# `Electricity`

### *class* capymoa.datasets.Electricity[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/datasets/_datasets.py#L224)

Bases: `_DownloadableARFF`

Electricity is a classification problem based on the Australian New
South Wales Electricity Market.

* Number of instances: 45,312
* Number of attributes: 8
* Number of classes: 2 (UP, DOWN)

The Electricity data set was collected from the Australian New South Wales
Electricity Market, where prices are not fixed. It was described by M.
Harries and analysed by Gama. These prices are affected by demand and supply
of the market itself and set every five minutes. The Electricity data set
contains 45,312 instances, where class labels identify the changes of the
price (2 possible classes: up or down) relative to a moving average of the
last 24 hours. An important aspect of this data set is that it exhibits
temporal dependencies. This version of the dataset has been normalised (AKA
`elecNormNew`) and it is the one most commonly used in benchmarks.

**References:**

1. [https://sourceforge.net/projects/moa-datastream/files/Datasets/Classification/elecNormNew.arff.zip/download/](https://sourceforge.net/projects/moa-datastream/files/Datasets/Classification/elecNormNew.arff.zip/download/)

#### \_\_init_\_(directory: [str](https://docs.python.org/3/builtins/stdtypes.html#str) | [Path](https://docs.python.org/3/library/pathlib.html#pathlib.Path) | [None](https://docs.python.org/3/builtins/constants.html#None) = None, auto_download: [bool](https://docs.python.org/3/builtins/functions.html#bool) = True, file_type: [Literal](https://docs.python.org/3/library/typing.html#typing.Literal)['arff', 'csv'] = 'arff')[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/datasets/_downloader.py#L76)

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()`](capymoa.datasets.md#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](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.datasets.Electricity.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#L379)

Return a help message

#### 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/datasets/_downloader.py#L113)

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/datasets/_downloader.py#L110)

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/datasets/_downloader.py#L104)

Return `True` if the stream have more instances to read.

#### next_instance()[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/datasets/_downloader.py#L107)

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/datasets/_downloader.py#L116)

Restart the stream to read instances from the beginning.

#### *classmethod* to_stream(path: [Path](https://docs.python.org/3/library/pathlib.html#pathlib.Path)) → [Stream](capymoa.stream.Stream.md#capymoa.stream.Stream)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/datasets/_downloader.py#L96)

Convert the downloaded and unpacked dataset into a datastream.

#### moa_stream *: \_InstanceStream | [None](https://docs.python.org/3/builtins/constants.html#None)*

#### schema *: [Schema](capymoa.stream.Schema.md#capymoa.stream.Schema)*

#### stream *: [Stream](capymoa.stream.Stream.md#capymoa.stream.Stream)*