Entropy¶

Running entropy.

Parameters¶

fading_factor – defaults to 1

Fading factor.
eps – defaults to 1e-08

Small value that will be added to the denominator to avoid division by zero.

Attributes¶

entropy (float)

The running entropy.
n (int)

The current number of observations.
counter (collections.Counter)

Count the number of times the values have occurred

Examples¶

>>> import math
>>> import random
>>> import numpy as np
>>> from scipy.stats import entropy
>>> from river import stats

>>> def entropy_list(labels, base=None):
...   value,counts = np.unique(labels, return_counts=True)
...   return entropy(counts, base=base)

>>> SEED = 42 * 1337
>>> random.seed(SEED)

>>> entro = stats.Entropy(fading_factor=1)

>>> list_animal = []
>>> for animal, num_val in zip(['cat', 'dog', 'bird'],[301, 401, 601]):
...     list_animal += [animal for i in range(num_val)]
>>> random.shuffle(list_animal)

>>> for animal in list_animal:
...     _ = entro.update(animal)

>>> print(f'{entro.get():.6f}')
1.058093
>>> print(f'{entropy_list(list_animal):.6f}')
1.058093

Methods¶

get

Return the current value of the statistic.

update

Update and return the called instance.

Parameters

x (numbers.Number)

References¶

Sovdat, B., 2014. Updating Formulas and Algorithms for Computing Entropy and Gini Index from Time-Changing Data Streams. arXiv preprint arXiv:1403.6348. ↩