# `RTG_2abrupt`

### *class* capymoa.datasets.RTG_2abrupt[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/datasets/_datasets.py#L194)

Bases: `_DownloadableARFF`

RTG_2abrupt is a synthetic classification problem based on the Random Tree
generator with 2 abrupt drifts.

* Number of instances: 100,000
* Number of attributes: 30
* Number of classes: 5
* `generators.RandomTreeGenerator -o 0 -u 30 -d 20`

This is a snapshot (100k instances with 2 simulated abrupt drifts) of the
synthetic generator based on the one proposed by Domingos and Hulten [1],
producing concepts that in theory should favour decision tree learners.
It constructs a decision tree by choosing attributes at random to split,
and assigning a random class label to each leaf. Once the tree is built,
new examples are generated by assigning uniformly distributed random values
to attributes which then determine the class label via the tree.

**References:**

1. Domingos, Pedro, and Geoff Hulten. “Mining high-speed data streams.”
   In Proceedings of the sixth ACM SIGKDD international conference on
   Knowledge discovery and data mining, pp. 71-80. 2000.

See also [`capymoa.stream.generator.RandomTreeGenerator`](capymoa.stream.generator.RandomTreeGenerator.md#capymoa.stream.generator.RandomTreeGenerator)

#### \_\_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.RTG_2abrupt.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)*