TransformerUnion

Packs multiple transformers into a single one.

Pipelines allow you to apply steps sequentially. Therefore, the output of a step becomes the input of the next one. In many cases, you may want to pass the output of a step to multiple steps. This simple transformer allows you to do so. In other words, it enables you to apply particular steps to different parts of an input. A typical example is when you want to scale numeric features and one-hot encode categorical features.

This transformer is essentially a list of transformers. Whenever it is updated, it loops through each transformer and updates them. Meanwhile, calling transform_one collects the output of each transformer and merges them into a single dictionary.

Parameters

• transformers

  Ideally, a list of (name, estimator) tuples. A name is automatically inferred if none is provided.

Examples

Take the following dataset:

X = [
    {'place': 'Taco Bell', 'revenue': 42},
    {'place': 'Burger King', 'revenue': 16},
    {'place': 'Burger King', 'revenue': 24},
    {'place': 'Taco Bell', 'revenue': 58},
    {'place': 'Burger King', 'revenue': 20},
    {'place': 'Taco Bell', 'revenue': 50}
]

As an example, let's assume we want to compute two aggregates of a dataset. We therefore define two feature_extraction.Aggs and initialize a TransformerUnion with them:

from river import compose
from river import feature_extraction
from river import stats

mean = feature_extraction.Agg(
    on='revenue', by='place',
    how=stats.Mean()
)
count = feature_extraction.Agg(
    on='revenue', by='place',
    how=stats.Count()
)
agg = compose.TransformerUnion(mean, count)

We can now update each transformer and obtain their output with a single function call:

from pprint import pprint
for x in X:
    agg.learn_one(x)
    pprint(agg.transform_one(x))

{'revenue_count_by_place': 1, 'revenue_mean_by_place': 42.0}
{'revenue_count_by_place': 1, 'revenue_mean_by_place': 16.0}
{'revenue_count_by_place': 2, 'revenue_mean_by_place': 20.0}
{'revenue_count_by_place': 2, 'revenue_mean_by_place': 50.0}
{'revenue_count_by_place': 3, 'revenue_mean_by_place': 20.0}
{'revenue_count_by_place': 3, 'revenue_mean_by_place': 50.0}

Note that you can use the + operator as a shorthand notation:

agg = mean + count

This allows you to build complex pipelines in a very terse manner. For instance, we can create a pipeline that scales each feature and fits a logistic regression as so:

from river import linear_model as lm
from river import preprocessing as pp

model = (
    (mean + count) |
    pp.StandardScaler() |
    lm.LogisticRegression()
)

Whice is equivalent to the following code:

model = compose.Pipeline(
    compose.TransformerUnion(mean, count),
    pp.StandardScaler(),
    lm.LogisticRegression()
)

Note that you access any part of a TransformerUnion by name:

model['TransformerUnion']['Agg']

Agg (
    on="revenue"
    by=['place']
    how=Mean ()
)

model['TransformerUnion']['Agg1']

Agg (
    on="revenue"
    by=['place']
    how=Count ()
)

You can also manually provide a name for each step:

agg = compose.TransformerUnion(
    ('Mean revenue by place', mean),
    ('# by place', count)
)

Mini-batch example:

X = pd.DataFrame([
    {"place": 2, "revenue": 42},
    {"place": 3, "revenue": 16},
    {"place": 3, "revenue": 24},
    {"place": 2, "revenue": 58},
    {"place": 3, "revenue": 20},
    {"place": 2, "revenue": 50},
])

Since we need a transformer with mini-batch support to demonstrate, we shall use a StandardScaler.

from river import compose
from river import preprocessing

agg = (
    compose.Select("place") +
    (compose.Select("revenue") | preprocessing.StandardScaler())
)

agg.learn_many(X)
agg.transform_many(X)

   place   revenue
0      2  0.441250
1      3 -1.197680
2      3 -0.693394
3      2  1.449823
4      3 -0.945537
5      2  0.945537

Methods

learn_many

Update each transformer.

Parameters

• X — 'pd.DataFrame'
• y — 'pd.Series | None' — defaults to None

learn_one

Update each transformer.

Parameters

• x — 'dict'
• y — defaults to None

transform_many

Passes the data through each transformer and packs the results together.

Parameters

• X — 'pd.DataFrame'

transform_one

Passes the data through each transformer and packs the results together.

Parameters

• x — 'dict'