Warning
DEPRECATED
-
class sklearn.grid_search.ParameterGrid(param_grid)
[source] -
Grid of parameters with a discrete number of values for each.
Deprecated since version 0.18: This module will be removed in 0.20. Use
sklearn.model_selection.ParameterGrid
instead.Can be used to iterate over parameter value combinations with the Python built-in function iter.
Read more in the User Guide.
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
123456>>>
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
12345678>>> 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
.. automethod:: __init__
-
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