# `RegressionInstance`

### *class* capymoa.core.RegressionInstance[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/core/_instance.py#L387)

Bases: [`Instance`](capymoa.core.Instance.md#capymoa.core.Instance)

An [`Instance`](capymoa.core.Instance.md#capymoa.core.Instance) with a continuous target value.

Most of the time, regression datastreams will automatically return instances
for you with the target value. For example, the [`capymoa.datasets.Fried`](capymoa.datasets.Fried.md#capymoa.datasets.Fried)
dataset:

```pycon
>>> from capymoa.datasets import Fried
...
>>> from capymoa.core import RegressionInstance
>>> stream = Fried()
>>> instance: RegressionInstance = stream.next_instance()
>>> instance.y_value
17.949
>>> instance.x
array([0.487, 0.072, 0.004, 0.833, 0.765, 0.6  , 0.132, 0.886, 0.073,
       0.342])
```

#### \_\_init_\_(schema: [Schema](capymoa.stream.Schema.md#capymoa.stream.Schema), instance: InstanceExample | [tuple](https://docs.python.org/3/builtins/stdtypes.html#tuple)[[ndarray](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)[[tuple](https://docs.python.org/3/builtins/stdtypes.html#tuple)[[Any](https://docs.python.org/3/library/typing.html#typing.Any), ...], [dtype](https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype)[float64]], float64]) → [None](https://docs.python.org/3/builtins/constants.html#None)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/core/_instance.py#L407)

Creates a new instance.

Its recommended that you prefer using [`from_array()`](#capymoa.core.RegressionInstance.from_array) or
[`from_java_instance()`](#capymoa.core.RegressionInstance.from_java_instance) to create instances, as they provide a more
user-friendly interface.

* **Parameters:**
  * **schema** – A schema that describes the datastream the instance belongs to.
  * **instance** – A vector of features (float values) or a Java instance.
* **Raises:**
  [**TypeError**](https://docs.python.org/3/builtins/exceptions.html#TypeError) – If the given instance type is of an unsupported type.

#### *classmethod* from_array(schema: [Schema](capymoa.stream.Schema.md#capymoa.stream.Schema), x: [ndarray](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)[[tuple](https://docs.python.org/3/builtins/stdtypes.html#tuple)[[Any](https://docs.python.org/3/library/typing.html#typing.Any), ...], [dtype](https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype)[float64]], y_value: float64) → [RegressionInstance](#capymoa.core.RegressionInstance)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/core/_instance.py#L417)

Creates a new regression instance from a schema, feature vector, and target value.

This is useful in the rare cases you need to create custom regression instances
from scratch. In most cases, your datastream will automatically create
these for you.

```pycon
>>> from capymoa.stream import Schema
...
>>> from capymoa.core import LabeledInstance
>>> import numpy as np
>>> schema = Schema.from_custom(
...     ["f1", "f2", "target"],
...     target="target",
...     name="CustomDataset",
... )
>>> x = np.array([0.1, 0.2])
>>> instance = RegressionInstance.from_array(schema, x, 0.5)
>>> instance
RegressionInstance(
    Schema(CustomDataset),
    x=[0.1 0.2],
    y_value=0.5
)
>>> instance.y_value
0.5
>>> instance.java_instance.toString()
'0.1,0.2,0.5,'
```

* **Parameters:**
  * **schema** – A schema describing the datastream the instance belongs to.
  * **x** – A vector of features [`numpy.ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray) containing float values.
  * **y_value** – A float value representing the target value or dependent variable.
* **Returns:**
  A new [`RegressionInstance`](#capymoa.core.RegressionInstance) object.

#### *classmethod* from_csv_row(schema: [Schema](capymoa.stream.Schema.md#capymoa.stream.Schema), row: [Sequence](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence)[[str](https://docs.python.org/3/builtins/stdtypes.html#str)]) → [Instance](capymoa.core.Instance.md#capymoa.core.Instance)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/core/_instance.py#L132)

Create an instance from a CSV row.

```pycon
>>> from capymoa.stream import Schema
>>> from capymoa.core import Instance
>>> schema = Schema.from_custom(
...     ["feature1", "feature2", "target"],
...     target="target",
...     categories={"feature2": ["A", "B"], "target": ["yes", "no"]},
...     name="classification-example"
... )
>>> row = ["1.0", "A", "yes"]
>>> instance = Instance.from_csv_row(schema, row)
>>> instance
Instance(
    Schema(classification-example),
    x=[1. 0.],
)
>>> instance.x
array([1., 0.])
```

* **Parameters:**
  * **schema** – A schema providing the structure of each row. Like the header of the CSV.
  * **row** – A sequence of strings representing a CSV row.
* **Raises:**
  [**ValueError**](https://docs.python.org/3/builtins/exceptions.html#ValueError) – If an attribute type is unsupported.
* **Returns:**
  A new [`Instance`](capymoa.core.Instance.md#capymoa.core.Instance) object.

#### *classmethod* from_java_instance(schema: [Schema](capymoa.stream.Schema.md#capymoa.stream.Schema), java_instance: InstanceExample) → [Instance](capymoa.core.Instance.md#capymoa.core.Instance)[[source]](https://github.com/adaptive-machine-learning/CapyMOA/blob/3e255b1/src/capymoa/core/_instance.py#L99)

#### *property* java_instance *: InstanceExample*

Returns a representation of the instance in Java for use in MOA. This
method is for advanced users who want to directly interact with MOA’s Java
API.

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

Returns the schema of the instance and the stream it belongs to.

#### *property* x *: [ndarray](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray)[[tuple](https://docs.python.org/3/builtins/stdtypes.html#tuple)[[Any](https://docs.python.org/3/library/typing.html#typing.Any), ...], [dtype](https://numpy.org/doc/stable/reference/generated/numpy.dtype.html#numpy.dtype)[float64]]*

Returns a feature vector containing float values for the instance.

* NaN values indicate missing features.

#### *property* y_value *: float64*

Returns the target value of the instance.