Spaces:
Sleeping
Sleeping
File size: 2,927 Bytes
e484a46 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 |
# 📚 REPRODUCIBILITY.md
*AI-Driven Polymer Aging Prediction & Classification System*
*(Canonical Raman-only Pipeline)*
> **Purpose**
> A single document that lets any new user clone the repo, arquire the dataset, recreate the conda environment, and generate the validated Raman pipeline artifacts.
---
## 1. System Requirements
| Component | Minimum Version | Notes |
|-----------|-----------------|-------|
| Python | 3.10+ | Conda recommended |
| Git | 2.30+ | Any modern version |
| Conda | 23.1+ | Mamba also fine |
| OS | Linux / MacOS / Windows | CPU run (no GPU needed) |
| Disk | ~1 GB | Dataset + artifacts |
---
## 2. Clone Repository
```bash
git clone https://github.com/dev-jaser/ai-ml-polymer-aging-prediction.git
cd ai-ml-polymer-aging-prediction
git checkout main
```
---
## 3. Create & Activate Conda Environment
```bash
conda env create -f environment.yml
conda activate polymer_env
```
> **Tip:** If you already created `polymer_env` just run `conda activate polymer_env`
---
## 4. Download RDWP Raman Dataset
1. Visit https://data.mendeley.com/datasets/kpygrf9fg6/1
2. Download the archive (**RDWP.zip or similar**) by clicking `Download Add 10.3 MB`
3. Extract all `*.txt` Raman files into:
```bash
ai-ml-polymer-aging-prediction/datasets/rdwp
```
4. Quick sanity check:
```bash
ls datasets/rdwp | grep ".txt" | wc -l # -> 170 + files expected
```
---
## 5. Validate the Entire Pipeline
Run the canonical smoke-test harness:
```bash
./validate_pipeline.sh
```
Successful run prints:
```bash
[PASS] Preprocessing
[PASS] Training & artificats
[PASS] Inference
[PASS] Plotting
All validation checks passed!
```
Artifacts created:
```bash
outputs/figure2_model.pth
outputs/logs/raman_figure2_diagnostics.json
outputs/inference/test_prediction.json
outputs/plots/validation_plot.png
```
---
## 6. Optional: Train ResNet Variant
```python
python scripts/train_model.py --model resnet --target-len 4000 --baseline --smooth --normalize
```
Check that these exist now:
```bash
outputs/resnet_model.pth
outputs/logs/raman_resnet_diagnostics.json
```
---
## 7. Clean-up & Re-Run
To re-run from a clean state:
```bash
rm -rf outputs/*
./validate_pipeline.sh
```
All artifacts will be regenerated.
---
## 8. Troubleshooting
| Symptom | Likely Cause | Fix |
|---------|--------------|-----|
| `ModuleNotFoundError` during scripts| `conda activate polymer_env` not done | Activate env|
| `CUDA not available` warning | Running on CPU | Safe to ignore |
| Fewer than 170 files in `datasets/rdwp` | Incomplete extract | Re-download archive |
| `validate_pipeline.sh: Permission denied` | Missing executable bit | `chmod +x validated_pipeline.sh` |
---
## 9. Contact
For issues or questions, open an Issue in the GitHub repo or contact @dev-jaser
|