# ConceptDriftStream¶

Generates a stream with concept drift.

A stream generator that adds concept drift or change by joining two streams. This is done by building a weighted combination of two pure distributions that characterizes the target concepts before and after the change.

The sigmoid function is an elegant and practical solution to define the probability that each new instance of the stream belongs to the new concept after the drift. The sigmoid function introduces a gradual, smooth transition whose duration is controlled with two parameters:

• $$p$$, the position of the change.

• $$w$$, the width of the transition.

The sigmoid function at sample $$t$$ is

$f(t) = 1/(1+e^{-4(t-p)/w})$

## Parameters¶

• stream (river.datasets.base.SyntheticDataset) – defaults to None

Original stream

• drift_stream (river.datasets.base.SyntheticDataset) – defaults to None

Drift stream

• position (int) – defaults to 5000

Central position of the concept drift change.

• width (int) – defaults to 1000

Width of concept drift change.

• seed (int) – defaults to None

If int, seed is used to seed the random number generator; If RandomState instance, seed is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

• alpha (float) – defaults to None

Angle of change used to estimate the width of concept drift change. If set, it will override the width parameter. Valid values are in the range (0.0, 90.0].

## Attributes¶

• desc

Return the description from the docstring.

## Examples¶

>>> from river.datasets import synth

>>> dataset = synth.ConceptDriftStream(stream=synth.SEA(seed=42, variant=0),
...                                    drift_stream=synth.SEA(seed=42, variant=1),
...                                    seed=1, position=5, width=2)

>>> for x, y in dataset.take(10):
...     print(x, y)
{0: 6.3942, 1: 0.2501, 2: 2.7502} False
{0: 2.2321, 1: 7.3647, 2: 6.7669} True
{0: 6.3942, 1: 0.2501, 2: 2.7502} False
{0: 8.9217, 1: 0.8693, 2: 4.2192} True
{0: 2.2321, 1: 7.3647, 2: 6.7669} True
{0: 8.9217, 1: 0.8693, 2: 4.2192} True
{0: 0.2979, 1: 2.1863, 2: 5.0535} False
{0: 0.2653, 1: 1.9883, 2: 6.4988} False
{0: 5.4494, 1: 2.2044, 2: 5.8926} False
{0: 8.0943, 1: 0.0649, 2: 8.0581} False


## Methods¶

take

Iterate over the k samples.

Parameters

• k (int)

An optional way to estimate the width of the transition $$w$$ is based on the angle $$lpha$$, $$w = 1/ tan(lpha)$$. Since width corresponds to the number of samples for the transition, the width is rounded to the nearest smaller integer. Notice that larger values of $$lpha$$ result in smaller widths. For $$lpha > 45.0$$, the width is smaller than 1 so values are rounded to 1 to avoid division by zero errors.