SparseRandomProjector

Sparse random projector.

This transformer reduces the dimensionality of inputs by projecting them onto a sparse random projection matrix.

Ping Li et al. recommend using a minimum density of 1 / sqrt(n_features). The transformer is not aware of how many features will be seen, so the user must specify the density manually.

Parameters

• n_components

  Default → 10

  Number of components to project the data onto.

• density

  Default → 0.1

  Density of the random projection matrix. The density is defined as the ratio of non-zero components in the matrix. It is equal to 1 - sparsity.

• 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.SparseRandomProjector(
    n_components=3,
    seed=42
)

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

{0: 92.89572746525327, 1: 1344540.5692342375, 2: 0}

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

MAE: 1.292572

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.

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1. D. Achlioptas. 2003. Database-friendly random projections: Johnson-Lindenstrauss with binary coins. Journal of Computer and System Sciences 66 (2003) 671-687 ↩

2. Ping Li, Trevor J. Hastie, and Kenneth W. Church. 2006. Very sparse random projections. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD'06). ACM, New York, NY, USA, 287-296. ↩