sparse_pca – Sparse PCA – L1-penalised factor loadings (sklearn / Zou-Hastie-Tibshirani 2006).#

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

Function signature#

mf.functions.sparse_pca_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 sparse principal components to extract. Clamped internally to min(T_clean, K) - 1.

Returns#

pd.DataFrame — scalar result.

Behavior#

Variant of PCA where loadings are pushed toward zero by an L1 penalty (params.alpha). Yields more interpretable factors at the cost of a small reconstruction loss; uses sklearn’s SparsePCA.

When to use

When you want factor loadings to map cleanly onto a small subset of original predictors (interpretability).

When NOT to use

When pure variance maximisation is more important than interpretability – use plain pca. For the Chen-Rohe (2023) SCA variant used in Zhou-Rapach (2025) Sparse Macro-Finance Factors, use sparse_pca_chen_rohe instead.

In recipe context#

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

# Layer L3 recipe fragment
params:
  op: sparse_pca

References#

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