54 lines
2.2 KiB
Markdown
54 lines
2.2 KiB
Markdown
# Olive Oil Transformer Model
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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:
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## Project Structure
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### Model Notebooks Location
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The model notebooks are located in the `/models` directory, organized by different prediction tasks:
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- **Olive Oil Model**: `/models/olive_oli/olive_oil-v2.ipynb`
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- Contains the implementation of the transformer model for olive oil production forecasting
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- Includes model training, evaluation, and visualization components
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- **Solar Energy Model**: `/models/solarenergy/solarenergy_model_v1.ipynb`
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- Transformer model for solar energy prediction
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- **Solar Radiation Model**: `/models/solarradiation/solarradiation_model.ipynb`
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- Implementation for solar radiation forecasting
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- **UV Index Model**: `/models/uv_index/uv_index_model.ipynb`
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- Model for UV index prediction
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### Synthetic Data Generation
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The script for generating synthetic training data is located at: ```/olive_oil_train_dataset/create_train_dataset.py```
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#### Command
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``
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python -m olive_oil_train_dataset.create_train_dataset --random-seed 42 --num-simulations 100000 --batch-size 10000 --max-workers 20
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``
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This script is responsible for creating synthetic data used in training the olive oil production model.
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### Utility Functions
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Common utility functions and helper methods are stored in: ```/utils/helpers.py```
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## Model Artifacts
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Each model directory contains its associated artifacts, including:
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- Trained model weights
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- Scalers for data normalization
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- Training logs
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- Model architecture visualizations
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- Performance analysis plots
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For example, the olive oil model directory contains:
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- Model weights in the `weights` subdirectory
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- Scalers for static and temporal features
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- Training logs in the `logs` subdirectory
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- Model architecture and performance visualization plots
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## Getting Started
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To work with the models:
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1. Start with the respective notebook in the `/models` directory
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2. For olive oil prediction, first generate synthetic data using the script in `/olive_oil_train_dataset`
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3. Utilize the utility functions from `/utils/helpers.py` as needed |