lucastakara / cbic_21_anomaly_detection

This repository explores the cutting-edge field of anomaly detection using deep learning, particularly through the implementation of autoencoders. Our approach revolves around the concept of reconstruction error, with a specific focus on leveraging the Mean Absolute Error (MAE) as the determining factor for anomalies within complex datasets.

Autoencoders for Anomaly Detection

This repository explores the cutting-edge field of anomaly detection using deep learning, particularly through the implementation of autoencoders. Paper regarding the code is in the following link: https://sbic.org.br/wp-content/uploads/2021/09/pdf/CBIC_2021_paper_37.pdf

Prerequisites

Before you begin, ensure you have met the following requirements:

• You have installed Python 3.10, which is required to create the virtual environment.
• You have a basic understanding of Python virtual environments.

Setting Up and Running the Project

To set up the Python environment and run the project, follow these steps:

1. Clone the Repository

First, clone the repository to your local machine using the following command:

git clone https://github.com/lucastakara/CBIC_21_Anomaly_Detection.git

2. Set Up the Python Virtual Environment

Navigate to the project directory and create a virtual environment using Python 3.10. The following steps are for Unix-based systems like Linux and macOS. If you are using Windows, the commands may differ slightly.

cd /path/to/your/repository  # Navigate to the cloned repository

python3.10 -m venv venv  # Create a virtual environment named 'venv'

Activate the virtual environment. The command to do this varies by operating system.

• On macOS and Linux:

source venv/bin/activate

• On Windows (use either Command Prompt or PowerShell):

.\venv\Scripts\activate

3. Install the Dependencies

With the virtual environment activated, install the project's dependencies:

pip install -r requirements.txt

4. Run the Project

Now that the environment is set up, you can run the project using:

python main.py

Next implementations

• Implement K-fold Cross-Validation
• Perform loss reconstruction with different loss functions (MSE, MAPE, RMSE)
• Implement Hyperparameter tuning

cbic_21_anomaly_detection's Issues

Create unit tests for the repository

1. Create unitary tests for enhancing the code.

Implement requirements.txt

List the dependencies for running the repository.

Refactoring code

Refactor some code for readability enhancement.

Modify main.py

Call all models in main.py at once

Fix Y-axis scaling

Y scale is not correct. It is displaying incorrect measures. Fix it to match the original dataset.

Add readme