RBFm_100k#

class capymoa.datasets.RBFm_100k[source]#

Bases: DownloadARFFGzip

RBFm_100k is a synthetic classification problem based on the Radial Basis Function generator.

Number of instances: 100,000
Number of attributes: 10
generators.RandomRBFGeneratorDrift -s 1.0E-4 -c 5

This is a snapshot (100k instances) of the synthetic generator RBF (Radial Basis Function), which works as follows: A fixed number of random centroids are generated. Each center has a random position, a single standard deviation, class label and weight. New examples are generated by selecting a center at random, taking weights into consideration so that centers with higher weight are more likely to be chosen. A random direction is chosen to offset the attribute values from the central point. The length of the displacement is randomly drawn from a Gaussian distribution with standard deviation determined by the chosen centroid. The chosen centroid also determines the class label of the example. This effectively creates a normally distributed hypersphere of examples surrounding each central point with varying densities. Only numeric attributes are generated.

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.