ElectricityTiny#

class capymoa.datasets.ElectricityTiny[source]#

Bases: DownloadARFFGzip

A truncated version of the Electricity dataset with 1000 instances.

This is a tiny version (2k instances) of the Electricity widely used dataset described by M. Harries. This should only be used for quick tests, not for benchmarking algorithms.

See Electricity for the widely used electricity dataset.

CLI_help() → str[source]#: Return cli help string for the stream.

__init__( directory: str = PosixPath('data'), auto_download: bool = True, CLI: str | None = None, schema: str | None = None, )[source]#

download(working_directory: Path) → Path[source]#

Download the dataset and return the path to the downloaded dataset within the working directory.

Parameters:: working_directory – The directory to download the dataset to.
Returns:: The path to the downloaded dataset within the working directory.

extract(stream_archive: Path) → Path[source]#

Extract the dataset from the archive and return the path to the extracted dataset.

Parameters:: stream_archive – The path to the archive containing the dataset.
Returns:: The path to the extracted dataset.

get_moa_stream() → InstanceStream | None[source]#: Get the MOA stream object if it exists.

get_path()[source]#

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.

to_stream(stream: Path) → Any[source]#

Convert the dataset to a MOA stream.

Parameters:: stream – The path to the dataset.
Returns:: A MOA stream.