# SEA

SEA synthetic dataset.

Implementation of the data stream with abrupt drift described in [^/0.27.0/api/datasets/synth/SEA/#1]. Each observation is composed of 3 features. Only the first two features are relevant. The target is binary, and is positive if the sum of the features exceeds a certain threshold. There are 4 thresholds to choose from. Concept drift can be introduced by switching the threshold anytime during the stream.

- **Variant 0**: `True` if \\(att1 + att2 \> 8\\)
- **Variant 1**: `True` if \\(att1 + att2 \> 9\\)
- **Variant 2**: `True` if \\(att1 + att2 \> 7\\)
- **Variant 3**: `True` if \\(att1 + att2 \> 9.5\\)

## Parameters

- **variant**

  *Default* → `0`

  Determines the classification function to use. Possible choices are 0, 1, 2, 3.
- **noise**

  *Default* → `0.0`

  Determines the amount of observations for which the target sign will be flipped.
- **seed**

  *Type* → `int | None`

  *Default* → `None`

  Random seed number used for reproducibility.

## Attributes

- **desc**

  Return the description from the docstring.

## Examples

```python
from river.datasets import synth

dataset = synth.SEA(variant=0, seed=42)

for x, y in dataset.take(5):
    print(x, y)
```

```text
{0: 6.39426, 1: 0.25010, 2: 2.75029} False
{0: 2.23210, 1: 7.36471, 2: 6.76699} True
{0: 8.92179, 1: 0.86938, 2: 4.21921} True
{0: 0.29797, 1: 2.18637, 2: 5.05355} False
{0: 0.26535, 1: 1.98837, 2: 6.49884} False
```

## Methods

> [!NOTE]
>
> **take**
>
> Iterate over the k samples.
>
> **Parameters**
>
> - **k** — `int`

[^/0.27.0/api/datasets/synth/SEA/#1]: [A Streaming Ensemble Algorithm (SEA) for Large-Scale Classification](<http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.482.3991&amp;rep=rep1&amp;type=pdf>)