random_projection – Johnson-Lindenstrauss random Gaussian projection.#

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Operational op under axis op, sub-layer L3_A_step_op, layer l3. Standalone callable: mf.functions.random_projection_transform.

Function signature#

mf.functions.random_projection_transform(
    panel: pd.DataFrame,
    n_components: int,
) -> pd.DataFrame

Parameters#

name

type

default

constraint

description

panel

pd.DataFrame

—

—

Input panel. Each column is a variable; rows are time periods. Series is promoted to a single-column DataFrame internally.

n_components

int

8

>= 1

Number of random projection output dimensions. Clamped internally to min(n_components, K).

Returns#

pd.DataFrame — scalar result.

Behavior#

Reduces dimensionality by multiplying with a random Gaussian matrix scaled to (approximately) preserve pairwise distances. Cheap baseline for dimensionality reduction; sklearn’s GaussianRandomProjection.

When to use

Sweep baselines / sanity checks against PCA’s structured reduction.

In recipe context#

Set params.op = "random_projection" in the relevant layer to activate this op within a recipe:

# Layer L3 recipe fragment
params:
  op: random_projection

References#

  • macroforecast design Part 2, L3: ‘feature engineering is a DAG of typed transforms; cascade-depth bounds the longest chain at cascade_max_depth.’