RandomTreesEmbedding¶

Embed samples according to the leaves of random trees.

This transformer builds an ensemble of totally random trees and encodes each sample with the leaf reached in every tree. The output is sparse: exactly one binary feature is active per tree.

This is the online counterpart of feeding a linear model with random-tree leaf indicators. By default, this implementation assumes that each feature has values comprised between 0 and 1. If this is not the case, then you can manually specify the limits via the limits argument. If you do not know the limits in advance, then you can use a preprocessing.MinMaxScaler as an initial preprocessing step.

The trees are built lazily from the first sample that is observed, either via transform_one or learn_one. If new features appear later in the stream, then the forest is rebuilt so that future splits may use them. Unless specified in limits, newly observed features are also assumed to lie in the range [0, 1].

Parameters¶

• n_trees

  Default → 10

  Number of trees in the ensemble.

• height

  Default → 8

  Height of each tree. A tree of height h contains 2 ** h leaves.

• limits

  Type → dict[base.typing.FeatureName, tuple[float, float]] | None

  Default → None

  Specifies the range of each feature. By default each feature is assumed to be in range [0, 1]. If the feature ranges are unknown beforehand, then use a preprocessing.MinMaxScaler upstream.

• seed

  Type → int | None

  Default → None

  Random seed for reproducibility.

Examples¶

from river import feature_extraction as fx
from river import linear_model as lm
from river import optim
from river import preprocessing

embedding = fx.RandomTreesEmbedding(n_trees=3, height=2, seed=42)
len(embedding.transform_one({'x': 0.3, 'y': 0.7}))

3

model = (
    preprocessing.MinMaxScaler() |
    fx.RandomTreesEmbedding(n_trees=5, height=3, seed=42) |
    lm.LogisticRegression(optimizer=optim.SGD(0.1))
)

Methods¶