Merge pull request #27 from jajupmochi/v0.2

V0.2

Author: jajupmochi

README.md

@@ -62,44 +62,44 @@ The docs of the library can be found [here](https://graphkit-learn.readthedocs.i
 
 ## Main contents
 
-### List of graph kernels
+### 1 List of graph kernels
 
 * Based on walks
-  * The common walk kernel [1]
+  * [The common walk kernel](gklearn/kernels/common_walk.py) [1]
     * Exponential
     * Geometric
-  * [The marginalized kenrel](https://github.com/jajupmochi/graphkit-learn/blob/master/gklearn/kernels/marginalized.py)
+  * [The marginalized kenrel](gklearn/kernels/marginalized.py)
     * With tottering [2]
     * Without tottering [7]
-  * The generalized random walk kernel [3]
-    * Sylvester equation
+  * [The generalized random walk kernel](gklearn/kernels/random_walk.py) [3]
+    * [Sylvester equation](gklearn/kernels/sylvester_equation.py)
     * Conjugate gradient
     * Fixed-point iterations
-    * Spectral decomposition
+    * [Spectral decomposition](gklearn/kernels/spectral_decomposition.py)
 * Based on paths
-  * [The shortest path kernel](https://github.com/jajupmochi/graphkit-learn/blob/master/gklearn/kernels/shortest_path.py) [4]
-  * [The structural shortest path kernel](https://github.com/jajupmochi/graphkit-learn/blob/master/gklearn/kernels/structural_sp.py) [5]
-  * [The path kernel up to length h](https://github.com/jajupmochi/graphkit-learn/blob/master/gklearn/kernels/path_up_to_h.py) [6]
+  * [The shortest path kernel](gklearn/kernels/shortest_path.py) [4]
+  * [The structural shortest path kernel](gklearn/kernels/structural_sp.py) [5]
+  * [The path kernel up to length h](gklearn/kernels/path_up_to_h.py) [6]
     * The Tanimoto kernel
     * The MinMax kernel
 * Non-linear kernels
-  * [The treelet kernel](https://github.com/jajupmochi/graphkit-learn/blob/master/gklearn/kernels/treelet.py) [10]
-  * [Weisfeiler-Lehman kernel](https://github.com/jajupmochi/graphkit-learn/blob/master/gklearn/kernels/weisfeiler_lehman.py) [11]
-    * [Subtree](https://github.com/jajupmochi/graphkit-learn/blob/master/gklearn/kernels/weisfeiler_lehman.py#L479)
+  * [The treelet kernel](gklearn/kernels/treelet.py) [10]
+  * [Weisfeiler-Lehman kernel](gklearn/kernels/weisfeiler_lehman.py) [11]
+    * [Subtree](gklearn/kernels/weisfeiler_lehman.py#L479)
 
 A demo of computing graph kernels can be found on [Google Colab](https://colab.research.google.com/drive/17Q2QCl9CAtDweGF8LiWnWoN2laeJqT0u?usp=sharing) and in the [`examples`](https://github.com/jajupmochi/graphkit-learn/blob/master/gklearn/examples/compute_graph_kernel.py) folder.
 
-### Graph Edit Distances
+### 2 Graph Edit Distances
 
-### Graph preimage methods
+### 3 Graph preimage methods
 
 A demo of generating graph preimages can be found on [Google Colab](https://colab.research.google.com/drive/1PIDvHOcmiLEQ5Np3bgBDdu0kLOquOMQK?usp=sharing) and in the [`examples`](https://github.com/jajupmochi/graphkit-learn/blob/master/gklearn/examples/median_preimege_generator.py) folder.
 
-### Interface to `GEDLIB`
+### 4 Interface to `GEDLIB`
 
-[`GEDLIB`](https://github.com/dbblumenthal/gedlib) is an easily extensible C++ library for (suboptimally) computing the graph edit distance between attributed graphs. [A Python interface](https://github.com/jajupmochi/graphkit-learn/tree/master/gklearn/gedlib) for `GEDLIB` is integrated in this library, based on [`gedlibpy`](https://github.com/Ryurin/gedlibpy) library.
+[`GEDLIB`](https://github.com/dbblumenthal/gedlib) is an easily extensible C++ library for (suboptimally) computing the graph edit distance between attributed graphs. [A Python interface](gklearn/gedlib) for `GEDLIB` is integrated in this library, based on [`gedlibpy`](https://github.com/Ryurin/gedlibpy) library.
 
-### Computation optimization methods
+### 5 Computation optimization methods
 
 * Python’s `multiprocessing.Pool` module is applied to perform **parallelization** on the computations of all kernels as well as the model selection.
 * **The Fast Computation of Shortest Path Kernel (FCSP) method** [8] is implemented in *the random walk kernel*, *the shortest path kernel*, as well as *the structural shortest path kernel* where FCSP is applied on both vertex and edge kernels.

gklearn/kernels/__init__.py

@@ -18,3 +18,13 @@
 from gklearn.kernels.path_up_to_h import PathUpToH
 from gklearn.kernels.treelet import Treelet
 from gklearn.kernels.weisfeiler_lehman import WeisfeilerLehman, WLSubtree
+
+# old version.
+from gklearn.kernels.commonWalkKernel import commonwalkkernel
+from gklearn.kernels.marginalizedKernel import marginalizedkernel
+from gklearn.kernels.randomWalkKernel import randomwalkkernel
+from gklearn.kernels.spKernel import spkernel
+from gklearn.kernels.structuralspKernel import structuralspkernel
+from gklearn.kernels.untilHPathKernel import untilhpathkernel
+from gklearn.kernels.treeletKernel import treeletkernel
+from gklearn.kernels.weisfeilerLehmanKernel import weisfeilerlehmankernel

gklearn/utils/__init__.py

@@ -24,3 +24,4 @@
 from gklearn.utils.utils import normalize_gram_matrix, compute_distance_matrix
 from gklearn.utils.trie import Trie
 from gklearn.utils.knn import knn_cv, knn_classification
+from gklearn.utils.model_selection_precomputed import model_selection_for_precomputed_kernel