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29 lines
1009 B
Markdown
29 lines
1009 B
Markdown
# Hyperparameter Optimization
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Demonstrates hyperparameter optimization using Ludwig's in-built capabilities.
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### Preparatory Steps
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- Create `data` directory
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- Download [Kaggle wine quality data set](https://www.kaggle.com/rajyellow46/wine-quality) into the `data` directory. Directory should
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appear as follows:
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```
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hyperopt/
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data/
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winequalityN.csv
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```
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### Description
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Jupyter notebook `model_hyperopt_example.ipynb` demonstrates several hyperparameter optimization capabilities. Key features demonstrated in the notebook:
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- Training data is prepared for use
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- Programmatically create Ludwig config dictionary from the training data dataframe
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- Setup parameter space for hyperparameter optimization
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- Perform two hyperparameter runs
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- Parallel workers using random search strategy
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- Serial processing using random search strategy
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- Parallel workers using grid search strategy (Note: takes about 35 minutes)
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- Demonstrate various Ludwig visualizations for hyperparameter optimization
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