expand_param_grid¶

Expands a grid of parameters.

This method can be used to generate a list of model parametrizations from a dictionary where each parameter is associated with a list of possible parameters. In other words, it expands a grid of parameters.

Typically, this method can be used to create copies of a given model with different parameter choices. The models can then be used as part of a model selection process, such as a selection.SuccessiveHalvingClassifier or a selection.EWARegressor.

The syntax for the parameter grid is quite flexible. It allows nesting parameters and can therefore be used to generate parameters for a pipeline.

Parameters¶

• model (base.Estimator)

• grid (dict)

The grid of parameters to expand. The provided dictionary can be nested. The only requirement is that the values at the leaves need to be lists.

Examples¶

As an initial example, we can expand a grid of parameters for a single model.

>>> from river import linear_model
>>> from river import optim
>>> from river import utils

>>> model = linear_model.LinearRegression()

>>> grid = {'optimizer': [optim.SGD(.1), optim.SGD(.01), optim.SGD(.001)]}
>>> models = utils.expand_param_grid(model, grid)
>>> len(models)
3

>>> models[0]
LinearRegression (
optimizer=SGD (
lr=Constant (
learning_rate=0.1
)
)
loss=Squared ()
l2=0.
l1=0.
intercept_init=0.
intercept_lr=Constant (
learning_rate=0.01
)
initializer=Zeros ()
)


You can expand parameters for multiple choices like so:

>>> grid = {
...     'optimizer': [
...         (optim.SGD, {'lr': [.1, .01, .001]}),
...         (optim.Adam, {'lr': [.1, .01, .01]})
...     ]
... }
>>> models = utils.expand_param_grid(model, grid)
>>> len(models)
6


You may specify a grid of parameters for a pipeline via nesting:

>>> from river import feature_extraction

>>> model = (
...     feature_extraction.BagOfWords() |
...     linear_model.LinearRegression()
... )

>>> grid = {
...     'BagOfWords': {
...         'strip_accents': [False, True]
...     },
...     'LinearRegression': {
...         'optimizer': [
...             (optim.SGD, {'lr': [.1, .01]}),