Documentation for RandomSearchOptimizer

Random search optimizer.

Searches for the best configuration by randomly testing hyperparameter combinations without any grid.

Source code in photonai/optimization/random_search/random_search.py

class RandomSearchOptimizer(PhotonSlaveOptimizer):
    """Random search optimizer.

    Searches for the best configuration by randomly
    testing hyperparameter combinations without any grid.

    """
    def __init__(self, limit_in_minutes: Union[float, None] = None, n_configurations: Union[int, None] = 10):
        """
        Initialize the object.
        One of limit_in_minutes or n_configurations must differ from None.

        Parameters:
            limit_in_minutes:
                Total time in minutes.

            n_configurations:
                Number of configurations to be calculated.

        """
        self.pipeline_elements = None
        self.parameter_iterable = None
        self.ask = self.next_config_generator()
        self.n_configurations = None

        if not limit_in_minutes or limit_in_minutes <= 0:
            self.limit_in_minutes = None
        else:
            self.limit_in_minutes = limit_in_minutes
        self.start_time = None
        self.end_time = None

        if not n_configurations or n_configurations <= 0:
            self.n_configurations = None
        else:
            self.n_configurations = n_configurations
        self.k_configutration = 0  # use k++ until k==n: break

        if self.n_configurations is None and self.limit_in_minutes is None:
            msg = "No stopping criteria for RandomSearchOptimizer."
            logger.error(msg)
            raise ValueError(msg)

    def prepare(self, pipeline_elements: list, maximize_metric: bool) -> None:
        """
        Initializes grid free random hyperparameter search.

        Parameters:
            pipeline_elements:
                List of all PipelineElements to create the hyperparameter space.

            maximize_metric:
                Boolean to distinguish between score and error.

        """
        self.start_time = None
        self.pipeline_elements = pipeline_elements
        self.ask = self.next_config_generator()

    def next_config_generator(self) -> Generator:
        """
        Generator for new configs - ask method.

        Returns:
            Yields the next config.

        """
        while True:
            self.k_configutration += 1
            new_config = True
            if self.limit_in_minutes:
                if self.start_time is None:
                    self.start_time = datetime.datetime.now()
                    self.end_time = self.start_time + datetime.timedelta(minutes=self.limit_in_minutes)

                if datetime.datetime.now() >= self.end_time:
                    new_config = False

            if self.n_configurations:
                if self.k_configutration >= self.n_configurations + 1:
                    new_config = False

            if not new_config:
                return

            _ = (yield self._generate_config())

    def _generate_config(self):
        config = {}
        for p_element in self.pipeline_elements:
            for h_key, h_value in p_element.hyperparameters.items():
                if isinstance(h_value, list):
                    config[h_key] = random.choice(h_value)
                else:
                    config[h_key] = h_value.get_random_value()
        return config

__init__(self, limit_in_minutes=None, n_configurations=10) special

Initialize the object. One of limit_in_minutes or n_configurations must differ from None.

Parameters:

Name	Type	Description	Default
limit_in_minutes	Optional[float]	Total time in minutes.	None
n_configurations	Optional[int]	Number of configurations to be calculated.	10

Source code in photonai/optimization/random_search/random_search.py

def __init__(self, limit_in_minutes: Union[float, None] = None, n_configurations: Union[int, None] = 10):
    """
    Initialize the object.
    One of limit_in_minutes or n_configurations must differ from None.

    Parameters:
        limit_in_minutes:
            Total time in minutes.

        n_configurations:
            Number of configurations to be calculated.

    """
    self.pipeline_elements = None
    self.parameter_iterable = None
    self.ask = self.next_config_generator()
    self.n_configurations = None

    if not limit_in_minutes or limit_in_minutes <= 0:
        self.limit_in_minutes = None
    else:
        self.limit_in_minutes = limit_in_minutes
    self.start_time = None
    self.end_time = None

    if not n_configurations or n_configurations <= 0:
        self.n_configurations = None
    else:
        self.n_configurations = n_configurations
    self.k_configutration = 0  # use k++ until k==n: break

    if self.n_configurations is None and self.limit_in_minutes is None:
        msg = "No stopping criteria for RandomSearchOptimizer."
        logger.error(msg)
        raise ValueError(msg)

next_config_generator(self)

Generator for new configs - ask method.

Returns:

Type	Description
Generator	Yields the next config.

Source code in photonai/optimization/random_search/random_search.py

def next_config_generator(self) -> Generator:
    """
    Generator for new configs - ask method.

    Returns:
        Yields the next config.

    """
    while True:
        self.k_configutration += 1
        new_config = True
        if self.limit_in_minutes:
            if self.start_time is None:
                self.start_time = datetime.datetime.now()
                self.end_time = self.start_time + datetime.timedelta(minutes=self.limit_in_minutes)

            if datetime.datetime.now() >= self.end_time:
                new_config = False

        if self.n_configurations:
            if self.k_configutration >= self.n_configurations + 1:
                new_config = False

        if not new_config:
            return

        _ = (yield self._generate_config())

prepare(self, pipeline_elements, maximize_metric)

Initializes grid free random hyperparameter search.

Parameters:

Name	Type	Description	Default
pipeline_elements	list	List of all PipelineElements to create the hyperparameter space.	required
maximize_metric	bool	Boolean to distinguish between score and error.	required

Source code in photonai/optimization/random_search/random_search.py

def prepare(self, pipeline_elements: list, maximize_metric: bool) -> None:
    """
    Initializes grid free random hyperparameter search.

    Parameters:
        pipeline_elements:
            List of all PipelineElements to create the hyperparameter space.

        maximize_metric:
            Boolean to distinguish between score and error.

    """
    self.start_time = None
    self.pipeline_elements = pipeline_elements
    self.ask = self.next_config_generator()