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]#

Construct a Stream from a MOA stream object.

Usually, you will want to construct a Stream using the capymoa.stream.stream_from_file() function.

Parameters:

  • moa_stream – The MOA stream object to read instances from. Is None if the stream is created from a numpy array.

  • schema – The schema of the stream. If None, the schema is inferred from the moa_stream.

  • CLI – Additional command line arguments to pass to the MOA stream.

Raises:

  • ValueError – If no schema is provided and no moa_stream is provided.

  • ValueError – If command line arguments are provided without a moa_stream.

__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.

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() → _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.

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

Convert the dataset to a MOA stream.

Parameters:

stream – The path to the dataset.

Returns:

A MOA stream.