# Hyperparameter Optimization

Demonstrates hyperparameter optimization using Ludwig's in-built capabilities.

### Preparatory Steps

- Create `data` directory
- Download [Kaggle wine quality data set](https://www.kaggle.com/rajyellow46/wine-quality) into the `data` directory. Directory should
  appear as follows:

```
hyperopt/
    data/
        winequalityN.csv
```

### Description

Jupyter notebook `model_hyperopt_example.ipynb` demonstrates several hyperparameter optimization capabilities. Key features demonstrated in the notebook:

- Training data is prepared for use
- Programmatically create Ludwig config dictionary from the training data dataframe
- Setup parameter space for hyperparameter optimization
- Perform two hyperparameter runs
  - Parallel workers using random search strategy
  - Serial processing using random search strategy
  - Parallel workers using grid search strategy (Note: takes about 35 minutes)
- Demonstrate various Ludwig visualizations for hyperparameter optimization