https://github.com/stellargraph/stellar-practical-ml-on-graphs
Course titled Practical Machine Learning on Graphs
https://github.com/stellargraph/stellar-practical-ml-on-graphs
Last synced: about 1 year ago
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Course titled Practical Machine Learning on Graphs
- Host: GitHub
- URL: https://github.com/stellargraph/stellar-practical-ml-on-graphs
- Owner: stellargraph
- Created: 2019-01-30T22:58:30.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2020-11-13T18:42:02.000Z (over 5 years ago)
- Last Synced: 2023-03-06T12:51:58.949Z (over 3 years ago)
- Language: Jupyter Notebook
- Homepage: https://www.stellargraph.io/
- Size: 58.8 MB
- Stars: 41
- Watchers: 16
- Forks: 15
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
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README
# Practical Machine Learning on Graphs course
## Overview
This course introduces graph machine learning, with a practical focus based on the [StellarGraph library](https://github.com/stellargraph/stellargraph).
The course slides are available in [the `slides/` directory](slides).
## Preparation
The main requirements are **Python 3.6 or 3.7** and a set of python modules listed in the file **requirements.txt**
with the correct module version numbers.
We provide installation instructions for Windows 10 and MacOS. The latter can be used as a guide for common Linux
distributions such as Ubuntu. We expect that users have some experience programming in Python and using basic tools
such as pip and git.
### Instructions for MacOS
[1] Install a Python 3 (version 3.6 or 3.7) distribution. We recommend
Anaconda that can be downloaded by clicking
[here.](https://conda.io/docs/user-guide/install/download.html) Alternatively, install
Python 3.6 from [here](https://www.python.org/downloads/release/python-369/) and also
`virtualenv` via the command `pip install virtualenv`.
[2] Download and install the latest version of `git` from [here.](https://git-scm.com/download/mac)
[3] Create a working directory, e.g., `/Users/YOUR-USER-NAME/Projects`, where `YOUR_USER_NAME`
should be replaced with your user name and `Projects` is the new working directory. From this
point on we will use `HOME` to refer to `/Users/YOUR-USER_NAME` such that the working
directory will be `HOME/Projects`
[4] Create a custom environment using the command (if using conda)
`conda create --name practical-ml python=3.6`
Alternatively, if you are not using Anaconda and conda, you can create a new virtual
environment using `virtualenv`. Instructions on how to install and use `virtualenv` can be
found [here.](https://virtualenv.pypa.io/en/stable/)
`virtualenv practical-ml`
The above command will create a new Python environment at location `HOME/Projects/practical-ml`
[5] Create the file `matplotlibrc` in directory `~/.matplotlib`; create the directory if it does
not exist in your system. The file contents should be,
`backend:TkAgg`
[6] Activate the new virtual environment created with `conda` by executing the command,
`source activate practical-ml`
or if created using `virtualenv` use the command,
`source practical-ml/bin/activate`
[7] Download or clone the course source code repository using the command (from the `HOME/Projects` directory),
`git clone https://github.com/stellargraph/stellar-practical-ml-on-graphs.git`
[8] Change to the `stellar-practical-ml-on-graphs` directory and install the python requirements using the command,
`pip install -r requirements.txt`
[9] You also need to register the `practical-ml` environment so that it is available in `Jupyter`. You
should use the following command,
`python -m ipykernel install --user --name=practical-ml`
[10] You can verify that you have the correct version of stellargraph installed by using the command,
`python -c "import stellargraph as sg; print(sg.__version__)"`
Pay attention to the double quotes and the __ is two underscores (before and after the word version.) The above command
should print
`0.10.0`
[11] You can now run jupyter notebook using the command,
`jupyter notebook`
You can access the course notebooks using your web browser at localhost:8888
**The installation should now be complete.**
### Instructions for Windows 10
[1] Install a Python 3 (version 3.6 or 3.7) distribution. You can download the recommended
version from [here](https://www.python.org/downloads/release/python-362/). Make sure to select the
option to **Add Python 3.6 to PATH** on the Setup screen.
[2] Open a Windows Command Prompt and run the following command,
`python --version`
if the installation was successful then it should print,
`Python 3.6.2`
The default Python distribution also includes `pip`. On the Command Prompt type the following
command,
`pip --version`
if the installation was successful then it should print the pip version,
`pip 19.0.2`
[3] Install `virtualenv` using the following command,
`pip install virtualenv`
[4] Install `Jupyter Notebook` using the command,
`pip install jupyter`
[5] Download and install the latest version of `git` from [here.](https://git-scm.com/download/win)
[6] Create a working directory, e.g., `C:\users\YOUR-USER-NAME\Projects`, where `YOUR_USER_NAME`
should be replaced with your user name and `Projects` is the new working directory. From this
point on we will use `HOME` to refer to `C:\users\YOUR-USER_NAME` such that the working
directory will be `HOME\Projects`
[7] Change to the `HOME\Projects` directory and create a new Python virtual environment called `practical-ml`
using the following command,
`virtualenv practical-ml`
This will create a new folder `HOME\Projects\practical-ml`
[8] Activate the new environment using the command,
`practical-ml\Scripts\activate`
[9] Download or clone the `stellar-practical-ml-on-graphs` repository (in `HOME\Projects`) using the
command,
`git clone https://github.com/stellargraph/stellar-practical-ml-on-graphs.git`
[10] Change to the `stellar-practical-ml-on-graphs` directory and install the python requirements using
the command,
`pip install -r requirements.txt`
[12] You also need to register the `practical-ml` environment so that it is available in `Jupyter`.
You should use the following command,
`python -m ipykernel install --user --name=practical-ml`
[13] You can verify that you have the correct version of `stellargraph` installed by using the command,
`python -c “import stellargraph as sg; print(sg.__version__)”`
Pay attention to the double quotes and the __ is two underscores (before and after the word version.) The above command should print
`0.10.0`
[14] You can now run jupyter notebook using the command,
`jupyter notebook`
You can access the course notebooks using your web browser at localhost:8888
**The installation should now be complete.**
## License
Copyright 2010-2020 Commonwealth Scientific and Industrial Research Organisation (CSIRO).
All Rights Reserved.
NOTICE: All information contained herein remains the property of the CSIRO. The intellectual and technical concepts
contained herein are proprietary to the CSIRO and are protected by copyright law. Dissemination of this information
or reproduction of this material is strictly forbidden unless prior written permission is obtained from the CSIRO.