Create README.md

processed_data/UO/README.md

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+# UO Processed Dataset
+
+Documentation for the **University of Ottawa (UO) bearing dataset** after running the updated preprocessing pipeline. Along with KAIST and PU, this is one of the three datasets that were refreshed most recently.
+
+---
+
+## Folder Layout
+
+```
+UO/
+├── train.pt
+├── val.pt
+├── test.pt
+├── args.json
+├── additional_features.pt
+└── before_sliding_window/
+    ├── train.pt
+    ├── val.pt
+    └── test.pt
+```
+
+- `train.pt`, `val.pt`, `test.pt` – windowed tensors post sliding-window subsampling.
+- `before_sliding_window/*.pt` – the same splits before windowing (full-length sequences).
+- `args.json` – preprocessing arguments (window size, stride, split ratios, etc.).
+- `additional_features.pt` – Torch-serialized metadata collected from each MAT file (including channel names and auxiliary sensor info).
+
+---
+
+## Saved Tensor Structure
+
+Each windowed `.pt` file is a dictionary with:
+
+- `samples`: `torch.Tensor` of shape `[num_windows, num_channels, window_length]`
+- `labels`: `torch.Tensor` with class ids (`0: healthy`, `1: inner race fault`, `2: outer race fault`)
+- `sequence_ids`: indices that point back to the original MAT files
+- `sliding_window_sequence_ids`: mapping from windows to their source sequences
+- `size`: split ratio used when generating the split (for traceability)
+
+The `before_sliding_window` tensors use the same keys but keep the un-windowed signals (length ≈ 2,000,000 samples per channel).
+
+---
+
+## Features
+
+Each MAT file contains several arrays; the preprocessing script keeps only the first two vibration channels (`channel_1`, `channel_2`) that provide the full 2 000 000 samples used in prior work. Their names are captured in `additional_features.pt` under each MAT file:
+
+```python
+import torch
+
+meta = torch.load("additional_features.pt")
+print(meta["healthy"]["H-A-1.mat"]["name_features"])
+```
+
+Use this metadata to understand the physical meaning of each channel or to filter specific features.
+
+---
+
+## Usage Example
+
+```python
+import torch
+
+train = torch.load("train.pt")
+windows = train["samples"]                   # [N, num_channels, window_length]
+labels = train["labels"]                     # bearing condition ids
+original_indices = train["sliding_window_sequence_ids"]
+
+print(windows.shape)
+print(labels.unique())
+```
+
+Access the un-windowed signals:
+
+```python
+raw = torch.load("before_sliding_window/train.pt")
+full_sequences = raw["samples"]              # [num_sequences, num_channels, original_length]
+```
+
+---
+
+## Processing Pipeline
+
+1. **Raw input** – MAT files organised into three lists: healthy (`H-*`), inner faults (`I-*`), outer faults (`O-*`).  
+2. **Channel selection** – For every file, the script extracts `channel_1` and `channel_2`, each of length 2 000 000 samples. Additional metadata (speed, load, etc.) is preserved in `additional_features.pt`.  
+3. **Class-wise sequence split** – Using `train_size`, sequences are randomly assigned to train vs. (val+test); the remaining sequences are divided into validation and test according to `val_size`/`test_size`.  
+4. **Save before-window tensors** – Full-length tensors are written to `before_sliding_window/{train,val,test}.pt` for troubleshooting.  
+5. **Sliding-window sampling** – Windows of length `window_size` are generated every `step = window_size * stride` samples from each sequence.  
+6. **Persist final datasets** – Windowed tensors and labels are stored in the root `.pt` files together with the mapping fields (`sequence_ids`, `sliding_window_sequence_ids`, `size`).
+
+---
+
+## Input / Output Cheat Sheet
+
+| Stage | Shape | Description |
+|-------|-------|-------------|
+| Raw MAT arrays | `(2 000 000,)` per channel | Original vibration signals |
+| After loading | `(1, 2, 2 000 000)` | Tensor for a single MAT file (two channels) |
+| Before sliding window (train) | `[num_sequences, 2, original_length]` | Randomly selected sequences saved to `before_sliding_window/train.pt` |
+| After sliding window (train) | `[num_windows, 2, window_size]` | Final training dataset in `train.pt` |
+
+---
+
+## Notes
+
+- This README applies to the refreshed implementation; other datasets still rely on the legacy processing approach.
+- The supplied splits rely on random shuffling with the configured ratios. Re-run the pipeline to regenerate different splits if required.