`transformer` – Transformer encoder regressor (torch, optional).#

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Operational op under axis family, sub-layer L4_A_model_selection, layer l4. Standalone callable: mf.functions.transformer_fit.

Function signature#

mf.functions.transformer_fit(
    X: np.ndarray | pd.DataFrame,
    y: np.ndarray | pd.Series,
) -> TransformerFitResult

Parameters#

name	type	default	constraint	description
`X`	`np.ndarray	pd.DataFrame`	—	—
`y`	`np.ndarray	pd.Series`	—	—

Returns#

TransformerFitResult — frozen dataclass with fit results.

Attribute	Type	Description
`.n_params`	`int`	Total trainable parameters in Transformer encoder + head.
`.n_features_in_`	`int`	Number of input features seen during fit (= d_model).
`.hidden_size`	`int`	dim_feedforward of the single TransformerEncoderLayer.
`.epochs_used`	`int`	Number of training epochs completed.
`.final_loss`	`float`	Training MSE via no-grad forward pass after fitting.
`.predict(X)`	`np.ndarray`	Predictions for new data X, shape (n_samples,).
`.summary()`	`str`	Arch metadata table: model_type, hidden_size, n_features, n_params, epochs_used, final_loss.

Behavior#

Requires pip install macroforecast[deep]. Self-attention on the lagged feature panel. Single encoder layer; suitable as a non-linear sequence-attention baseline.

When to use

Attention-based macro forecasting research; sequence-NN benchmark.

When NOT to use

Without [deep] installed.

In recipe context#

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

# Layer L4 recipe fragment
params:
  family: transformer

References#

macroforecast design Part 2, L4: ‘forecasting model is the layer where every authoring iteration ends – pick family, tune, repeat.’
Vaswani et al. (2017) ‘Attention is all you need’, NeurIPS.

transformer – Transformer encoder regressor (torch, optional).#