Hyper100k#

class capymoa.datasets.Hyper100k[source]#

Bases: _DownloadableARFF

Hyper100k is a classification problem based on the moving hyperplane generator.

  • Number of instances: 100,000

  • Number of attributes: 10

  • Number of classes: 2

References:

  1. Hulten, Geoff, Laurie Spencer, and Pedro Domingos. “Mining time-changing data streams.” Proceedings of the seventh ACM SIGKDD international conference son Knowledge discovery and data mining. 2001.

__init__(
directory: str | Path = get_download_dir(),
auto_download: bool = True,
)[source]#

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

Parameters:
__iter__() Iterator[_AnyInstance][source]#

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[source]#

Get the next instance in the stream.

Returns:

The next instance in the stream.

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() _AnyInstance[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.

classmethod to_stream(path: Path) InstanceStream[source]#

Convert the downloaded and unpacked dataset into a datastream.

schema: Schema#