autointent.nodes.NodeOptimizer#

class autointent.nodes.NodeOptimizer(node_type, search_space, target_metric, metrics=None)#

Class for optimizing nodes in a computational pipeline.

This class is responsible for optimizing different modules within a node using various search strategies and logging the results.

Parameters:
node_type#
node_info#
target_metric#
metrics = None#
modules_search_spaces#
fit(context)#

Performs the optimization process for the node.

Parameters:

context (autointent.context.Context) – The optimization context containing relevant data.

Raises:

AssertionError – If an invalid sampler type is provided.

Return type:

None

objective(trial, search_space, context)#

Defines the objective function for optimization.

Parameters:
Returns:

The value of the target metric for the given trial.

Return type:

float

get_module_dump_dir(context, module_name, j_combination)#

Creates and returns the path to the module dump directory.

Parameters:

  • context (autointent.context.Context) – The context object.

  • module_name (str) – The name of the module being optimized.

  • j_combination (int) – The combination index for the parameters.

Returns:

The path to the module dump directory.

Return type:

str | None

validate_nodes_with_dataset(dataset, mode)#

Validates nodes against the dataset.

Parameters:

  • dataset (autointent.Dataset) – The dataset used for validation.

  • mode (autointent.custom_types.SearchSpaceValidationMode) – The validation mode (“raise” or “warning”).

Raises:

ValueError – If validation fails and mode is set to “raise”.

Return type:

None

validate_search_space(search_space)#

Check if search space is configured correctly.

Parameters:

search_space (list[dict[str, Any]])

Return type:

None