| name | pre-process |
| description | Pre-process a dataset into pre-windowed .npz files for use with the ModernTSF `pre_processed` dataset type. Use when the user wants to convert CSV data to .npz format before training, or to set up a new dataset with pre-computed windows. |
When to use / what to ask
Converts a CSV into pre-windowed train/val/test.npz for the pre_processed dataset type. Ask for:
- Input — a single CSV (
--input-csv, auto-split 0.7,0.1,0.2 by default) or a folder already holding train/val/test.csv (--input-dir)
- Output dir and window sizes (
--seq-len / --label-len / --pred-len)
- Feature mode (
M|S|MS, default M; S/MS also need --target, default OT)
Command
uv run python tool/pre_process.py \
--input-csv dataset/ETT-small/ETTh1.csv \
--output-dir dataset/ETTh1_npy \
--seq-len 512 --label-len 0 --pred-len 96 --features M
Then point a dataset config at the output:
[dataset]
name = "pre_processed"
root_path = "<output-dir>"
data_path = ""
Notes
- Window sizes baked into the
.npz must match the values used at training time.
- Scaler is always fitted on train only; with scaling on (default), set
task.inverse = true in the run config to inverse-transform predictions.
Reference
Full flags (--split-ratio, --no-scale, output file contents): docs/en/pre-process.md.
