a: len(a[1]) == 0,
iteritems(data)):
indexes, data = zip(*iteritems(d))
rows, cols = zip(*indexes)
M[key] = csr_matrix((data, (rows, cols)),
shape=(ng, len(all_keys[key])),
dtype=np.int64)
self._fit_keys = all_keys
self._ngx = ng
elif self._method_calling == 3:
# A feature matrix for all levels
M = dict()
for (key, d) in filterfalse(lambda a: len(a[1]) == 0,
iteritems(data)):
indexes, data = zip(*iteritems(d))
rows, cols = zip(*indexes)
M[key] = csr_matrix(
(data, (rows, cols)),
shape=(ng,
len(all_keys[key]) + len(self._fit_keys[key])),
dtype=np.int64)
self._ngy = ng
return M
