BPt.p.Choice#

class BPt.p.Choice(*args, **kwargs)[source]#

BPt parameter wrapper around nevergrad.p.Choice.

Examples

Constructing a Choice parameter:

In [1]: import BPt as bp

In [2]: class_weight_choice = bp.p.Choice(['balanced', None])

In [3]: class_weight_choice
Out[3]: Choice(['balanced', None])

This parameter can then be set when setting Pipeline Pieces. In the example below we will set it to be the params when constructing a logistic ridge model.

In [4]: params = {'class_weight': class_weight_choice}

In [5]: model = bp.Model('ridge', params=params)

In [6]: model
Out[6]: Model(obj='ridge', params={'class_weight': Choice(['balanced', None])})

Attributes

`args`	Value of the positional arguments.
`choices`	The different options, as a Tuple Parameter
`descriptors`	Stores the descriptor information for this parameter.
`dimension`	Dimension of the standardized space for this parameter i.e size of the vector returned by get_standardized_data(reference=...)
`generation`	Generation of the parameter (children are current generation + 1)
`index`	Index of the chosen option
`indices`	Array of indices of the chosen option
`kwargs`	Value of the keyword arguments.
`losses`	Possibly multiobjective losses which were told to the optimizer along this parameter.
`name`	Name of the parameter This is used to keep track of how this Parameter is configured (included through internal/model parameters), mostly for reproducibility A default version is always provided, but can be overriden directly through the attribute, or through the set_name method (which allows chaining).
`random_state`	Random state the instrumentation and the optimizers pull from.

Methods

`add_layer`(other)	Adds a layer which will modify the object behavior
`copy`()	Creates a full copy of the parameter (with new unique uid).
`freeze`()	Prevents the parameter from changing value again (through value, mutate etc...)
`get_standardized_data`(*, reference)	Get the standardized data representing the value of the instance as an array in the optimization space.
`get_value_hash`()	Hashable object representing the current value of the instance
`mutate`()	Mutate parameters of the instance, and then its value
`recombine`(*others)	Update value and parameters of this instance by combining it with other instances.
`register_cheap_constraint`(func[, as_layer])	Registers a new constraint on the parameter values.
`sample`()	Sample a new instance of the parameter.
`satisfies_constraints`()	Whether the instance satisfies the constraints added through the register_cheap_constraint method
`set_name`(name)	Sets a name and return the current instrumentation (for chaining)
`set_standardized_data`(data, *[, reference])	Updates the value of the provided reference (or self) using the standardized data.
`spawn_child`([new_value])	Creates a new instance which shares the same random generator than its parent, is sampled from the same data, and mutates independently from the parentp.
`to_grid`()	This method will attempt to convert from the current BPt / nevergrad style parameter to a sklearn-grid search compatible one.