ludwig-ai--ludwig
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84 行
1.8 KiB
YAML
84 行
1.8 KiB
YAML
model_type: ecd
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input_features:
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- name: fixed acidity
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type: number
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preprocessing:
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normalization: zscore
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- name: volatile acidity
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type: number
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preprocessing:
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normalization: zscore
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- name: citric acid
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type: number
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preprocessing:
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normalization: zscore
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- name: residual sugar
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type: number
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preprocessing:
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normalization: zscore
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- name: chlorides
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type: number
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preprocessing:
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normalization: zscore
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- name: free sulfur dioxide
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type: number
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preprocessing:
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normalization: zscore
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- name: total sulfur dioxide
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type: number
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preprocessing:
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normalization: zscore
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- name: density
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type: number
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preprocessing:
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normalization: zscore
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- name: pH
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type: number
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preprocessing:
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normalization: zscore
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- name: sulphates
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type: number
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preprocessing:
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normalization: zscore
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- name: alcohol
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type: number
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preprocessing:
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normalization: zscore
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output_features:
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- name: quality
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type: binary
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trainer:
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epochs: 20
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# NOTE: The Optuna executor requires PR #4090 to be merged, or Ludwig >= 0.14.
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# Install: pip install ludwig optuna
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hyperopt:
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executor:
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type: optuna
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num_samples: 20
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sampler: auto # auto selects the best sampler; also: tpe, gp, cmaes, random
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pruner: hyperband # stop unpromising trials early (MedianPruner also supported)
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storage: sqlite:///optuna_results.db # enables resumability across runs
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parameters:
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trainer.learning_rate:
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space: loguniform
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lower: 1.0e-5
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upper: 1.0e-2
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trainer.batch_size:
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space: int
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lower: 16
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upper: 256
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trainer.optimizer.type:
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space: choice
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categories:
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- adam
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- adamw
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- radam
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- schedule_free_adamw
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goal: minimize
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metric: validation.combined.loss
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split: validation
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