decode_reimplementation_outputs/README.md

# DECODE Re-implementation

This folder is used by `scripts/decode_reimplementation.py`.

## Goal

Re-implement the training flow from the DECODE paper for the local I-BLEND data:

1. Resample energy to 10-minute intervals.
2. Convert original power readings in W to interval energy in Wh.
3. Merge occupancy and calendar/schedule features.
4. Optionally merge weather if the local weather range overlaps the energy range.
5. Add historical energy features, including the previous three same-working-day-class values at the same time instant.
6. Normalize with Min-Max scaling.
7. Split chronologically into 70% train, 15% validation, and 15% test.
8. Train Ridge Regression, Decision Tree, Random Forest, and optionally LSTM.
   The LSTM backend prefers PyTorch when available and falls back to TensorFlow/Keras.

## Install Dependencies

Base ML models:

```bash
python3 -m pip install pandas numpy scikit-learn
```

LSTM model, choose one backend:

```bash
python3 -m pip install torch
python3 -m pip install tensorflow
```

## Run Examples

Train the 7 paper-style building targets:

```bash
python3 scripts/decode_reimplementation.py --mode paper_buildings
```

Train the 9 local meter targets:

```bash
python3 scripts/decode_reimplementation.py --mode meters
```

Quick smoke test on one target without LSTM:

```bash
python3 scripts/decode_reimplementation.py --mode paper_buildings --target Academic --max-rows 20000 --skip-lstm
```

Run one meter with LSTM:

```bash
python3 scripts/decode_reimplementation.py --mode meters --target Boys_main --epochs 20 --batch-size 64
```

## Outputs

- `processed/<target>_train_ready.csv`: feature-engineered supervised table.
- `results_paper_buildings.csv`: model metrics for 7 building-level targets.
- `results_meters.csv`: model metrics for 9 meter-level targets.
- `run_config_*.json`: run metadata and paths.

## Notes

The local weather file currently starts in 2018, while energy data ends in 2017. Because of this, the script does not force weather features into the model unless `--include-weather` is passed and an overlap is detected.