Updated installation guide with conda

Author: kinkunchan

README.md

@@ -9,60 +9,58 @@ The library itself has already included a set of popular methods and datasets re
 - Built-in methods: [node2vec](https://github.com/aditya-grover/node2vec), [struc2vec](https://github.com/leoribeiro/struc2vec), [GraphWave](https://github.com/snap-stanford/graphwave), [xNetMF](https://github.com/GemsLab/REGAL), [role2vec](https://github.com/benedekrozemberczki/role2vec), [DRNE](https://github.com/tadpole/DRNE), [MultiLENS](https://github.com/GemsLab/MultiLENS), [RiWalk](github.com/maxuewei2/RiWalk), [SEGK](https://github.com/giannisnik/segk), (more methods to add in the near future)
 
 - Built-in datasets: 
-
-  | Dataset                                                      | # Nodes | # Edges |
-  | ------------------------------------------------------------ | ------- | ------- |
-  | [BlogCatalog](http://snap.stanford.edu/node2vec/)            | 10,312  | 333,983 |
-  | [Facebook](http://snap.stanford.edu/data/egonets-Facebook.html) | 4,039   | 88,234  |
+  
+  | Dataset                                                                                  | # Nodes | # Edges |
+  | ---------------------------------------------------------------------------------------- | ------- | ------- |
+  | [BlogCatalog](http://snap.stanford.edu/node2vec/)                                        | 10,312  | 333,983 |
+  | [Facebook](http://snap.stanford.edu/data/egonets-Facebook.html)                          | 4,039   | 88,234  |
   | [ICEWS](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/QI2T9A) | 1,255   | 1,414   |
-  | [PPI](snap.stanford.edu/graphsage/)                          | 56,944  | 818,786 |
-  | [BR air-traffic](https://github.com/leoribeiro/struc2vec/tree/master/graph) | 131     | 1,038   |
-  | [EU air-traffic](https://github.com/leoribeiro/struc2vec/tree/master/graph) | 399     | 5,995   |
-  | [US air-traffic](https://github.com/leoribeiro/struc2vec/tree/master/graph) | 1,190   | 13,599  |
-  | [DD6](https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets) | 4,152   | 20,640  |
-  | Synthetic Datasets                                           |         |         |
+  | [PPI](snap.stanford.edu/graphsage/)                                                      | 56,944  | 818,786 |
+  | [BR air-traffic](https://github.com/leoribeiro/struc2vec/tree/master/graph)              | 131     | 1,038   |
+  | [EU air-traffic](https://github.com/leoribeiro/struc2vec/tree/master/graph)              | 399     | 5,995   |
+  | [US air-traffic](https://github.com/leoribeiro/struc2vec/tree/master/graph)              | 1,190   | 13,599  |
+  | [DD6](https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets)               | 4,152   | 20,640  |
+  | Synthetic Datasets                                                                       |         |         |
 
-The library requires *Python 3.7* for best usage. In *Python 3.8*, the Tensorflow 1.14.0 used in DRNE might not be successfully installed.
+The library requires *Python 3.6.2 for best usage. In *Python 3.8*, the Tensorflow 1.14.0 used in DRNE might not be successfully installed.
 
 ## Installation and Usage
 
 Make sure you are using *Python 3.6+* for all below!
 
-1. First, creat a virtual environment and activate the virtual environment.
-
+1. First, creat a virtual environment and activate the virtual environment using conda
+   
    ```bash
-   python3 -m venv <VENV_NAME>
-   source <VENV_NAME>/bin/activate
+   conda create -n "<VENV_NAME>" python=3.6.2 ipython
+   conda activate <VENV_NAME>
    ```
 
 2. Change directory to the `StrucEmbeddingLibrary` and install the dependencies
-
+   
    ```bash
    (<VENV_NAME>) cd StrucEmbeddingLibrary
    (<VENV_NAME>) python3 -m pip install -r requirements.txt --no-cache-dir
    ```
 
 3. Install the `SEMB` package
-
+   
    ```bash
    (<VENV_NAME>) cd StrucEmbeddingLibrary
    (<VENV_NAME>) python3 setup.py install
    ```
-
+   
    After installation, we  highly recommend you go through our [Tutorial](https://github.com/GemsLab/StrucEmbeddingLibrary/blob/master/Tutorial.ipynb) to see how SEMB library works.
 
 4. To enable using the jupyter notebook, do the following,
-
+   
    ```bash
    (<VENV_NAME>) python3 -m pip install ipykernel --no-cache-dir
    (<VENV_NAME>) python3 -m ipykernel install --name=<VENV_NAME>
    (<VENV_NAME>) jupyter notebook
    ```
-
+   
    Choose `<VENV_NAME>` at the top right corner of the page when creating a new jupyter notebook / running the tutorial notebook.
 
-
-
 ## Extending SEMB
 
 First make sure the `semb` library is installed.
@@ -94,6 +92,7 @@ Currently, SEMB only supports embedding and evaluation on *undirected* and *unwe
 - Now the method is load-able by the main client program that uses `semb`!
 
 ### Note
+
 For both `dataset` and `method` extensions, make sure the `get_id()` to be overridden and returns the same id as your chosen id in your package name.
 
 ### Contact

Tutorial.ipynb

@@ -9,7 +9,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/Users/mark/GoogleDrive/UM/S4/GEMS/Git/SEMB_env/bin/python3\n",
+      "3.6.2 (v3.6.2:5fd33b5926, Jul 16 2017, 20:11:06) \n",
+      "[GCC 4.2.1 (Apple Inc. build 5666) (dot 3)]\n",
+      "sys.version_info(major=3, minor=6, micro=2, releaselevel='final', serial=0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "print(sys.executable)\n",
+    "print(sys.version)\n",
+    "print(sys.version_info)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -58,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -76,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -87,7 +110,7 @@
        " DatasetInfo(name='2 - US', description='European airport sample data', src_url='/Users/mark/GoogleDrive/UM/S4/GEMS/Git/StrucEmbeddingLibrary/semb/datasets/airports/../../../sample-data/Airports/airport_US/usa-airports.edgelist', label_url='/Users/mark/GoogleDrive/UM/S4/GEMS/Git/StrucEmbeddingLibrary/semb/datasets/airports/../../../sample-data/Airports/airport_US/airport_USA_label.txt')]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -105,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,16 +144,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<networkx.classes.graph.Graph at 0x7f879c8b1d68>"
+       "<networkx.classes.graph.Graph at 0x7f9648fb0f60>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -155,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {

requirements.txt

@@ -10,7 +10,6 @@ gensim==3.7.1
 tqdm==4.32.2
 texttable==1.6.2
 fastdtw==0.3.4
-sparsesvd==0.2.2
 grakel==0.1a6
 scikit-learn==0.21.3
 absl-py==0.11.0
@@ -38,11 +37,13 @@ protobuf==3.14.0
 pyparsing==2.4.7
 python-dateutil==2.8.1
 pytz==2020.4
-semb==0.0.1
 six==1.15.0
 smart-open==4.0.1
 termcolor==1.1.0
 threadpoolctl==2.1.0
 Werkzeug==1.0.1
 wrapt==1.12.1
-zipp==3.4.0
+zipp==3.4.0
+sparsesvd~=0.2.0
+requests~=2.27.1
+requests-file~=1.5.1