GaussianRandomProjector¶

Gaussian random projector.

This transformer reduces the dimensionality of inputs through Gaussian random projection.

The components of the random projections matrix are drawn from N(0, 1 / n_components).

Parameters¶

n_components

Default → 10

Number of components to project the data onto.
seed

Type → int | None

Default → None

Random seed for reproducibility.

Examples¶

from river import datasets
from river import evaluate
from river import linear_model
from river import metrics
from river import preprocessing

dataset = datasets.TrumpApproval()
model = preprocessing.GaussianRandomProjector(
    n_components=3,
    seed=42
)

for x, y in dataset:
    x = model.transform_one(x)
    print(x)
    break

{0: -61289.371..., 1: 141312.510..., 2: 279165.993...}

model = (
    preprocessing.GaussianRandomProjector(
        n_components=5,
        seed=42
    ) |
    preprocessing.StandardScaler() |
    linear_model.LinearRegression()
)
evaluate.progressive_val_score(dataset, model, metrics.MAE())

MAE: 0.933...

Methods¶

learn_one

Update with a set of features x.

A lot of transformers don't actually have to do anything during the learn_one step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the learn_one can override this method.

Parameters

x — 'dict'

transform_one

Transform a set of features x.

Parameters

x — 'dict'

Returns

dict: The transformed values.