Conversion to PyWLGK (pt. 2)

.github/workflows/publish.yaml

@@ -1,4 +1,4 @@
-name: Publish PyWLK to Conda
+name: Publish PyWLGK to Conda
 
 on:
   release:
@@ -33,19 +33,19 @@ jobs:
       run: |
         conda install -y anaconda-client conda-build git
 
-    - name: Build PyWLK for linux-64
+    - name: Build PyWLGK for linux-64
       shell: bash -l {0}
       run: |
         conda-build -q -c conda-forge -c bioconda --output-folder . --no-test recipe
 
-    - name: Convert PyWLK to osx-64, osx-arm64, win-64
+    - name: Convert PyWLGK to osx-64, osx-arm64, win-64
       shell: bash -l {0}
       run: |
         conda convert -p osx-64 linux-64/*.tar.bz2
         conda convert -p osx-arm64 linux-64/*.tar.bz2
         conda convert -p win-64 linux-64/*.tar.bz2
 
-    - name: Publish PyWLK to Conda
+    - name: Publish PyWLGK to Conda
       env:
         ANACONDA_TOKEN: ${{ secrets.ANACONDA_TOKEN }}
       shell: bash -l {0}

.github/workflows/publish_pip.yaml

@@ -1,4 +1,4 @@
-name: Publish PyWLK to PyPI
+name: Publish PyWLGK to PyPI
 
 on:
   release:
@@ -25,7 +25,7 @@ jobs:
         run: |
           pip install --upgrade build twine
 
-      - name: Publish PyWLK to PyPI
+      - name: Publish PyWLGK to PyPI
         env:
           PYPI_TOKEN: ${{ secrets.PYPI_TOKEN }}
         shell: bash -l {0}

README.md

@@ -1,11 +1,11 @@
-# PyWLK
+# PyWLGK
 
 Python implementation of the Weisfeiler-Lehman Graph Kernels (WLKs) method.
 This package is an alternative to FastWLK, GraKel, and other implementations of the WLKs method.
 
 ## Installation
 
-By design, PyWLK is installable from PyPI and Anaconda. To install the package with `pip`, run the following command:
+By design, PyWLGK is installable from PyPI and Anaconda. To install the package with `pip`, run the following command:
 
 ```bash
 pip install pywlgk
@@ -19,7 +19,7 @@ conda install pywlgk
 
 ## Usage
 
-PyWLK is designed to be easy to use. The following example shows how to use PyWLK to compute the WLKs kernel between 
+PyWLGK is designed to be easy to use. The following example shows how to use PyWLGK to compute the WLKs kernel between 
 two graphs.
 
 ```python
@@ -32,6 +32,6 @@ labels = np.ones((2, 10), dtype=np.int32)
 wlk(adjs, labels, k=4)
 ```
 
-PyWLK takes as input a stack of adjacency matrices (`adjs`) and a stack of node labels (`labels`). The adjacency 
+PyWLGK takes as input a stack of adjacency matrices (`adjs`) and a stack of node labels (`labels`). The adjacency 
 matrices must be symmetric, whereas the labels can have any type. Additionally, one can specify a `k` to control how 
 many iterations of the kernel will be computed.

docs/conf.py

@@ -30,7 +30,7 @@
 master_doc = "index"
 
 author = "Roman Joeres"
-project = "PyWLK"
+project = "PyWLGK"
 copyright = f"{datetime.datetime.now().year}, {author}"
 
 version = list(open("../pyproject.toml", "r").readlines())[2].strip().split("\"")[1].split("\"")[0]

docs/index.rst

@@ -1,14 +1,14 @@
-Welcome to the documentation of PyWLK
-=====================================
+Welcome to the documentation of PyWLGK
+======================================
 
-PyWLK is a Python implementation of the Weisfeiler-Lehman Graph Kernels (WLKs). It is based on the paper by ? et al.
-The main improvement over other tools is that PyWLK can be installed from PyPI and Anaconda for any current python
+PyWLGK is a Python implementation of the Weisfeiler-Lehman Graph Kernels (WLKs). It is based on the paper by ? et al.
+The main improvement over other tools is that PyWLGK can be installed from PyPI and Anaconda for any current python
 version, i.e. Python 3.8 and newer.
 
 Installation
 ------------
 
-PyWLK can be installed from PyPI using pip:
+PyWLGK can be installed from PyPI using pip:
 
 .. code-block:: bash
 
@@ -23,7 +23,7 @@ or from Anaconda using conda:
 Usage
 -----
 
-After installation, PyWLK can be used as follows:
+After installation, PyWLGK can be used as follows:
 
 .. code-block:: python
 
@@ -35,7 +35,7 @@ After installation, PyWLK can be used as follows:
     labels = np.ones((2, 10), dtype=np.int32)
     wlk(adjs, labels, k=4)
 
-PyWLK takes as input a stack of adjacency matrices (:code:`adjs`) and a stack of node labels (:code:`labels`). The
+PyWLGK takes as input a stack of adjacency matrices (:code:`adjs`) and a stack of node labels (:code:`labels`). The
 adjacency matrices must be symmetric, whereas the labels can have any type. Additionally, one can specify a :code:`k`
 to control how many iterations of the kernel will be computed.
 

examples/Random_Graph.ipynb

@@ -3,9 +3,9 @@
   {
    "cell_type": "markdown",
    "source": [
-    "# How to use PyWLK\n",
+    "# How to use PyWLGK\n",
     "\n",
-    "In this tutorial, we will show how to use PyWLK to compute the WL kernel. We will first create two random graphs and then compute the WL kernel between them."
+    "In this tutorial, we will show how to use PyWLGK to compute the WL kernel. We will first create two random graphs and then compute the WL kernel between them."
    ],
    "metadata": {
     "collapsed": false
@@ -109,14 +109,14 @@
   {
    "cell_type": "markdown",
    "source": [
-    "### How to use PyWLK\n",
+    "### How to use PyWLGK\n",
     "\n",
-    "Now we can use PyWLK to compute the WL kernel. The two arguments are a list of adjacency matrices and a list of labels. The labels can have any type. The last argument is the number of iterations of the WL algorithm. The default value is 4.\n",
+    "Now we can use PyWLGK to compute the WL kernel. The two arguments are a list of adjacency matrices and a list of labels. The labels can have any type. The last argument is the number of iterations of the WL algorithm. The default value is 4.\n",
     "\n",
     "\n",
     "#### Default parameters\n",
     "\n",
-    "We will demonstrate how different parameters affect the output. By default, PyWLK does not normalize the output and uses the dot-product to compute the similarities of graphs."
+    "We will demonstrate how different parameters affect the output. By default, PyWLGK does not normalize the output and uses the dot-product to compute the similarities of graphs."
    ],
    "metadata": {
     "collapsed": false

pyproject.toml

@@ -1,5 +1,5 @@
 [tool.poetry]
-name = "PyWLK"
+name = "PyWLGK"
 version = "1.0.0"
 description = "Python implementation of Weisfeiler-Lehman Graph Kernels"
 authors = ["Roman Joeres <joeres.roman@web.de>"]

recipe/meta.yaml

@@ -16,7 +16,7 @@ requirements:
     - numpy
 
 about:
-  home: https://github.com/Old-Shatterhand/PyWLK
+  home: https://github.com/Old-Shatterhand/PyWLGK
   license: MIT
   license_file: LICENSE
   summary: "Python implementation of the WL kernel to be installable from conda and pip for all major platforms and python versions."