Multivariate Point Outliers

Ver. 1.1.0 (2024-04-26)

This module provides a multivariate benchmark stream with configurable baselines per feature and additional random point outliers.

class mlpro.bf.streams.streams.point_outliers.StreamMLProPOutliers(p_num_dim: int = 4, p_num_instances: int = 1000, p_functions: list[str] = ['sin', 'cos', 'const', 'lin'], p_outlier_rate: float = 0.05, p_seed=None, p_logging=True, **p_kwargs)

Bases: StreamMLProBase

This benchmark stream provides multidimensional instances with configurable baselines per feature. Additionally, random point outliers per feature are induced.

p_num_dimint

The number of dimensions or features of the data. Default = 3.

p_num_instancesint

Total number of instances. The value ‘0’ means indefinite. Default = 1000.

p_functionslist[str]

List of mathematical functions per feature.

p_outlier_ratefloat

A value in [0,1] that defines the number of random outliers in % per feature.

p_seed

Seeding value for the random generator. Default = None (no seeding).

p_logging

Log level (see constants of class Log). Default: Log.C_LOG_ALL.

C_ID = 'PointOutliersND'

C_NAME = 'Point Outliers N-Dim'

C_TYPE = 'Benchmark'

C_VERSION = '1.1.0'

C_SCIREF_ABSTRACT = 'This benchmark stream provides multidimensional instances with configurable baselines per feature. Additionally, random point outliers per feature are induced.'

C_BOUNDARIES = [0, 0]

_setup_feature_space() → MSpace

Custom method to set up the feature space of the stream. It is called by method get_feature_space().

Returns:

feature_space – Feature space of the stream.

Return type:

MSpace

_init_dataset()

Custom method to generate stream data as a numpy array named self._dataset.

_get_next() → Instance

Custom method to determine the next data stream instance. At the end of the stream exception StopIteration is to be raised. See method __next__() for more details.

Returns:

instance – Next instance of data stream or None.

Return type:

Instance

_fct_sin(p_x, p_outlier: bool)

_fct_cos(p_x, p_outlier: bool)

_fct_const(p_x, p_outlier: bool)

_fct_lin(p_x, p_outlier: bool)