PokerHand#

class capymoa.datasets.PokerHand[source]#

Bases: _DownloadableARFF

PokerHand is a classification problem where each instance is an example of a hand consisting of five playing cards drawn from a standard deck of 52.

  • Number of instances: 1,025,009

  • Number of attributes: 10

  • Number of classes: 10

Each card is described using two attributes (suit and rank), for a total of 10 predictive attributes. The task is to predict the poker hand, ranging from nothing to royal flush. Note that the order of cards is important, so there are 480 possible Royal Flush hands instead of just 4.

References:

  1. Cattral, Robert, Franz Oppacher, and Dwight Deugo. “Evolutionary data mining with automatic rule generalization.” Recent Advances in Computers, Computing and Communications (2002).

  2. “poker-hand.” OpenML (2015): https://www.openml.org/d/1567.

__init__(
directory: str | Path | None = None,
auto_download: bool = True,
file_type: Literal['arff', 'csv'] = 'arff',
)[source]#

Setup a stream from a dataset 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.

  • file_type – Download either the "arff" or "csv" dataset asset.

__iter__() → Self[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 a help message

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()[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) → Stream[source]#

Convert the downloaded and unpacked dataset into a datastream.

moa_stream: _InstanceStream | None#
schema: Schema#
stream: Stream#