HUGGINGFACE.md

# Hugging Face Data/Code Split Workflow

Use two Hugging Face repos:

- **Data repo**: Dataset repo, e.g. `YOUR_USERNAME/decode-iblend-data`
- **Code repo**: Model repo by default, e.g. `YOUR_USERNAME/decode-iblend-code`

This keeps the large CSV data separate from the notebook/script code.

## 1. Upload From This Machine

```bash
python3 -m pip install huggingface_hub
export HF_TOKEN=hf_your_token_here

python3 scripts/upload_to_huggingface.py \
  --data-repo-id YOUR_USERNAME/decode-iblend-data \
  --code-repo-id YOUR_USERNAME/decode-iblend-code
```

Optional private repos:

```bash
python3 scripts/upload_to_huggingface.py \
  --data-repo-id YOUR_USERNAME/decode-iblend-data \
  --code-repo-id YOUR_USERNAME/decode-iblend-code \
  --private
```

Dry run:

```bash
python3 scripts/upload_to_huggingface.py \
  --data-repo-id YOUR_USERNAME/decode-iblend-data \
  --code-repo-id YOUR_USERNAME/decode-iblend-code \
  --dry-run
```

If you also want the code repo to be a Dataset repo:

```bash
python3 scripts/upload_to_huggingface.py \
  --data-repo-id YOUR_USERNAME/decode-iblend-data \
  --code-repo-id YOUR_USERNAME/decode-iblend-code \
  --code-repo-type dataset
```

## 2. Download Data And Code In A Fresh Notebook

```python
import os
import subprocess
import sys
from pathlib import Path

subprocess.run([sys.executable, "-m", "pip", "install", "-q", "huggingface_hub"], check=True)

from huggingface_hub import snapshot_download

HF_DATA_REPO_ID = "YOUR_USERNAME/decode-iblend-data"
HF_CODE_REPO_ID = "YOUR_USERNAME/decode-iblend-code"
CODE_REPO_TYPE = "model"  # change to "dataset" if you uploaded code as a dataset repo

DATA_DIR = Path("hf_data")
CODE_DIR = Path("hf_code")

snapshot_download(
    repo_id=HF_DATA_REPO_ID,
    repo_type="dataset",
    local_dir=str(DATA_DIR),
    local_dir_use_symlinks=False,
)

snapshot_download(
    repo_id=HF_CODE_REPO_ID,
    repo_type=CODE_REPO_TYPE,
    local_dir=str(CODE_DIR),
    local_dir_use_symlinks=False,
)

os.environ["IBLEND_DATA_ROOT"] = str(DATA_DIR.resolve())
SCRIPT_PATH = str((CODE_DIR / "scripts" / "decode_reimplementation.py").resolve())

print("Data root:", os.environ["IBLEND_DATA_ROOT"])
print("Script:", SCRIPT_PATH)
```

Then run immediately in the same notebook with `subprocess`:

```python
import subprocess
import sys

TRAIN_ENV = os.environ.copy()
TRAIN_ENV["DECODE_DISABLE_TENSORFLOW"] = "1"
TRAIN_ENV.pop("CUDA_VISIBLE_DEVICES", None)

subprocess.run([
    sys.executable, SCRIPT_PATH,
    "--mode", "paper_buildings",
    "--target", "Academic",
    "--test-span-days", "7",
    "--skip-lstm",
], check=True, env=TRAIN_ENV)
```

For deep learning, run each model separately:

```python
for model_name in ["lstm", "cnn", "tcn"]:
    subprocess.run([
        sys.executable, SCRIPT_PATH,
        "--mode", "paper_buildings",
        "--target", "Academic",
        "--test-span-days", "7",
        "--dl-models", model_name,
        "--epochs", "20",
        "--batch-size", "64",
    ], check=True, env=TRAIN_ENV)
```

## Data Repo Layout

```text
energy_dataset/
IIITD_occupancy_dataset/
iiitd_calender_schedule/
weather_comparison/
```

## Code Repo Layout

```text
DECODE_Reimplementation.ipynb
HUGGINGFACE.md
scripts/decode_reimplementation.py
scripts/upload_to_huggingface.py
scripts/preprocess_and_eda_by_building.py
decode_reimplementation_outputs/README.md
```

`decode_reimplementation.py` reads data from `IBLEND_DATA_ROOT`, so code and data can live in separate folders.