Grid Search Algorithm¶
Grid Search¶
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class
orion.algo.gridsearch.
GridSearch
(space, n_values=100, seed=None)[source]¶ Grid Search algorithm
Parameters: - n_values: int or dict
Number of points for each dimensions, or dictionary specifying number of points for each dimension independently (name, n_values). For categorical dimensions, n_values will not be used, and all categories will be used to build the grid.
Attributes: configuration
Return tunable elements of this algorithm in a dictionary form appropriate for saving.
is_done
Return True when all grid has been covered.
- requires_dist
- requires_type
state_dict
Return a state dict that can be used to reset the state of the algorithm.
Methods
build_grid
(space, n_values[, max_trials])Build a grid of trials set_state
(state_dict)Reset the state of the algorithm based on the given state_dict suggest
([num])Return the entire grid of suggestions -
static
build_grid
(space, n_values, max_trials=10000)[source]¶ Build a grid of trials
Parameters: - n_values: int or dict
Number of points for each dimensions, or dictionary specifying number of points for each dimension independently (name, n_values). For categorical dimensions, n_values will not be used, and all categories will be used to build the grid.
- max_trials: int
Maximum number of trials for the grid. If n_values lead to more trials than max_trials, the n_values will be adjusted down. Will raise ValueError if it is impossible to build a grid smaller than max_trials (for instance if choices are too large).
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configuration
¶ Return tunable elements of this algorithm in a dictionary form appropriate for saving.
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is_done
¶ Return True when all grid has been covered.
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set_state
(state_dict)[source]¶ Reset the state of the algorithm based on the given state_dict
Parameters: - state_dict: dict
Dictionary representing state of an algorithm
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state_dict
¶ Return a state dict that can be used to reset the state of the algorithm.
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suggest
(num=1)[source]¶ Return the entire grid of suggestions
Returns: - list of points or None
A list of lists representing points suggested by the algorithm. The algorithm may opt out if it cannot make a good suggestion at the moment (it may be waiting for other trials to complete), in which case it will return None.