Parallel Hyperparameter Optimization (HPO) on Container Runtime¶

dataset_map = {
  "train": DataConnector.from_dataframe(session.create_dataframe(X_train)),
  "test": DataConnector.from_dataframe(session.create_dataframe(X_test)),
  ),
}

search_space = {
 "n_estimators": [50, 51],
 "max_depth": [4, 5]),
 "learning_rate": [0.01, 0.3],
}

from snowflake.ml.modeling.tune.search import BayesOpt, RandomSearch, GridSearch
search_alg = BayesOpt()
search_alg = RandomSearch()
search_alg = GridSearch()

search_space = {
    "n_estimators": tune.uniform(50, 200),
    "max_depth": tune.uniform(3, 10),
    "learning_rate": tune.uniform(0.01, 0.3),
}

from snowflake.ml.modeling import tune
tuner_config = tune.TunerConfig(
  metric="accuracy",
  mode="max",
  search_alg=search_algorithm.BayesOpt(
      utility_kwargs={"kind": "ucb", "kappa": 2.5, "xi": 0.0}
  ),
  num_trials=5,
  max_concurrent_trials=1,
)

def train_func():
  tuner_context = get_tuner_context()
  config = tuner_context.get_hyper_params()
  dm = tuner_context.get_dataset_map()
  ...
  tuner_context.report(metrics={"accuracy": accuracy}, model=model)

from snowflake.ml.modeling import tune
tuner = tune.Tuner(train_func, search_space, tuner_config)
tuner_results = tuner.run(dataset_map=dataset_map)

print(tuner_results.results)
print(tuner_results.best_model)
print(tuner_results.best_result)

from snowflake.ml.modeling.tune import Tuner

class Tuner:
  def __init__(
      self,
      train_func: Callable,
      search_space: SearchSpace,
      tuner_config: TunerConfig,
  )

  def run(
      self, dataset_map: Optional[Dict[str, DataConnector]] = None
  ) -> TunerResults

from snowflake.ml.modeling.tune import uniform, choice, loguniform, randint

def uniform(lower: float, upper: float)
    """
    Sample a float value uniformly between lower and upper.

    Use for parameters where all values in range are equally likely to be optimal.
    Examples: dropout rates (0.1 to 0.5), batch normalization momentum (0.1 to 0.9).
    """


def loguniform(lower: float, upper: float) -> float:
    """
    Sample a float value uniformly in log space between lower and upper.

    Use for parameters spanning several orders of magnitude.
    Examples: learning rates (1e-5 to 1e-1), regularization strengths (1e-4 to 1e-1).
    """


def randint(lower: int, upper: int) -> int:
    """
    Sample an integer value uniformly between lower(inclusive) and upper(exclusive).

    Use for discrete parameters with a range of values.
    Examples: number of layers, number of epochs, number of estimators.
    """



def choice(options: List[Union[float, int, str]]) -> Union[float, int, str]:
    """
    Sample a value uniformly from the given options.

    Use for categorical parameters or discrete options.
    Examples: activation functions ['relu', 'tanh', 'sigmoid']
    """

from snowflake.ml.modeling.tune import TunerConfig

class TunerConfig:
  """
  Configuration class for the tuning process.

  Attributes:
    metric (str): The name of the metric to optimize. This should correspond
        to a key in the metrics dictionary reported by the training function.

    mode (str): The optimization mode for the metric. Must be either "min"
        for minimization or "max" for maximization.

    search_alg (SearchAlgorithm): The search algorithm to use for
        exploring the hyperparameter space. Defaults to random search.

    num_trials (int): The maximum number of parameter configurations to
        try. Defaults to 5

    max_concurrent_trials (Optional[int]): The maximum number of concurrently running trials per node. If   not specified, it defaults to the total number of nodes in the cluster. This value must be a positive
    integer if provided.


  Example:
      >>> from snowflake.ml.modeling.tune import  TunerConfig
      >>> config = TunerConfig(
      ...     metric="accuracy",
      ...     mode="max",
      ...     num_trials=5,
      ...     max_concurrent_trials=1
      ... )
  """

from snowflake.ml.modeling.tune.search import BayesOpt, RandomSearch, GridSearch

@dataclass
class BayesOpt():
    """
    Bayesian Optimization class that encapsulates parameters for the acquisition function.

    This class is designed to facilitate Bayesian optimization by configuring
    the acquisition function through a dictionary of keyword arguments.

    Attributes:
        utility_kwargs (Optional[Dict[str, Any]]):
            A dictionary specifying parameters for the utility (acquisition) function.
            If not provided, it defaults to:
                {
                    'kind': 'ucb',   # Upper Confidence Bound acquisition strategy
                    'kappa': 2.576,  # Exploration parameter for UCB
                    'xi': 0.0      # Exploitation parameter
                }
    """
    utility_kwargs: Optional[Dict[str, Any]] = None

@dataclass
class RandomSearch():
    The default and most basic way to do hyperparameter search is via random search.

    Attributes:
Seed or NumPy random generator for reproducible results. If set to None (default), the global generator (np.random) is used.
    random_state: Optional[int] = None

@dataclass
class TunerResults:
    results: pd.DataFrame
    best_result: pd.DataFrame
    best_model: Optional[Any]

from snowflake.ml.modeling.tune import get_tuner_context

class TunerContext:
    """
    A centralized context class for managing trial configuration, reporting, and dataset information.
    """

    def get_hyper_params(self) -> Dict[str, Any]:
        """
        Retrieve the configuration dictionary.

        Returns:
            Dict[str, Any]: The configuration dictionary for the trial.
        """
        return self._hyper_params

    def report(self, metrics: Dict[str, Any], model: Optional[Any] = None) -> None:
    """
    Report metrics and optionally the model if provided.

    This method is used to report the performance metrics of a model and, if provided, the model itself.
    The reported metrics will be used to guide the next set of hyperparameters selection in the
    optimization process.

    Args:
        metrics (Dict[str, Any]): A dictionary containing the performance metrics of the model.
            The keys are metric names, and the values are the corresponding metric values.
        model (Optional[Any], optional): The trained model to be reported. Defaults to None.

    Returns:
        None: This method doesn't return anything.
    """

    def get_dataset_map(self) -> Optional[Dict[str, Type[DataConnector]]]:
        """
        Retrieve the dataset mapping.

        Returns:
            Optional[Dict[str, Type[DataConnector]]]: A mapping of dataset names to DataConnector types, if available.
        """
        return self._dataset_map