Olive Oil Transformer Model

This repository contains transformer-based models for various predictions, including olive oil production forecasting. Here's a guide to the key components of the project:

Project Structure

Model Notebooks Location

The model notebooks are located in the /models directory, organized by different prediction tasks:

• Olive Oil Model: /models/olive_oli/olive_oil-v2.ipynb

  • Contains the implementation of the transformer model for olive oil production forecasting
  • Includes model training, evaluation, and visualization components

• Solar Energy Model: /models/solarenergy/solarenergy_model_v1.ipynb

  • Transformer model for solar energy prediction

• Solar Radiation Model: /models/solarradiation/solarradiation_model.ipynb

  • Implementation for solar radiation forecasting

• UV Index Model: /models/uv_index/uv_index_model.ipynb

  • Model for UV index prediction

Synthetic Data Generation

The script for generating synthetic training data is located at: /olive_oil_train_dataset/create_train_dataset.py

Command

python -m olive_oil_train_dataset.create_train_dataset --random-seed 42 --num-simulations 100000 --batch-size 10000 --max-workers 20

This script is responsible for creating synthetic data used in training the olive oil production model.

Utility Functions

Common utility functions and helper methods are stored in: /utils/helpers.py

Model Artifacts

Each model directory contains its associated artifacts, including:

• Trained model weights
• Scalers for data normalization
• Training logs
• Model architecture visualizations
• Performance analysis plots

For example, the olive oil model directory contains:

• Model weights in the weights subdirectory
• Scalers for static and temporal features
• Training logs in the logs subdirectory
• Model architecture and performance visualization plots

Getting Started

To work with the models:

1. Start with the respective notebook in the /models directory
2. For olive oil prediction, first generate synthetic data using the script in /olive_oil_train_dataset
3. Utilize the utility functions from /utils/helpers.py as needed