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'
Returns
Transformer: self
transform_one
Transform a set of features x
.
Parameters
- x — 'dict'
Returns
dict: The transformed values.