Covtype#

class capymoa.datasets.Covtype[source]#

Bases: _DownloadableARFF

The classic covertype (/covtype) classification problem

  • Number of instances: 581,012 (30m^2 cells)

  • Number of attributes: 54 (10 continuous, 44 categorical)

  • Number of classes: 7 (forest cover types)

Forest Covertype (or simply covtype) contains the forest cover type for 30 x 30 meter cells obtained from US Forest Service (USFS) Region 2 Resource Information System (RIS) data.

References:

  1. Blackard,Jock. (1998). Covertype. UCI Machine Learning Repository. https://doi.org/10.24432/C50K5N.

  2. https://archive.ics.uci.edu/ml/datasets/Covertype

See Also:

  • CovtFD - Covtype with simulated feature drifts

  • CovtypeNorm - A normalized version of the classic covertype dataset

  • CovtypeTiny - A truncated version of the classic covertype dataset

__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#