Project submission for the second project of the machine learning course.
Team Among Us
17 December 2020
- Install the necessary libraries
- Extract the two pickle zip folders under Models/RF (see below, in Contents)
- Run the file
run.py
- Follow the on-screen prompts:
- Select the model between
MK
for Markovian,LSTM
,RF
orTCN
. Note that the code will loop until you input a valid model. - Select whether to perform a hyperparameter optimization,
inputting
1
if you wish to perform one, or0
if you do not. - Select the number of days you want to take into consideration. Note that if you are not performing a hyperparamter optimization, this value must be 2, 10, 50, 100 or 150.
- Note that the last two options will not appear if you have chosen a Markovian model
- Select the model between
- Wait until the code finished to execute. If you are performing a hyperparamter optimization, this can take a while.
- Visualize the results in the plots that appear or by seeing the performance indcators in the console.
project
│ README.md
│ LSTM_run.py
│ MK_run.py
│ preprocess.py
| Report.pdf
│ RF_run.py
│ run.py
│ TCN_run.py
│
└───Archives
└───Data
└───Figures
└───Helpers
| │ p_indicators.py
| │ performance_comparison.py
| | relabel.py
| | state_estimation.py
|
└───Models
│ └───LSTM
│ └───TCN
│ └───RF
│ │ RF_pickles_1.zip <----- THIS FILE MUST BE EXTRACTED BEFORE RUNNING!
│ │ RF_pickles_2.zip <----- THIS FILE MUST BE EXTRACTED BEFORE RUNNING!
│
└───Readings
The project should run with any version of Python 3, although Python 3.7.7 was used for this project. The following libraries must be installed for the project to run properly.
- Keras 3.1.1
- Keras-tuner 1.0.2
- Keras-tcn 3.1.1
- Numpy 1.17.0
- Pandas 1.0.5
- Pyarrow 1.0.1
- Python-louvain 0.14
- Scikit-learn 0.23.1
- Seaborn 0.10.1
- Sklearn 0.21.1
- Tensorflow 2.3.1
Team Among Us
Joshua Cayetano-Emond
Lorenzo Germini
Benoit Pahud