NUnique¶

Approximate number of unique values counter.

This is basically an implementation of the HyperLogLog algorithm. Adapted from hypy. The code is a bit too terse but it will do for now.

Parameters¶

error_rate – defaults to 0.01

Desired error rate. Memory usage is inversely proportional to this value.
seed (int) – defaults to None

Set the seed to produce identical results.

Attributes¶

n_bits (int)
n_buckets (int)
buckets (list)

Examples¶

>>> import string
>>> from river import stats

>>> alphabet = string.ascii_lowercase
>>> n_unique = stats.NUnique(error_rate=0.2, seed=42)

>>> n_unique.update('a').get()
1

>>> n_unique.update('b').get()
2

>>> for letter in alphabet:
...     n_unique = n_unique.update(letter)
>>> n_unique.get()
31

Lowering the error_rate parameter will increase the precision.

>>> n_unique = stats.NUnique(error_rate=0.01, seed=42)
>>> for letter in alphabet:
...     n_unique = n_unique.update(letter)
>>> n_unique.get()
26

Methods¶

clone

Return a fresh estimator with the same parameters.

The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.

get

Return the current value of the statistic.

revert

Revert and return the called instance.

Parameters

x

update

Update and return the called instance.