partial_least_squares – Partial least squares regression – supervised factor extraction.#

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

Function signature#

mf.functions.partial_least_squares_transform(
    panel: pd.DataFrame,
    target: pd.Series,
    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.

target

pd.Series

—

—

Supervisory signal aligned to the panel index. Must share at least one index value with panel; raises ValueError if the intersection is empty.

n_components

int

3

>= 1

Number of PLS latent components. Clamped internally to min(T_clean - 1, K_clean).

Returns#

pd.DataFrame — scalar result.

Behavior#

Computes orthogonal latent components that maximise the covariance with the target (not just predictor variance, as in PCA). sklearn’s PLSRegression; params.n_components.

When to use

When a target-supervised reduction is preferable to PCA’s unsupervised projection.

In recipe context#

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

# Layer L3 recipe fragment
params:
  op: partial_least_squares

References#

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

  • Wold, Sjöström & Eriksson (2001) ‘PLS-regression: a basic tool of chemometrics’, Chemometrics and Intelligent Laboratory Systems 58(2): 109-130.