Origin/0.1a10

.circleci/config.yml

@@ -30,7 +30,7 @@ jobs:
       - DOC_REPO: GraKeL
 
       # The base URL for the Github page where the documentation will be hosted
-      - DOC_URL: 0.1a8
+      - DOC_URL: 0.1a10
 
       # The email is to be used for commits in the Github Page
       - EMAIL: [email protected]

.conda_build.sh

@@ -10,4 +10,6 @@ conda-build . --output-folder conda_build/ --python 3.7
 conda-build . --output-folder conda_build/ --python 3.8
 conda-build . --output-folder conda_build/ --python 3.9
 conda-build . --output-folder conda_build/ --python 3.10
+conda-build . --output-folder conda_build/ --python 3.11
+conda-build . --output-folder conda_build/ --python 3.12
 conda convert -f --platform all conda_build/linux-64/*.tar.bz2 -o conda_build

.github/workflows/release.yaml

@@ -50,7 +50,7 @@ jobs:
       # 1 runner per combination of (os, py)
       matrix:
         os: [ubuntu-latest, windows-latest, macos-latest]
-        py: ["cp37-*", "cp38-*", "cp39-*", "cp310-*"]
+        py: ["cp37-*", "cp38-*", "cp39-*", "cp310-*", "cp311-*", "cp312-*"]
       # All workers independent, don't cancel all if one fails
       fail-fast: false
 

.github/workflows/test.yaml

@@ -24,6 +24,7 @@ env:
 
   import-package-name: grakel
   extra-requires: "[dev]"  # "" for no extra_requires
+  extra-requires-soft: "[test]"  # "" for no extra_requires
   test-dir: grakel/tests
 
   # https://github.com/eddiebergman/GraKeL/blob/63a2723fc9488257a7c880fa9b5e5cc95ada9f42/ci_scripts/travis/install.sh#L8-L11
@@ -47,7 +48,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        py: ["3.7", "3.8", "3.9", "3.10"]
+        py: ["3.7", "3.8", "3.9", "3.10", "3.11", "3.12"]
         os: ["ubuntu-latest", "macos-latest", "windows-latest"]
 
     steps:
@@ -62,7 +63,12 @@ jobs:
     - name: Install ${{ env.import-package-name }}
       run: |
         python -m pip install --upgrade pip setuptools wheel
-        python -m pip install -e ".${{ env.extra-requires }}"
+        # escape for cvxopt
+        if ( [[ "${{ matrix.os }}" == "windows-latest" ]] && [[ "${{ matrix.py }}" == "3.7" ]] ) || [[ "${{ matrix.py }}" == "3.12" ]];then
+          python -m pip install -e ".${{ env.extra-requires-soft }}"
+        else
+          python -m pip install -e ".${{ env.extra-requires }}"
+        fi
 
     - name: Tests
       run: |

grakel/__init__.py

@@ -36,6 +36,7 @@
 from grakel.utils import graph_from_networkx
 from grakel.utils import graph_from_pandas
 from grakel.utils import graph_from_csv
+from grakel.utils import graph_from_torch_geometric
 
 __all__ = [
     "datasets",
@@ -67,6 +68,7 @@
     "graph_from_networkx",
     "graph_from_pandas",
     "graph_from_csv",
+    "graph_from_torch_geometric",
     "KMTransformer",
     "cross_validate_Kfold_SVM"
 ]

grakel/graph.py

@@ -3,6 +3,7 @@
 from __future__ import absolute_import
 import numbers
 import warnings
+import copy
 
 import numpy as np
 
@@ -773,7 +774,6 @@
                             raise ValueError('Graph does not have any labels for edges.')
                 return self.edge_labels
             else:
-
                 raise ValueError('label type can only be "vertex" or "edge"')
 
     def get_label_group(self, label_type="vertex", purpose="dictionary"):
@@ -1479,6 +1479,62 @@
 
         return subgraph
 
+    def copy(self):
+        """Returns a copy of the `self` graph.
+
+        Parameters
+        ----------
+        None.
+
+        Returns
+        -------
+        copy : graph
+            The copied graph.
+
+        """
+        return copy.deepcopy(self)
+
+    def clone(self):
+        """Alias for copy function.
+
+        Parameters
+        ----------
+        None.
+
+        Returns
+        -------
+        clone : graph
+            The cloned graph.
+
+        """
+        return self.copy()
+
+    def __repr__(self):
+        output = ["#vertices"]
+        output += [','.join(map(str, self.get_vertices('any')))]
+
+        output += ["#edges"]
+        output += ['\n'.join(map(lambda x: str(x[0]) + ',' + str(x[1]), self.get_edges('any')))]
+
+        def list_repr(x):
+            # convert numpy to list
+            if type(x) in [np.array, np.ndarray]:
+                x = x.tolist()
+            elif isinstance(x, Iterable):
+                x = list(x)
+            else:
+                return str(x)
+            return '[' + ','.join(map(str, x)) + ']'
+
+        if bool(self.node_labels):
+            output += ["#node_labels"]
+            output += ['\n'.join(map(lambda x: str(x[0]) + '->'+ list_repr(x[1]), self.node_labels.items()))]
+
+        if bool(self.edge_labels):
+            output += ["#edge_labels"]
+            output += ['\n'.join(map(lambda x: str(x[0][0]) + ',' + str(x[0][1]) + '->' + list_repr(x[1]), self.edge_labels.items()))]
+
+        return '\n'.join(output)
 
 def is_adjacency(g, transform=False):
     """Define if input is in a valid adjacency matrix format.

grakel/kernels/graphlet_sampling.py

@@ -283,7 +283,7 @@ def transform(self, X):
             km /= np.sqrt(np.outer(Y_diag, X_diag))
         return km
 
-    def fit_transform(self, X):
+    def fit_transform(self, X, y=None):
         """Fit and transform, on the same dataset.
 
         Parameters

grakel/kernels/kernel.py

@@ -167,7 +167,7 @@ def transform(self, X):
             km /= np.sqrt(np.outer(Y_diag, X_diag))
         return km
 
-    def fit_transform(self, X):
+    def fit_transform(self, X, y=None):
         """Fit and transform, on the same dataset.
 
         Parameters

grakel/kernels/neighborhood_hash.py

@@ -208,7 +208,7 @@ def fit(self, X, y=None):
         # Return the transformer
         return self
 
-    def fit_transform(self, X):
+    def fit_transform(self, X, y=None):
         """Fit and transform, on the same dataset.
 
         Parameters

grakel/kernels/neighborhood_subgraph_pairwise_distance.py

@@ -283,7 +283,7 @@ def transform(self, X, y=None):
             S /= np.sqrt(np.outer(*self.diagonal()))
         return S
 
-    def fit_transform(self, X):
+    def fit_transform(self, X, y=None):
         """Fit and transform, on the same dataset.
 
         Parameters
@@ -311,7 +311,10 @@ def fit_transform(self, X):
             K = M.dot(M.T).toarray()
             K_diag = K.diagonal()
             N[key] = K_diag
-            S += np.nan_to_num(K / np.sqrt(np.outer(K_diag, K_diag)))
+            Q = K / np.sqrt(np.outer(K_diag, K_diag))
+            np.fill_diagonal(Q, np.nan_to_num(np.diag(Q), nan=1.))
+            Q = np.nan_to_num(Q)
+            S = S + Q
 
         self._X_level_norm_factor = N
 

grakel/kernels/random_walk.py

@@ -102,14 +102,14 @@
         if not self._initialized["p"]:
             if self.p is not None:
                 if type(self.p) is int and self.p > 0:
-                    if self.kernel_type == "geometric":
+                    if self.kernel_type == "exponential":
                         self.mu_ = [1]
                         fact = 1
                         power = 1
                         for k in range(1, self.p + 1):
                             fact *= k
                             power *= self.lamda
-                            self.mu_.append(fact/power)
+                            self.mu_.append(power/fact)
                     else:
                         self.mu_ = [1]
                         power = 1
@@ -437,7 +437,7 @@
                 P = np.eye(XY.shape[0])
                 S = self.mu_[0] * P
                 for k in self.mu_[1:]:
-                    P *= XY
+                    P = np.matmul(P, XY)
                     S += k*P
             elif self.kernel_type == "exponential":
                 S = expm(self.lamda*XY).T

grakel/kernels/weisfeiler_lehman.py

@@ -336,8 +336,7 @@
                 raise ValueError('input must be an iterable\n')
             else:
                 nx = 0
-                distinct_values = set()
-                Gs_ed, L = dict(), dict()
+                Gs_ed, L, distinct_values, extras = dict(), dict(), set(), dict()
                 for (i, x) in enumerate(iter(X)):
                     is_iter = isinstance(x, Iterable)
                     if is_iter:
@@ -348,17 +347,34 @@
                                           + str(i))
                             continue
 
-                        elif len(x) in [2, 3]:
-                            x = Graph(x[0], x[1], {}, self._graph_format)
+                        else:
+                            if len(x) > 2:
+                                extra = tuple()
+                                if len(x) > 3:
+                                    extra = tuple(x[3:])
+                                x = Graph(x[0], x[1], x[2], graph_format=self._graph_format)
+                                extra = (x.get_labels(purpose=self._graph_format, label_type="edge", return_none=True),) + extra
+                            else:
+                                x = Graph(x[0], x[1], {}, graph_format=self._graph_format)
+                                extra = tuple()
+
                     elif type(x) is Graph:
                         x.desired_format("dictionary")
+                        el = x.get_labels(purpose=self._graph_format, label_type="edge", return_none=True)
+                        if el is None:
+                            extra = tuple()
+                        else:
+                            extra = (el, )
                     else:
-                        raise ValueError('each element of X must have at ' +
-                                         'least one and at most 3 elements\n')
+                        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')
                     Gs_ed[nx] = x.get_edge_dictionary()
                     L[nx] = x.get_labels(purpose="dictionary")
 
                     # Hold all the distinct values
+                    extras[nx] = extra
                     distinct_values |= set(
                         v for v in itervalues(L[nx])
                         if v not in self._inv_labels[0])
@@ -382,7 +398,7 @@
                         new_labels[k] = WL_labels_inverse[v]
                 L[j] = new_labels
                 # produce the new graphs
-                new_graphs.append([Gs_ed[j], new_labels])
+                new_graphs.append((Gs_ed[j], new_labels) + extras[j])
             yield new_graphs
 
             for i in range(1, self._n_iter):
@@ -418,7 +434,7 @@
                             new_labels[k] = WL_labels_inverse[v]
                     L[j] = new_labels
                     # Create the new graphs with the new labels.
-                    new_graphs.append([Gs_ed[j], new_labels])
+                    new_graphs.append((Gs_ed[j], new_labels) + extras[j])
                 yield new_graphs
 
         if self._parallel is None: