Sensor

class capymoa.datasets.Sensor

Bases: DownloadARFFGzip

Sensor stream is a classification problem based on indoor sensor data.

• Number of instances: 2,219,803

• Number of attributes: 5

• Number of classes: 54

The stream contains temperature, humidity, light, and sensor voltage collected from 54 sensors deployed in Intel Berkeley Research Lab. The classification objective is to predict the sensor ID.

References:

1. https://www.cse.fau.edu/~xqzhu/stream.html

CLI_help() → str

Return cli help string for the stream.

__init__(

directory: str = PosixPath('data'),

auto_download: bool = True,

CLI: str | None = None,

schema: str | None = None,

)

download(working_directory: Path) → Path

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

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

Get the MOA stream object if it exists.

get_path()

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() → LabeledInstance | RegressionInstance

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.

to_stream(stream: Path) → Any

Convert the dataset to a MOA stream.

Parameters:

stream – The path to the dataset.

Returns:

A MOA stream.