# Post-Hoc Analysis

After training, SLM Lab automatically generates graphs and metrics. You can regenerate these anytime with updated styling or new metrics.

## What SLM Lab Generates

Every training run produces:

**Training curves** showing reward over time:

![Example Training Curve](https://huggingface.co/datasets/SLM-Lab/benchmark/resolve/main/data/ppo_cartpole_2026_01_30_221924/ppo_cartpole_t0_trial_graph_mean_returns_vs_frames.png)

**Moving average curves** for smoother visualization:

![Example Moving Average](https://huggingface.co/datasets/SLM-Lab/benchmark/resolve/main/data/ppo_cartpole_2026_01_30_221924/ppo_cartpole_t0_trial_graph_mean_returns_ma_vs_frames.png)

## Regenerating Analysis

Use `retro_analyze` to regenerate graphs and metrics without re-running training:

## Basic Usage

```bash
uv run python -c 'from slm_lab.experiment import retro_analysis; retro_analysis.retro_analyze("data/ppo_lunar_2026_01_30_221924")'
```

This regenerates:

* All graphs (PNG and HTML)
* Trial-level aggregated metrics
* Experiment summary (for search runs)

## What Gets Regenerated

| Artifact          | Location                             | Effect        |
| ----------------- | ------------------------------------ | ------------- |
| Trial graphs      | `*_trial_graph_*.png` (root)         | Overwritten   |
| Trial metrics     | `*_trial_metrics_scalar.json` (root) | Overwritten   |
| Session graphs    | `graph/*_session_graph_*.png`        | Overwritten   |
| Session data      | `info/*_session_df_{train,eval}.csv` | **Preserved** |
| Model checkpoints | `model/*.pt`                         | **Preserved** |
| Spec file         | `*_spec.json` (root)                 | **Preserved** |

{% hint style="success" %}
**Safe to run**: Retro analysis only overwrites derived data. Your raw session data, trained models, and spec files are never modified.
{% endhint %}

## Common Use Cases

### Update Graph Styling

SLM Lab updates Plotly styling periodically. Regenerate graphs to get the latest look:

```bash
# Pull latest code
git pull
uv sync

# Regenerate all experiments in data/
for dir in data/*/; do
    uv run python -c "from slm_lab.experiment import retro_analysis; retro_analysis.retro_analyze('$dir')"
done
```

### Recompute Metrics After Code Changes

If you modify the analysis module (e.g., add a new metric):

```bash
# After modifying slm_lab/experiment/analysis.py
uv run python -c 'from slm_lab.experiment import retro_analysis; retro_analysis.retro_analyze("data/ppo_lunar_2026_01_30_221924")'

# Check updated metrics
cat data/ppo_lunar_2026_01_30_221924/ppo_lunar_t0_trial_metrics_scalar.json
```

### Generate Publication-Quality Graphs

For papers or presentations, you may want higher-resolution or different formats:

```python
from slm_lab.experiment import retro_analysis

# Regenerate with custom settings
retro_analysis.retro_analyze('data/ppo_lunar_2026_01_30_221924')

# The HTML files support interactive exploration
# PNG files are ready for documents
```

## Batch Processing

Process multiple experiments:

```python
import os
from slm_lab.experiment import retro_analysis

data_dirs = [d for d in os.listdir('data') if os.path.isdir(f'data/{d}')]

for data_dir in data_dirs:
    print(f"Processing {data_dir}...")
    retro_analysis.retro_analyze(f'data/{data_dir}')
```

## Troubleshooting

### "Session data not found"

Ensure the data folder contains `*_session_df_train.csv` and `*_session_df_eval.csv` files in the `info/` subfolder. These are required for analysis.

### Graphs look wrong

Check that your SLM Lab version matches the data format. Very old experiments may need manual migration.


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