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iter_array

Iterates over the rows from an array of features and an array of targets.

This method is intended to work with numpy arrays, but should also work with Python lists.

Parameters

  • X (numpy.ndarray)

    A 2D array of features. This can also be a 1D array of strings, which can be the case if you're working with text.

  • y (numpy.ndarray) – defaults to None

    An optional array of targets.

  • feature_names (List[Hashable]) – defaults to None

    An optional list of feature names. The features will be labeled with integers if no names are provided.

  • target_names (List[Hashable]) – defaults to None

    An optional list of output names. The outputs will be labeled with integers if no names are provided. Only applies if there are multiple outputs, i.e. if y is a 2D array.

  • shuffle (bool) – defaults to False

    Indicates whether or not to shuffle the input arrays before iterating over them.

  • seed (int) – defaults to None

    Random seed used for shuffling the data.

Examples

>>> from river import stream
>>> import numpy as np

>>> X = np.array([[1, 2, 3], [11, 12, 13]])
>>> Y = np.array([True, False])

>>> dataset = stream.iter_array(
...     X, Y,
...     feature_names=['x1', 'x2', 'x3']
... )
>>> for x, y in dataset:
...     print(x, y)
{'x1': 1, 'x2': 2, 'x3': 3} True
{'x1': 11, 'x2': 12, 'x3': 13} False

This also works with a array of texts:

>>> X = ["foo", "bar"]
>>> dataset = stream.iter_array(
...     X, Y,
...     feature_names=['x1', 'x2', 'x3']
... )
>>> for x, y in dataset:
...     print(x, y)
foo True
bar False