add new get_started

doc/source/conf.py

@@ -34,7 +34,8 @@ extensions = [
     'sphinx.ext.autodoc',
     'sphinx.ext.coverage',
     'sphinx.ext.napoleon',
-    'sphinx.ext.autosectionlabel'
+    'sphinx.ext.autosectionlabel',
+    'nbsphinx'
 ]
 
 # Add any paths that contain templates here, relative to this directory.

doc/source/get_started.rst

-Get Started
-===========
-
-Installation
-------------
-
-See :ref:`Installation`
-
-Generate a graph with a model
------------------------------
-
-We developed a serie of functions for generating graph based on the following models:
-
-* Spatial model
-* Stochastic Block Model
-* Configuration Model
-* Random (ER)
-* Mixed Model (Spatial + SBM
-
-For example, if you want to generate a graph based on a stochastic block model, use the following code :
-
-.. code-block:: python
-
-    from lib.random import stochastic_block_model_graph
-    G = stochastic_block_model_graph(nb_nodes=300,nb_edges=1200,nb_com=5,percentage_edge_betw=0.01)
-
-If you wish to generate a dataset containing generated graph with different configurations,
-you can use the script generate_random_graph.py` using the following command :
-
-.. code-block:: shell
-
-    python generate_theoric_random_graph.py <output_dir>
\ No newline at end of file

doc/source/index.rst

@@ -12,7 +12,7 @@ Summary
    :maxdepth: 2
 
    installation
-   get_started
+   get_started.ipynb
    graph_generator
    link_pred_eval
 

doc/source/installation.rst

 Installation
 ============
 
-To use the code in this repo, first you'll need to install Python requirements using the following command
+To simplify things, this installation guide is using conda environnement. Therefore, first, we must
+create a Python3.7 environnement
 
 .. code-block:: shell
 
-    pip install -r requirements.txt
+    conda create --name test_lp python=3.7
+    conda activate test_lp
+
+Then, you need to install graph-tool.
+
+.. code-block:: shell
+
+    conda install -c conda-forge graph-tool
+
+Once graph-tool is installed, we need to install OpenNE:
+
+.. code-block:: shell
 
+    git clone https://github.com/thunlp/OpenNE.git
+    cd OpenNE
+    conda install numpy networkx scipy tensorflow==1.14 gensim scikit-learn
+    cd src
+    python setup.py install
+    cd ..
+
+To verify if the installation went well, run the following command :
+
+.. code-block:: shell
+
+    python -m openne --help
 
 Then, install our custom version of EvalNE using pip
 
 .. code-block:: shell
 
-    pip install git+https://github.com/thunlp/OpenNE.git
     pip install git+https://github.com/Jacobe2169/EvalNE
 
 
-Finally, install the Python library `graph-tool
-<https://graph-tool.skewed.de>`_.
\ No newline at end of file
+Finally, To use the code in this repo, you'll need to install Python requirements using the following command
+
+.. code-block:: shell
+
+    pip install -r requirements.txt
+    pip install decorator==4.3
\ No newline at end of file