KDD99#

class capymoa.datasets.KDD99[source]#

Bases: _DownloadableARFF

KDD99 is a network intrusion detection problem based on a 10% stratified subsample of the 1998 DARPA Intrusion Detection Evaluation Program data.

  • Number of instances: 494,020

  • Number of attributes: 41

  • Number of classes: 23

The task is to distinguish between normal connections and different types of network intrusions (attacks), grouped into four main categories: denial-of-service, unauthorized access from a remote machine, unauthorized access to local superuser privileges, and surveillance/probing.

References:

  1. Stolfo, Salvatore, Wei Fan, Wenke Lee, Andreas Prodromidis, and Philip Chan. “KDD Cup 1999 Data.” UCI Machine Learning Repository (1999): https://doi.org/10.24432/C51C7N.

  2. “KDDCup99.” OpenML (2014): https://www.openml.org/d/1113.

__init__(
directory: str | Path | None = None,
auto_download: bool = True,
file_type: Literal['arff', 'csv'] = 'arff',
)[source]#

Setup a stream from a dataset file and optionally download it if missing.

Parameters:

  • directory – Where downloads are stored. Defaults to capymoa.datasets.get_download_dir().

  • auto_download – Download the dataset if it is missing.

  • file_type – Download either the "arff" or "csv" dataset asset.

__iter__() → Self[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 a help message

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()[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) → Stream[source]#

Convert the downloaded and unpacked dataset into a datastream.

moa_stream: _InstanceStream | None#
schema: Schema#
stream: Stream#