sklearn.grid_search.ParameterGrid¶
- class sklearn.grid_search.ParameterGrid(param_grid)¶
Grid of parameters with a discrete number of values for each.
Can be used to iterate over parameter value combinations with the Python built-in function iter.
Parameters : param_grid : dict of string to sequence, or sequence of such
The parameter grid to explore, as a dictionary mapping estimator parameters to sequences of allowed values.
An empty dict signifies default parameters.
A sequence of dicts signifies a sequence of grids to search, and is useful to avoid exploring parameter combinations that make no sense or have no effect. See the examples below.
See also
- GridSearchCV
- uses ParameterGrid to perform a full parallelized parameter search.
Examples
>>> from sklearn.grid_search import ParameterGrid >>> param_grid = {'a': [1, 2], 'b': [True, False]} >>> list(ParameterGrid(param_grid)) == ( ... [{'a': 1, 'b': True}, {'a': 1, 'b': False}, ... {'a': 2, 'b': True}, {'a': 2, 'b': False}]) True
>>> grid = [{'kernel': ['linear']}, {'kernel': ['rbf'], 'gamma': [1, 10]}] >>> list(ParameterGrid(grid)) == [{'kernel': 'linear'}, ... {'kernel': 'rbf', 'gamma': 1}, ... {'kernel': 'rbf', 'gamma': 10}] True
- __init__(param_grid)¶