# RAF

**Repository Path**: DestinyLin/RAF

## Basic Information

- **Project Name**: RAF
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-04-21
- **Last Updated**: 2025-04-24

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Usage

- To perform inference for RAF and the baseline, install the necessary packages by running:

    ```
    pip install -r requirements.txt
    ```


## Running Chronos: Baseline vs. Naive RAF Performance Comparison
This codebase is built on top of the main [Chronos Forecasting GitHub repository](https://github.com/amazon-science/chronos-forecasting). For more details and updates, please refer to the official repo.

- For the baseline approach, run `run_chronos.py`:
    ```
    python run_chronos.py path/to/config/file.yaml path/to/result/file.csv --no-augment
    ```

- For RAF, run `run_chronos.py`:
    ```
    python run_chronos.py path/to/config/file.yaml path/to/result/file.csv --augment
    ```

## Fine-tuning Chronos Models for Advanced RAF and Baseline
- The same approach is followed for fine-tuning in the Chronos repository. The configuration files required to fine-tune the models are provided under `configs/fine-tune` for both the baseline approach and RAF. The same scripts are run for both approaches, using different configuration files.

- The list of arrow files obtained from Benchmark I is also provided. The datasets in `RAF_finetune_datasets` are augmented via time series retrieval, whereas the datasets in `Baseline_finetune_datasets` are not. However, these datasets can be replicated or new ones can be generated by running `generate_fine_tune_data.py` as follows:

    ```
    # For augmented datasets via time series retrieval
    python generate_fine_tune_data.py path/to/config/file.yaml  --augment

    # For regular datasets with no retrieval
    python generate_fine_tune_data.py path/to/config/file.yaml  --no-augment
    ```

- Start the fine-tuning for Chronos Base:
    ```
    # Fine-tune `amazon/chronos-t5-base` for 1000 steps with initial learning rate of 1e-5
    python chronos_training/train.py --config /path/to/modified/config.yaml \
        --model-id amazon/chronos-t5-base \
        --no-random-init \
        --max-steps 1000 \
        --learning-rate 0.00001
    ```
- Start the fine-tuning for Chronos Mini:
    ```
    # Fine-tune `amazon/chronos-t5-tiny` for 400 steps with initial learning rate of 1e-5
    python chronos_training/train.py --config /path/to/modified/config.yaml \
        --model-id amazon/chronos-t5-tiny \
        --no-random-init \
        --max-steps 400 \
        --learning-rate 0.00001
    ```

The output and checkpoints will be saved in `RAF_models/run-{id}/` for fine-tuning on the RAF_finetune_datasets, and in `Baseline_models/run-{id}/` for fine-tuning on the Baseline_finetune_datasets.

## Plotting the Results for Baseline and Naive RAF

- In order to visualize the different forecasts obtained from Naive RAF and the baseline, run the following script: 

    ```
    python plot_chronos_results.py path/to/config/file.yaml
    ```

This will generate plots for the given dataset using the provided context and prediction lengths specified in the configuration file.

## Calculating the Relative Improvements

- Run the following script to compute the aggregate relative WQL and MASE values obtained from Naive RAF and the baseline scores.

    ```
    python compute_scores.py path/to/baseline/score/file.csv path/to/RAF/score/file.csv
    ```