Hyperparameter Optimization

This is a complete example of Ludwig's hyperparameter optimization capability.

These interactive notebooks follow the steps of this example:

• Ludwig CLI:
• Ludwig Python API:

Download the Adult Census Income dataset

Adult Census Income is an extract of 1994 Census data for predicting whether a person's income exceeds $50K per year. The data set consists of over 49K records with 14 attributes with missing data.

ludwig datasets download adult_census_income

This command will create a dataset adult_census_income.csv in the current directory.

The columns in the dataset are

column	description
age	numeric variable, age of person
workclass	categorical variable, Type of empolyment
fnlwgt	numeric variable, no defintion
education	categorical variable, education level
education-num	nmeric variable, no definition
marital-status	categorical variable, marital status
occupation	categorical variable, occupation
relationship	categorical variable, Relationship to household
race	categorical variable, race
sex	categorical variable, gender
capital-gain	numeric variable, no definition
capital-loss	numeric variable, no definition
hours-per-week	numeric variable, hours worked per week
native-country	categorical variable, Country of origin
income	binary variable, " <=50K" or " >50K"
split	numeric variable, indicating data split training(0), test(2)

Setup for hyperparameter optimization run

Hyperparameter optimization is defined with the hyperopt section of the Ludwig configuration specification.

clipython

ludwig hyperopt

preprocessing:
    ...
input_features:
    ...
combiner:
    ...
output_features:
    ...
trainer:
    ...

# hyperopt specification 
hyperopt:
    # specify parameters for the Ray Tune to executor to run the hyperparameter optimization
    executor:
        ...
    # specify Ray Tune search algorithm to use
    search_alg:
        ...
    # hyperparameter search space for the optimization
    parameters:
        ...
    # minimize or maximize the metric score
    goal: ...
    # metric score to optimize
    metric: ...
    # name of the output feature
    output_feature: ...

# define model configuration
config = {
    'combiner': ... ,
    'input_features': ... ,
    'output_features': ... ,
    'preprocessing': ...,
    'trainer':... ,

    # hyperopt specification 
    'hyperopt':  {
        # specify parameters for the Ray Tune to executor to run the hyperparameter optimization
        'executor': {'type': 'ray', ... },
        # specify Ray Tune search algorithm to use
        'search_alg': {... },
        # hyperparameter search space for the optimization
        'parameters': {...},
        # minimize or maximize the metric score
        'goal': ...,
        # metric score to optimize
        'metric': ...,
        # name of the output feature
        'output_feature': ...,
    }
}

Hyperparameter Search Space Specification

For this example, we want to determine the effect of Ludwig's Trainer's learning_rate and num_fc_layers of the income output feature on model's roc_auc metric. To do this we will use two different hyperparameter optimization approaches: Random Search and Grid Search.

Random Search

clipython

hyperopt:
    executor:
        num_samples: 16
    goal: maximize
    metric: roc_auc
    output_feature: income
    parameters: 
        income.num_fc_layers: 
            space: randint
            lower: 2
            upper: 9
        trainer.learning_rate:
            space: loguniform
            lower: 0.001
            upper: 0.1
    search_alg:
        type: variant_generator
        random_state: 1919

 'hyperopt': {
    'executor': {'num_samples': 16, },
    'goal': 'maximize',
    'metric': 'roc_auc',
    'output_feature': 'income',
    'parameters': {
        'income.num_fc_layers': {
            'space': 'randint',
            'lower': 2,
            'upper': 9
        },
        'trainer.learning_rate': {
            'space': 'loguniform',
            'lower': 0.001,
            'upper': 0.1}
        },
    'search_alg': {'type': 'variant_generator', 'random_state': 1919, }
},

Grid Search

clipython

hyperopt:
    executor:
        num_samples: 1
    goal: maximize
    metric: roc_auc
    output_feature: income
    parameters: 
        income.num_fc_layers: 
            space: grid_search
            values: [2, 4, 6, 8]
        trainer.learning_rate:
            space: grid_search
            values: [0.001, 0.003, 0.007, 0.01]
    search_alg:
        type: variant_generator
        random_state: 1919

'hyperopt': {
    'executor': {'num_samples': 1,},
    'goal': 'maximize',
    'metric': 'roc_auc',
    'output_feature': 'income',
    'parameters': {
        'income.num_fc_layers': {'space': 'grid_search', 'values': [2, 4, 6, 8]},
        'trainer.learning_rate': {'space': 'grid_search', 'values': [0.001, 0.003, 0.007, 0.01]}},
    'search_alg': {'type': 'variant_generator', 'random_state': 1919, }
},

Run Hyperparameter Optimization

Here are example commands/function call to run Ludwig's hyperparameter optimization capability.

clipython

ludwig hyperopt command

ludwig hyperopt --dataset adult_census_income.csv \
  --config config.yaml \
  --output_directory results \
  --hyperopt_log_verbosity 1

hyperopt() method

hyperopt_results = hyperopt(
    config, 
    dataset=adult_census_df, 
    output_directory="results", 
    hyperopt_log_verbosity=1
)

Visualize Hyperparameter Optimization Results

clipython

ludwig visualize hyperopt_report command

ludwig visualize hyperopt_hiplot command

# generate visualizations on hyperparameter effects on the metric
ludwig visualize --visualization hyperopt_report \
              --hyperopt_stats_path results/hyperopt_statistics.json \
              --output_directory visualizations \
              --file_format png

# generate hyperopt hiplot parallel coordinate visualization
ludwig visualize --visualization hyperopt_hiplot \
                  --hyperopt_stats_path results/hyperopt_statistics.json \
                  --output_directory visualizations

visualize.hyperopt_report() function

visualize.hyperopt_hiplot() function

hyperopt_report("./rs_output/hyperopt_statistics.json")

hyperopt_hiplot("./rs_output/hyperopt_statistics.json", output_directory="visualizations")

hyperopt_report

hyperopt_hiplot