RandomTreeGenerator#

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

Bases: Stream

Stream generator for a stream based on a randomly generated tree.

>>> from capymoa.stream.generator import RandomTreeGenerator
...
>>> stream = RandomTreeGenerator()
>>> stream.next_instance()
LabeledInstance(
    Schema(generators.RandomTreeGenerator ),
    x=ndarray(..., 10),
    y_index=0,
    y_label='class1'
)
>>> stream.next_instance().x
array([4.        , 2.        , 2.        , 1.        , 4.        ,
       0.39717434, 0.34751803, 0.29405703, 0.50648363, 0.11596709])

__init__( instance_random_seed: int = 1, tree_random_seed: int = 1, num_classes: int = 2, num_nominals: int = 5, num_numerics: int = 5, num_vals_per_nominal: int = 5, max_tree_depth: int = 5, first_leaf_level: int = 3, leaf_fraction: float = 0.15, )[source]#

Construct a random tree generator.

Parameters:

instance_random_seed – Seed for random generation of instances.
tree_random_seed – Seed for random generation of tree.
num_classes – The number of classes to generate.
num_nominals – The number of nominal attributes to generate.
num_numerics – The number of numeric attributes to generate.
num_vals_per_nominal – The number of values to generate per nominal attribute.
max_tree_depth – The maximum depth of the tree concept.
first_leaf_level – The first level of the tree above max_tree_depth that can have leaves
leaf_fraction – The fraction of leaves per level from first leaf level onwards.

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.