Abstract: In this workshop/tutorial, the audience will be guided through the first steps necessary for applying data analytics and machine learning to optical networks. The workshop will allow attendees to use the available development tools (e.g., Jupyter Lab), enable participants to perform data importing and selection (e.g., removing samples with missing features), as well as explore the correlation between the different features of the dataset. The tutorial will be concluded with an interactive discussion on the remaining challenges and ideas for extensions. Lunch will be included.
To find out more about this event, please visit the Congress website.
If you have any questions or concerns, or have any issues accessing the link, please email kmaclure@osa.org directly.
If you have any questions or issues while installing the platform necessary to follow this hands-on tutorial, please email carlos.natalino@chalmers.se directly.
During this tutorial, a set of usefull tools will be introduced to the attendees.
All the software used in this tutorial is open source and free. As an attendee, you need to install the following software:
- Git (official download page)
- Python 3.6 or 3.7 (we recommend the Anaconda distribution with Python 3.7 available here) -- a comprehensive installation tutorial is available here
After installing these software, you should be able to use the commands git and npm in your prompt (in Windows) or terminal (in Linux and MacOS).
Once Anaconda is installed, open the Anaconda prompt (in Windows) or the terminal (in Linux or MacOS) and run the following command line:
conda create --name handsontutorial numpy pandas scikit-learn matplotlib seaborn tensorflow keras jupyterlab xlrd nodejs
The above command is responsible for creating an environment named handsontutorial and installing the following packages:
- Pandas
- Scikit-Learn
- Matplotlib
- Seaborn
- Keras
- TensorFlow
- NumPy
- Jupyter Lab
- NodeJS
During the tutorial, we will use the variable inspector extension and explore its functionalities. This installation is optional and the tutorial can be followed without problems without this inspector. However, it is highly recommended that you also do this installation.
The variable inspector is available here. In the following, the installation procedures available in the official repository are reproduced:
- Enable the
handsontutorialenvironment by typing the following command in your Anaconda prompt (in Windows) or terminal (in Linux and MacOS):
conda activate handsontutorial
- Clone the git repository by typing the following command in your prompt (for Windows) or terminal (for Linix and MacOS):
git clone https://github.com/lckr/jupyterlab-variableInspector.git
- Once you downloaded the repository use the following steps:
cd jupyterlab-variableInspector
npm install
npm run build
jupyter labextension install .
- Confirm the installation of the extension with:
jupyter labextension list
which should print enabled and OK.
After the installation, we need to validate if everything is working. First, open the Anaconda Prompt (in Windows) or terminal (in Linux or MacOS). Then, type the following command
conda activate handsontutorial
which will load the environment created in the previous step. Then, navigate (using the cd command) to a folder of your preference, and type the following command:
git clone https://github.com/carlosnatalino/osa-networks-hands-on-ml.git
cd osa-networks-hands-on-ml
jupyter lab
After typing this command, you should have a browser window opened. In the screen, right-click on the file named validate-installation.ipynb and select open. Once the file is opened, click on the Run menu and select Run All Cells. You should see a screen similar to the one below.
The, right-click on any part of the code and select Open Variable Inspector. A new tab will be shown, and you can re-size and re-position the screen accordingly. An example of this process is shown in the variable inspector repository here.
