Trainer

mllabs._trainer.Trainer

Runs cross-validation training on a subset of Pipeline nodes.

Created via :meth:~mllabs.Experimenter.add_trainer. Shares the Experimenter's Pipeline and DataCache.

Attributes:

Name	Type	Description
name	str	Trainer name.
selected_stages	list[str]	Stage nodes included in training.
selected_heads	list[str]	Head nodes to train.
split_indices	list[tuple] | None	[(train_idx, valid_idx), ...] or None if no splitting.
node_objs	dict	{node_name: TrainStageObj | TrainHeadObj}.

select_head(nodes)

Specify Head nodes to train and auto-collect their upstream Stages.

Parameters:

Name	Type	Description	Default
nodes		Node query — list, regex str, or None (all heads).	required

train()

Train all unbuilt selected nodes across all splits.

Nodes are trained in topological order. Each node completes all splits before the next node begins.

process(data, v=None)

Apply trained processors to new data, yielding one result per split.

Parameters:

Name	Type	Description	Default
data		Input dataset.	required
v		Output column filter applied to Head outputs.	None

Yields:

Name	Type	Description
DataFrame		Concatenated Head outputs for each split.

to_inferencer(v=None)

Export trained processors to a standalone :class:~mllabs.Inferencer.

All selected nodes must be in built state.

Parameters:

Name	Type	Description	Default
v		Output column filter passed to the Inferencer.	None

Returns:

Name	Type	Description
Inferencer		Independent inferencer ready for deployment.

Raises:

Type	Description
RuntimeError	If any selected node is not built.

reset_nodes(nodes)

get_n_splits()