topobench.utils.config_resolvers module#
Configuration resolvers for the topobench package.
- check_pses_in_transforms(transforms)#
Check if there are positional or structural encodings in the transforms.
- Parameters:
- transformsDictConfig
Configuration parameters for the transforms.
- Returns:
- bool
True if there are positional or structural encodings, False otherwise.
- get_default_metrics(task, metrics=None)#
Get default metrics for a given task.
- Parameters:
- taskstr
Task, either “classification” or “regression”.
- metricslist, optional
List of metrics to be used. If None, the default metrics will be used.
- Returns:
- list
List of default metrics.
- Raises:
- ValueError
If the task is invalid.
- get_default_trainer()#
Get default trainer configuration.
- Returns:
- str
Default trainer configuration file name.
- get_default_transform(dataset, model)#
Get default transform for a given data domain and model.
- Parameters:
- datasetstr
Dataset name. Should be in the format “data_domain/name”.
- modelstr
Model name. Should be in the format “model_domain/name”.
- Returns:
- str
Default transform.
- get_flattened_channels(num_nodes, channels)#
Get the output dimension of flattening a feature matrix.
- Parameters:
- num_nodesint
Hidden dimension for the first layer.
- channelsint
Channel dimension.
- Returns:
- int
Flatenned cchannels dimension.
- get_monitor_metric(task, metric)#
Get monitor metric for a given task.
- Parameters:
- taskstr
Task, either “classification” or “regression”.
- metricstr
Name of the metric function.
- Returns:
- str
Monitor metric.
- Raises:
- ValueError
If the task is invalid.
- get_monitor_mode(task)#
Get monitor mode for a given task.
- Parameters:
- taskstr
Task, either “classification” or “regression”.
- Returns:
- str
Monitor mode, either “max” or “min”.
- Raises:
- ValueError
If the task is invalid.
- get_non_relational_out_channels(num_nodes, channels, task_level)#
Get the output dimension for a non-relational model.
- Parameters:
- num_nodesint
Number of nodes in the input graph.
- channelsint
Channel dimension.
- task_levelint
Task level for the model.
- Returns:
- int
Output dimension.
- get_required_lifting(data_domain, model)#
Get required transform for a given data domain and model.
- Parameters:
- data_domainstr
Dataset domain.
- modelstr
Model name. Should be in the format “model_domain/name”.
- Returns:
- str
Required transform.
- infer_in_channels(dataset, transforms)#
Infer the number of input channels for a given dataset.
- Parameters:
- datasetDictConfig
Configuration parameters for the dataset.
- transformsDictConfig
Configuration parameters for the transforms.
- Returns:
- list
List with dimensions of the input channels.
- infer_num_cell_dimensions(selected_dimensions, in_channels)#
Infer the length of a list.
- Parameters:
- selected_dimensionslist
List of selected dimensions. If not None it will be used to infer the length.
- in_channelslist
List of input channels. If selected_dimensions is None, this list will be used to infer the length.
- Returns:
- int
Length of the input list.
- infer_topotune_num_cell_dimensions(neighborhoods)#
Infer the length of a list.
- Parameters:
- neighborhoodslist
List of neighborhoods.
- Returns:
- int
Length of the input list.