# `SEA`

### *class* capymoa.stream.generator.SEA[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/stream/generator.py#L136)

Bases: [`MOAStream`](capymoa.stream.MOAStream.md#capymoa.stream.MOAStream)

Generates SEA concepts functions.

```pycon
>>> from capymoa.stream.generator import SEA
...
>>> stream = SEA()
>>> stream.next_instance()
LabeledInstance(
    Schema(generators.SEAGenerator ),
    x=[7.309 4.101 2.077],
    y_index=1,
    y_label='groupB'
)
>>> stream.next_instance().x
array([6.58867239, 7.10739628, 1.52736201])
```

Street, W. N., & Kim, Y. (2001). A streaming ensemble algorithm (SEA) for
large-scale classification. [doi:10.1145/502512.502568](https://doi.org/10.1145/502512.502568)

#### \_\_init_\_(instance_random_seed: [int](https://docs.python.org/3/builtins/functions.html#int) = 1, function: [int](https://docs.python.org/3/builtins/functions.html#int) = 1, balance_classes: [bool](https://docs.python.org/3/builtins/functions.html#bool) = False, noise_percentage: [int](https://docs.python.org/3/builtins/functions.html#int) = 10)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/stream/generator.py#L156)

Construct a SEA datastream generator.

* **Parameters:**
  * **instance_random_seed** – Seed for random generation of instances, defaults to 1
  * **function** – Classification function used, as defined in the original paper, defaults to 1
  * **balance_classes** – Balance the number of instances of each class, defaults to False
  * **noise_percentage** – Percentage of noise to add to the data, defaults to 10

#### \_\_iter_\_() → [Self](https://docs.python.org/3/library/typing.html#typing.Self)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/stream/_stream.py#L356)

Get an iterator over the stream.

This will NOT restart the stream if it has already been iterated over.
Please use the [`restart()`](#capymoa.stream.generator.SEA.restart) method to restart the stream.

* **Yield:**
  An iterator over the stream.

#### \_\_next_\_() → \_AnyInstance[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/stream/_stream.py#L366)

Get the next instance in the stream.

* **Returns:**
  The next instance in the stream.

#### cli_help() → [str](https://docs.python.org/3/builtins/stdtypes.html#str)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/stream/_stream.py#L466)

Return cli help string for the stream.

#### get_moa_stream() → InstanceStream | [None](https://docs.python.org/3/builtins/constants.html#None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/stream/_stream.py#L502)

Get the MOA stream object if it exists.

#### get_schema() → [Schema](capymoa.stream.Schema.md#capymoa.stream.Schema)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/stream/_stream.py#L498)

Return the schema of the stream.

#### has_more_instances() → [bool](https://docs.python.org/3/builtins/functions.html#bool)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/stream/_stream.py#L474)

Return True if the stream have more instances to read.

#### next_instance() → \_AnyInstance[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/stream/_stream.py#L478)

Return the next instance in the stream.

* **Raises:**
  [**ValueError**](https://docs.python.org/3/builtins/exceptions.html#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]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/stream/_stream.py#L506)

Restart the stream to read instances from the beginning.