sklearn.model_selection.ParameterGrid

class sklearn.model_selection.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. The order of the generated parameter combinations is deterministic.

Read more in the User Guide.

Parameters:

param_griddict of str 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.model_selection 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
>>> ParameterGrid(grid)[1] == {'kernel': 'rbf', 'gamma': 1}
True