Module panama.ml.auto.search_space

Classes

class SearchSpace (name: str = 'sp')

Expand source code

class SearchSpace:
    """A class for defining a search space for hyperparameter tuning.

    Attributes:
        name: The name of the search space.
        params: A dictionary of parameter names and their search spaces.
    """

    def __init__(self, name: str = "sp"):
        """Initializes the search space with the given name.

        Args:
            name: The name of the search space.

        Returns:
            None.
        """
        self.name = name
        self.params = {}
        self.params_orig = {}

    def add_param(self, param_name: str, param_space: hp) -> None:
        """Adds a parameter and its search space to the search space.

        Args:
            param_name: The name of the parameter.
            param_space: The hyperopt search space for the parameter.

        Returns:
            None.
        """
        self.params[param_name] = param_space

    def from_dict(self, space_dict: Dict) -> "SearchSpace":
        """Constructs the search space from a dictionary of parameter names and their search spaces.

        Args:
            space_dict: A dictionary of parameter names and their search spaces.

        Returns:
            SearchSpace: The constructed search space.
        """
        self.params_orig = space_dict
        for param_name, param_space in space_dict.items():
            if (param_space["type"] == "int") and ("min" in param_space.keys()) and ("max" in param_space.keys()):
                self.add_param(
                    param_name,
                    hp.uniformint(
                        param_name,
                        param_space["min"],
                        param_space["max"],
                    ),
                )
            elif (param_space["type"] == "float") and ("min" in param_space.keys()) and ("max" in param_space.keys()):
                self.add_param(
                    param_name,
                    hp.uniform(param_name, param_space["min"], param_space["max"]),
                )
            elif param_space["type"] == "choice" and "values" in param_space.keys():
                self.add_param(param_name, hp.choice(param_name, param_space["values"]))
            else:
                raise ValueError(f"Invalid search space for parameter {param_name}")

    def from_json(self, space_file: str) -> "SearchSpace":
        """Constructs the search space from a JSON file.

        Args:
            space_file: The path to the JSON file.

        Returns:
            SearchSpace: The constructed search space.
        """
        with open(space_file, "r") as f:
            space_dict = json.load(f)
            self.from_dict(space_dict)

    def from_default(self, model) -> "SearchSpace":
        """Constructs the search space from a JSON file.

        Args:
            model (BaseTunableModel): model for which default search space is needed.

        Returns:
            SearchSpace: The constructed search space.
        """
        self.from_dict(model.DEFAULT_SEARCH_DICT)

    def get_space(self) -> Dict:
        """Returns the dictionary of parameter names and their search spaces.

        Args:
            None.

        Returns:
            dict: A dictionary of parameter names and their search spaces.
        """
        return self.params

    def get_space_dict(self) -> Dict:
        """Returns the dictionary of parameter names and their search spaces.

        Args:
            None.

        Returns:
            dict: A dictionary of parameter names and their search spaces.
        """
        return self.params_orig

A class for defining a search space for hyperparameter tuning.

Attributes

name

The name of the search space.

params

A dictionary of parameter names and their search spaces.

Initializes the search space with the given name.

Args

name

The name of the search space.

Returns

None.

Methods

def add_param(self,
param_name: str,
param_space: ) ‑> None

Expand source code

def add_param(self, param_name: str, param_space: hp) -> None:
    """Adds a parameter and its search space to the search space.

    Args:
        param_name: The name of the parameter.
        param_space: The hyperopt search space for the parameter.

    Returns:
        None.
    """
    self.params[param_name] = param_space

Adds a parameter and its search space to the search space.

Args

param_name

The name of the parameter.

param_space

The hyperopt search space for the parameter.

Returns

None.

def from_default(self, model) ‑> SearchSpace

Expand source code

def from_default(self, model) -> "SearchSpace":
    """Constructs the search space from a JSON file.

    Args:
        model (BaseTunableModel): model for which default search space is needed.

    Returns:
        SearchSpace: The constructed search space.
    """
    self.from_dict(model.DEFAULT_SEARCH_DICT)

Constructs the search space from a JSON file.

Args

model : BaseTunableModel

model for which default search space is needed.

Returns

SearchSpace

The constructed search space.

def from_dict(self, space_dict: Dict) ‑> SearchSpace

Expand source code

def from_dict(self, space_dict: Dict) -> "SearchSpace":
    """Constructs the search space from a dictionary of parameter names and their search spaces.

    Args:
        space_dict: A dictionary of parameter names and their search spaces.

    Returns:
        SearchSpace: The constructed search space.
    """
    self.params_orig = space_dict
    for param_name, param_space in space_dict.items():
        if (param_space["type"] == "int") and ("min" in param_space.keys()) and ("max" in param_space.keys()):
            self.add_param(
                param_name,
                hp.uniformint(
                    param_name,
                    param_space["min"],
                    param_space["max"],
                ),
            )
        elif (param_space["type"] == "float") and ("min" in param_space.keys()) and ("max" in param_space.keys()):
            self.add_param(
                param_name,
                hp.uniform(param_name, param_space["min"], param_space["max"]),
            )
        elif param_space["type"] == "choice" and "values" in param_space.keys():
            self.add_param(param_name, hp.choice(param_name, param_space["values"]))
        else:
            raise ValueError(f"Invalid search space for parameter {param_name}")

Constructs the search space from a dictionary of parameter names and their search spaces.

Args

space_dict

A dictionary of parameter names and their search spaces.

Returns

SearchSpace

The constructed search space.

def from_json(self, space_file: str) ‑> SearchSpace

Expand source code

def from_json(self, space_file: str) -> "SearchSpace":
    """Constructs the search space from a JSON file.

    Args:
        space_file: The path to the JSON file.

    Returns:
        SearchSpace: The constructed search space.
    """
    with open(space_file, "r") as f:
        space_dict = json.load(f)
        self.from_dict(space_dict)

Constructs the search space from a JSON file.

Args

space_file

The path to the JSON file.

Returns

SearchSpace

The constructed search space.

def get_space(self) ‑> Dict

Expand source code

def get_space(self) -> Dict:
    """Returns the dictionary of parameter names and their search spaces.

    Args:
        None.

    Returns:
        dict: A dictionary of parameter names and their search spaces.
    """
    return self.params

Returns the dictionary of parameter names and their search spaces.

Args

None.

Returns

dict

A dictionary of parameter names and their search spaces.

def get_space_dict(self) ‑> Dict

Expand source code

def get_space_dict(self) -> Dict:
    """Returns the dictionary of parameter names and their search spaces.

    Args:
        None.

    Returns:
        dict: A dictionary of parameter names and their search spaces.
    """
    return self.params_orig

Returns the dictionary of parameter names and their search spaces.

Args

None.

Returns

dict

A dictionary of parameter names and their search spaces.