HyperPlaneClassification#

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

Bases: Stream

Generates HyperPlane concepts functions.

>>> from capymoa.stream.generator import HyperPlaneClassification
...
>>> stream = HyperPlaneClassification()
>>> stream.next_instance()
LabeledInstance(
    Schema(generators.HyperplaneGenerator ),
    x=ndarray(..., 10),
    y_index=0,
    y_label='class1'
)
>>> stream.next_instance().x
array([0.00485253, 0.85225356, 0.02341807, 0.70500995, 0.27502995,
       0.0753878 , 0.61059154, 0.95493077, 0.2740691 , 0.19020221])
__init__(
instance_random_seed: int = 1,
number_of_classes: int = 2,
number_of_attributes: int = 10,
number_of_drifting_attributes: int = 2,
magnitude_of_change: float = 0.0,
noise_percentage: int = 5,
sigma_percentage: int = 10,
)[source]#

Construct a HyperPlane Classification datastream generator.

Parameters:

  • instance_random_seed – Seed for random generation of instances, defaults to 1

  • number_of_classes – The number of classes of the generated instances, defaults to 2

  • number_of_attributes – The number of attributes of the generated instances, defaults to 10

  • number_of_drifting_attributes – The number of drifting attributes, defaults to 2

  • magnitude_of_change – Magnitude of change in the generated instances, defaults to 0.0

  • noise_percentage – Percentage of noise to add to the data, defaults to 10

  • sigma_percentage – Percentage of sigma 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.