TargetAgg

Computes a streaming aggregate of the target values.

This transformer is identical to feature_extraction.Agg, the only difference is that it operates on the target rather than on a feature. At each step, the running statistic how of target values in group by is updated with the target. It is therefore a supervised transformer.

Parameters

• by

  Type → str | list[str] | None

  The feature by which to group the target values. All the data is included in the aggregate if this is None.

• how

  Type → stats.base.Univariate | utils.Rolling | utils.TimeRolling

  The statistic to compute.

• target_name

  Default → y

  The target name which is used in the result.

Attributes

• state

  Return the current values for each group as a series.

• target_name

Examples

Consider the following dataset, where the second value of each value is the target:

dataset = [
    ({'country': 'France', 'place': 'Taco Bell'}, 42),
    ({'country': 'Sweden', 'place': 'Burger King'}, 16),
    ({'country': 'France', 'place': 'Burger King'}, 24),
    ({'country': 'Sweden', 'place': 'Taco Bell'}, 58),
    ({'country': 'Sweden', 'place': 'Burger King'}, 20),
    ({'country': 'France', 'place': 'Taco Bell'}, 50),
    ({'country': 'France', 'place': 'Burger King'}, 10),
    ({'country': 'Sweden', 'place': 'Taco Bell'}, 80)
]

As an example, let's perform a target encoding of the place feature. Instead of simply updating a running average, we use a stats.BayesianMean which allows us to incorporate some prior knowledge. This makes subsequent models less prone to overfitting. Indeed, it dampens the fact that too few samples might have been seen within a group.

from river import feature_extraction
from river import stats

agg = feature_extraction.TargetAgg(
    by='place',
    how=stats.BayesianMean(
        prior=3,
        prior_weight=1
    )
)

for x, y in dataset:
    print(agg.transform_one(x))
    agg.learn_one(x, y)

{'y_bayes_mean_by_place': 3.0}
{'y_bayes_mean_by_place': 3.0}
{'y_bayes_mean_by_place': 9.5}
{'y_bayes_mean_by_place': 22.5}
{'y_bayes_mean_by_place': 14.333}
{'y_bayes_mean_by_place': 34.333}
{'y_bayes_mean_by_place': 15.75}
{'y_bayes_mean_by_place': 38.25}

Just like with feature_extraction.Agg, we can specify multiple features on which to group the data:

agg = feature_extraction.TargetAgg(
    by=['place', 'country'],
    how=stats.BayesianMean(
        prior=3,
        prior_weight=1
    )
)

for x, y in dataset:
    print(agg.transform_one(x))
    agg.learn_one(x, y)

{'y_bayes_mean_by_place_and_country': 3.0}
{'y_bayes_mean_by_place_and_country': 3.0}
{'y_bayes_mean_by_place_and_country': 3.0}
{'y_bayes_mean_by_place_and_country': 3.0}
{'y_bayes_mean_by_place_and_country': 9.5}
{'y_bayes_mean_by_place_and_country': 22.5}
{'y_bayes_mean_by_place_and_country': 13.5}
{'y_bayes_mean_by_place_and_country': 30.5}

agg.state

place        country
Taco Bell    France     31.666667
Burger King  Sweden     13.000000
             France     12.333333
Taco Bell    Sweden     47.000000
Name: y_bayes_mean_by_place_and_country, dtype: float64

This transformer can also be used in conjunction with utils.TimeRolling. The latter requires a t argument, which is a timestamp that indicates when the current row was observed. For instance, we can calculate the average (how) revenue (on) for each place (by) over the last 7 days (t):

import datetime as dt
import random
import string
from river import utils

agg = feature_extraction.TargetAgg(
    by="group",
    how=utils.TimeRolling(stats.Mean(), dt.timedelta(days=7))
)

for day in range(366):
    g = random.choice(string.ascii_lowercase)
    x = {"group": g}
    y = string.ascii_lowercase.index(g) + random.random()
    t = dt.datetime(2023, 1, 1) + dt.timedelta(days=day)
    agg.learn_one(x, y, t=t)

Methods

learn_one

Update with a set of features x and a target y.

Parameters

• x — 'dict'
• y — 'base.typing.Target'
• t — defaults to None

transform_one

Transform a set of features x.

Parameters

• x — 'dict'

Returns

dict: The transformed values.

1. Streaming groupbys in pandas for big datasets