LEDGeneratorDrift

class capymoa.stream.generator.LEDGeneratorDrift

Bases: MOAStream

An LED Generator Drift

>>> from capymoa.stream.generator import LEDGeneratorDrift
...
>>> stream = LEDGeneratorDrift()
>>> stream.next_instance()
LabeledInstance(
    Schema(generators.LEDGeneratorDrift -d 7),
    x=[1. 1. 0. ... 0. 0. 0.],
    y_index=5,
    y_label='5'
)
>>> stream.next_instance().x
array([0., 0., 1., 0., 1., 0., 1., 1., 1., 1., 0., 1., 1., 0., 1., 0., 1.,
       0., 0., 1., 1., 0., 1., 1.])

__init__(

instance_random_seed: int = 1,

noise_percentage: int = 10,

reduce_data: bool = False,

number_of_attributes_with_drift: int = 7,

)

Construct an LED Generator Drift

Parameters:

• instance_random_seed – Seed for random generation of instances.

• noise_percentage – Percentage of noise to add to the data

• reduce_data – Reduce the data to only contain 7 relevant binary attributes

• number_of_attributes_with_drift – Number of attributes with drift

CLI_help() → str

Return cli help string for the stream.

__iter__() → Iterator[_AnyInstance]

Get an iterator over the stream.

This will NOT restart the stream if it has already been iterated over. Please use the restart() method to restart the stream.

Yield:

An iterator over the stream.

__next__() → _AnyInstance

Get the next instance in the stream.

Returns:

The next instance in the stream.

get_moa_stream() → InstanceStream | None

Get the MOA stream object if it exists.

get_schema() → Schema

Return the schema of the stream.

has_more_instances() → bool

Return True if the stream have more instances to read.

next_instance() → _AnyInstance

Return the next instance in the stream.

Raises:

ValueError – If the machine learning task is neither a regression nor a classification task.

Returns:

A labeled instances or a regression depending on the schema.

restart()

Restart the stream to read instances from the beginning.