PDM manual for tin200

This repository is based upon work done by Othelie Eliassen, Marius Vinorum, and Aleksander Bo Tunheim from NMBU.

Introduction

The source code uses the scikit, numpy, pandas, and tensorflow libraries.

First, we read the data in the data repository with pandas. Then, the code is processed with scikit and numpy, before it is used by Tensorflow. The tensorflow library will use the processed data and train a machine learning model with the data.

Getting started

To get started, make sure you have python 3 installed. Install the required packaged either with conda, or with pip.

Setup with Conda

Run the command:

conda env create -f environment.yml

Setup with pip

Run the command:

python3 -m pip install -r requirements.txt

Running the code

Enter the folder where you have downloaded the code, and open a terminal in the current folder. For a guide, take a look at the following guide: https://www.groovypost.com/howto/open-command-window-terminal-window-specific-folder-windows-mac-linux/

Make sure that you are in the root directory of the folder(i.e. not in the folder code, data, images, model or report).

NOTE: In Windows, you might have to use the anaconda prompt instead of the command prompt or powershell.

To run the streamlit version of the app, run the command:

If you used conda, you need to enter your environment by doing the following:

conda activate pdm-env

Now, we have to do multiple steps:

1. We have to first run the Particle1.py script to read the data, sort it and then normalize the data
2. Then we run the Particle2.py script to build our neural network, and train it.
3. Finally, to train our model, we run the particle3.py script to evaluate our model.

All scripts can be ran with the following code in your terminal:

python ./code/<Script name>.py