RTG_2abrupt

class capymoa.datasets.RTG_2abrupt

Bases: _DownloadableARFF

RTG_2abrupt is a synthetic classification problem based on the Random Tree generator with 2 abrupt drifts.

• Number of instances: 100,000

• Number of attributes: 30

• Number of classes: 5

• generators.RandomTreeGenerator -o 0 -u 30 -d 20

This is a snapshot (100k instances with 2 simulated abrupt drifts) of the synthetic generator based on the one proposed by Domingos and Hulten [1], producing concepts that in theory should favour decision tree learners. It constructs a decision tree by choosing attributes at random to split, and assigning a random class label to each leaf. Once the tree is built, new examples are generated by assigning uniformly distributed random values to attributes which then determine the class label via the tree.

References:

1. Domingos, Pedro, and Geoff Hulten. “Mining high-speed data streams.” In Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 71-80. 2000.

See also capymoa.stream.generator.RandomTreeGenerator

__init__(

directory: str | Path = get_download_dir(),

auto_download: bool = True,

)

Setup a stream from an ARFF file and optionally download it if missing.

Parameters:

• directory – Where downloads are stored. Defaults to capymoa.datasets.get_download_dir().

• auto_download – Download the dataset if it is missing.

__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.

cli_help() → str

Return cli help string for 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.

classmethod to_stream(path: Path) → InstanceStream

Convert the downloaded and unpacked dataset into a datastream.

schema: Schema