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 (Union[str, List[str]])

  The feature by which to group the target values.

• how (river.stats.base.Univariate)

  The statistic to compute.

• target_name – defaults to target

  The target name which is used in the result.

Attributes

• groups

  Maps group keys to univariate statistics.

• feature_name

  The name of the feature in the output.

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 = agg.learn_one(x, y)
{'target_bayes_mean_by_place': 3.0}
{'target_bayes_mean_by_place': 3.0}
{'target_bayes_mean_by_place': 9.5}
{'target_bayes_mean_by_place': 22.5}
{'target_bayes_mean_by_place': 14.333}
{'target_bayes_mean_by_place': 34.333}
{'target_bayes_mean_by_place': 15.75}
{'target_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 = agg.learn_one(x, y)
{'target_bayes_mean_by_place_and_country': 3.0}
{'target_bayes_mean_by_place_and_country': 3.0}
{'target_bayes_mean_by_place_and_country': 3.0}
{'target_bayes_mean_by_place_and_country': 3.0}
{'target_bayes_mean_by_place_and_country': 9.5}
{'target_bayes_mean_by_place_and_country': 22.5}
{'target_bayes_mean_by_place_and_country': 13.5}
{'target_bayes_mean_by_place_and_country': 30.5}

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.

learn_one

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

Parameters

• x (dict)
• y (Union[bool, str, int, numbers.Number])

Returns

SupervisedTransformer: self

transform_one

Transform a set of features x.

Parameters

• x (dict)

Returns

dict: The transformed values.

References

1. Streaming groupbys in pandas for big datasets