python 3.6 compatablity

example.py

@@ -7,12 +7,12 @@
 X = np.array([[1,1], [0,0], [1,0], [0,1]])
 y = np.array([1, 1, 0, 0])
 
-print 'Testing XOR'
+print ('Testing XOR')
 
 for clf, name in [(SVC(kernel=RBF(), C=1000), 'pykernel'), (SVC(kernel='rbf', C=1000), 'sklearn')]:
     clf.fit(X, y)
-    print name
-    print clf
-    print 'Predictions:', clf.predict(X)
-    print 'Accuracy:', accuracy_score(clf.predict(X), y)
-    print
+    print (name)
+    print (clf)
+    print ('Predictions:', clf.predict(X))
+    print( 'Accuracy:', accuracy_score(clf.predict(X), y))
+    print()

pykernels/__init__.py

@@ -1,6 +1,6 @@
-from basic import Linear, Polynomial, RBF
-from regular import Exponential, Laplacian, RationalQuadratic, InverseMultiquadratic, Cauchy, TStudent,\
+from .basic import Linear, Polynomial, RBF
+from .regular import Exponential, Laplacian, RationalQuadratic, InverseMultiquadratic, Cauchy, TStudent,\
                     ANOVA, Spline, Min, Log, Power, Chi2, AdditiveChi2, GeneralizedHistogramIntersection,\
                     Tanimoto, Sorensen, MinMax, Wavelet, Fourier
-from graph.randomwalk import RandomWalk
-from graph.allgraphlets import All34Graphlets
+from .graph.randomwalk import RandomWalk
+from .graph.allgraphlets import All34Graphlets

pykernels/basic.py

@@ -5,9 +5,9 @@
 
 __author__ = 'lejlot'
 
-from pykernels.base import Kernel
+from .base import Kernel
 import numpy as np
-from utils import euclidean_dist_matrix
+from .utils import euclidean_dist_matrix
 
 class Linear(Kernel):
     """

pykernels/graph/__init__.py

@@ -1,3 +1,3 @@
-from randomwalk import RandomWalk
-from allgraphlets import All34Graphlets
-from shortestpath import ShortestPath
+from .randomwalk import RandomWalk
+from .allgraphlets import All34Graphlets
+from .shortestpath import ShortestPath

pykernels/graph/allgraphlets.py

@@ -7,7 +7,7 @@
 import itertools
 import numpy as np
 from pykernels.base import Kernel, GraphKernel
-import basic
+from . import basic
 
 def dec2bin(k, bitlength=0):
     """Decimal to binary"""

pykernels/graph/basic.py

@@ -21,10 +21,10 @@ def graphs_to_adjacency_lists(data):
     try:
         if data.ndim == 3:
             return np.array(data)
-    except Exception, exc:
+    except Exception:
         try:
             return np.array([G.adjacency_matix for G in data])
-        except Exception, exc:
+        except Exception:
             return np.array(data)
 
 def relabel(data, data_2):

pykernels/graph/randomwalk.py

@@ -8,7 +8,7 @@
 from pykernels.base import Kernel, GraphKernel
 from scipy.sparse import lil_matrix, kron,identity
 from scipy.sparse.linalg import lsqr
-import basic
+from . import basic
 
 def _norm(adj_mat):
     """Normalize adjacency matrix"""

pykernels/graph/shortestpath.py

@@ -5,7 +5,7 @@
 
 import numpy as np
 import numpy.matlib as matlib
-import basic
+from . import basic
 from pykernels.base import Kernel, GraphKernel
 from scipy.sparse import lil_matrix
 

pykernels/regular.py

@@ -5,9 +5,9 @@
 
 __author__ = 'lejlot'
 
-from pykernels.base import Kernel
+from .base import Kernel
 import numpy as np
-from utils import euclidean_dist_matrix
+from .utils import euclidean_dist_matrix
 import warnings
 
 class Cossim(Kernel):

setup.py

@@ -28,7 +28,7 @@
             'Intended Audience ::  Machine Learning Research',
             'License :: OSI Approved ::' + LICENSE,
             'Programming Language :: Python :: 2.7',
-            'Programming Language :: Python :: 3',
+            'Programming Language :: Python :: 3.6',
             'Operating System :: OS Independent',
             'Topic :: Software Development :: Libraries :: Python Modules',
             'Topic :: Scientific/Engineering :: Machine Learning',