Documentation for RandomGridSearchOptimizer

Random grid search optimizer.

Searches for the best configuration by randomly testing n points of a grid of possible hyperparameters.

Examples:

my_pipe = Hyperpipe(name='rgrid_based_pipe',
                    optimizer='random_grid_search',
                    optimizer_params={'n_configurations': 50,
                                      'limit_in_minutes': 10},
                    ...
                    )
my_pipe.fit(X, y)

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

class RandomGridSearchOptimizer(GridSearchOptimizer):
    """Random grid search optimizer.

    Searches for the best configuration by randomly
    testing n points of a grid of possible hyperparameters.

    Example:
        ``` python
        my_pipe = Hyperpipe(name='rgrid_based_pipe',
                            optimizer='random_grid_search',
                            optimizer_params={'n_configurations': 50,
                                              'limit_in_minutes': 10},
                            ...
                            )
        my_pipe.fit(X, y)
        ```

    """
    def __init__(self, limit_in_minutes: Union[float, None] = None, n_configurations: Union[int, None] = 25):
        """
        Initialize the object.

        Parameters:
            limit_in_minutes:
                Total time in minutes.

            n_configurations:
                Number of configurations to be calculated.

        """
        super(RandomGridSearchOptimizer, self).__init__()
        self._k = n_configurations
        self.n_configurations = self._k
        self.limit_in_minutes = limit_in_minutes
        self.start_time, self.end_time = None, None

    def prepare(self, pipeline_elements: list, maximize_metric: bool) -> None:
        """
        Prepare hyperparameter search.

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

            maximize_metric:
                Boolean to distinguish between score and error.

        """
        super(RandomGridSearchOptimizer, self).prepare(pipeline_elements, maximize_metric)
        self.start_time = None
        self.n_configurations = self._k
        self.param_grid = list(self.param_grid)
        # create random order in list
        np.random.shuffle(self.param_grid)
        if self.n_configurations is not None:
            # k is maximal all grid items
            if self.n_configurations > len(self.param_grid):
                self.n_configurations = len(self.param_grid)
            self.param_grid = self.param_grid[0:self.n_configurations]

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

        Returns:
            Yields the next config.

        """
        if self.start_time is None and self.limit_in_minutes is not None:
            self.start_time = datetime.datetime.now()
            self.end_time = self.start_time + datetime.timedelta(minutes=self.limit_in_minutes)
        for parameters in super(RandomGridSearchOptimizer, self).next_config_generator():
            if self.limit_in_minutes is None or datetime.datetime.now() < self.end_time:
                yield parameters

__init__(self, limit_in_minutes=None, n_configurations=25) special

Initialize the object.

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.	25

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

def __init__(self, limit_in_minutes: Union[float, None] = None, n_configurations: Union[int, None] = 25):
    """
    Initialize the object.

    Parameters:
        limit_in_minutes:
            Total time in minutes.

        n_configurations:
            Number of configurations to be calculated.

    """
    super(RandomGridSearchOptimizer, self).__init__()
    self._k = n_configurations
    self.n_configurations = self._k
    self.limit_in_minutes = limit_in_minutes
    self.start_time, self.end_time = None, None

next_config_generator(self)

Generator for new configs - ask method.

Returns:

Type	Description
Generator	Yields the next config.

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

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

    Returns:
        Yields the next config.

    """
    if self.start_time is None and self.limit_in_minutes is not None:
        self.start_time = datetime.datetime.now()
        self.end_time = self.start_time + datetime.timedelta(minutes=self.limit_in_minutes)
    for parameters in super(RandomGridSearchOptimizer, self).next_config_generator():
        if self.limit_in_minutes is None or datetime.datetime.now() < self.end_time:
            yield parameters

prepare(self, pipeline_elements, maximize_metric)

Prepare 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/grid_search/grid_search.py

def prepare(self, pipeline_elements: list, maximize_metric: bool) -> None:
    """
    Prepare hyperparameter search.

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

        maximize_metric:
            Boolean to distinguish between score and error.

    """
    super(RandomGridSearchOptimizer, self).prepare(pipeline_elements, maximize_metric)
    self.start_time = None
    self.n_configurations = self._k
    self.param_grid = list(self.param_grid)
    # create random order in list
    np.random.shuffle(self.param_grid)
    if self.n_configurations is not None:
        # k is maximal all grid items
        if self.n_configurations > len(self.param_grid):
            self.n_configurations = len(self.param_grid)
        self.param_grid = self.param_grid[0:self.n_configurations]