A RunResult is the outcome of a single hyperparameter configuration run of an Experiment. An ExperimentResult is a collection of all RunResults from a full Experiment, encapsulating all of the defined hyperparameter configurations and their produced outputs.

​
RunResult Class

​
Overview

The RunResult class captures the outcome of a single hyperparameter configuration run of an experiment from the defined hyperparameter search space, including the score achieved, the parameters used, and any additional metadata related to the run.

​
Fields

ParameterTypeDefaultDescriptionRequired
scorefloatN/AThe score achieved in this run.Yes
paramsDict[str, Any]N/AA dictionary of parameters used in this run.Yes
metadataOptional[Dict[str, Any]]{}Optional metadata related to the run.No

​
Example Usage

from nomadic.result import RunResult

# Example usage of RunResult
result = RunResult(
    score=0.85,
    params={"learning_rate": 0.01, "batch_size": 32},
    metadata={"run_id": "001", "experiment": "baseline"}
)

print(result.score)  # Output: 0.85
print(result.params)  # Output: {'learning_rate': 0.01, 'batch_size': 32}
print(result.metadata)  # Output: {'run_id': '001', 'experiment': 'baseline'}

​
ExperimentResult Class

​
Overview

The ExperimentResult class aggregates the results from multiple hyperparameter configuration runs in an experiment, identifying the best run based on the score and providing methods to analyze and export the results.

​
Fields

ParameterTypeDefaultDescriptionRequired
hp_search_spaceDict[str, Any]N/AThe explored hyperparameter search space for this ExperimentRunYes
run_resultsList[RunResult]N/AA list of RunResult instances, representing each run’s outcomeYes
best_idxOptional[int]0The position of the best RunResult in run_resultsNo
nameOptional[str]NoneName of the Experiment ResultNo
client_idOptional[str]NoneID of ExperimentResult on the Nomadic Workspace, if synchedNo

​
Methods

​
best_run_result() -> RunResult

Returns the RunResult with the highest score.

​
to_df(include_metadata=True) -> pd.DataFrame

Exports the ExperimentResult to a pandas DataFrame. If include_metadata is True, metadata fields are included as additional columns.

​
Example Usage

from nomadic.result import RunResult, ExperimentResult
from nomadic.tuner import tune

# Example usage of ExperimentResult
run1 = RunResult(score=0.85, params={"learning_rate": 0.01, "batch_size": 32})
run2 = RunResult(score=0.90, params={"learning_rate": 0.02, "batch_size": 32})
run3 = RunResult(score=0.83, params={"learning_rate": 0.01, "batch_size": 64})
run4 = RunResult(score=0.89, params={"learning_rate": 0.02, "batch_size": 64})

experiment_result = ExperimentResult(
    hp_search_space={
        'learning_rate': tune.choice([0.01, 0.02]),
        'batch_size': tune.choice([32, 64]),
    },
    run_results=[run1, run2, run3, run4]
)

print(experiment_result.best_run_result.score)  # Output: 0.90

# Export to DataFrame
df = experiment_result.to_df()
print(df)
TunerDatasets