SEA#

class capymoa.stream.generator.SEA[source]#

Bases: Stream

Generates SEA concepts functions.

>>> from capymoa.stream.generator import SEA
...
>>> stream = SEA()
>>> stream.next_instance()
LabeledInstance(
    Schema(generators.SEAGenerator ),
    x=ndarray(..., 3),
    y_index=1,
    y_label='groupB'
)
>>> stream.next_instance().x
array([6.58867239, 7.10739628, 1.52736201])

Street, W. N., & Kim, Y. (2001). A streaming ensemble algorithm (SEA) for large-scale classification. doi:10.1145/502512.502568

__init__( instance_random_seed: int = 1, function: int = 1, balance_classes: bool = False, noise_percentage: int = 10, )[source]#

Construct a SEA datastream generator.

Parameters:

instance_random_seed – Seed for random generation of instances, defaults to 1
function – Classification function used, as defined in the original paper, defaults to 1
balance_classes – Balance the number of instances of each class, defaults to False
noise_percentage – Percentage of noise to add to the data, defaults to 10

CLI_help() → str[source]#: Return cli help string for the stream.

get_moa_stream() → InstanceStream | None[source]#: Get the MOA stream object if it exists.

get_schema() → Schema[source]#: Return the schema of the stream.

has_more_instances() → bool[source]#: Return True if the stream have more instances to read.

next_instance() → LabeledInstance | RegressionInstance[source]#

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()[source]#: Restart the stream to read instances from the beginning.