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

  Type → np.ndarray | list[_RowT]

  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

  Type → np.ndarray | list[typing.Any] | None

  Default → None

  An optional array of targets.

• feature_names

  Type → Sequence[base.typing.FeatureName] | None

  Default → None

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

• target_names

  Type → Sequence[base.typing.FeatureName] | None

  Default → 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

  Type → bool

  Default → False

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

• seed

  Type → int | None

  Default → None

  Random seed used for shuffling the data.

• chunk_size

  Type → int

  Default → 1024

  Number of rows converted at a time to native Python values. Only applies to 1D numpy arrays, i.e. texts and single-output targets. A larger value uses more memory while iterating.

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 an array of texts:

X = ["foo", "bar"]
dataset = stream.iter_array(X, Y)
for x, y in dataset:
    print(x, y)

foo True
bar False