def interpret(
maps_dir,
data_group,
name,
caps_directory,
tsv_path,
selection_metrics,
multi_cohort,
target_node,
save_individual,
batch_size,
nproc,
use_cpu,
verbose=0,
):
verbose_list = ["warning", "info", "debug"]
maps_manager = MapsManager(maps_dir, verbose=verbose_list[verbose])
maps_manager.interpret(
data_group=data_group,
name=name,
caps_directory=caps_directory,
tsv_path=tsv_path,
selection_metrics=selection_metrics,
multi_cohort=multi_cohort,
target_node=target_node,
save_individual=save_individual,
batch_size=batch_size,
num_workers=nproc,
use_cpu=use_cpu,
)
def test_interpret(cli_commands):
cnn_input = cli_commands
if os.path.exists("results"):
shutil.rmtree("results")
train_error = not os.system("clinicadl " + " ".join(cnn_input))
maps_manager = MapsManager("results", verbose="debug")
maps_manager.interpret("train", "test")
interpret_map = maps_manager.get_interpretation("train", "test")
assert train_error
shutil.rmtree("results")
def interpret(
maps_dir: str,
data_group: str,
name: str,
caps_directory: str,
tsv_path: str,
selection_metrics: List[str],
diagnoses: List[str],
multi_cohort: bool,
target_node: int,
save_individual: bool,
batch_size: int,
n_proc: int,
gpu: bool,
verbose=0,
overwrite: bool = False,
overwrite_name: bool = False,
):
"""
This function loads a MAPS and interprets all the models selected using a metric in selection_metrics.
Args:
maps_dir: path to the MAPS.
data_group: name of the data group interpreted.
caps_directory: path to the CAPS folder. For more information please refer to
[clinica documentation](https://aramislab.paris.inria.fr/clinica/docs/public/latest/CAPS/Introduction/).
tsv_path: path to a TSV file containing the list of participants and sessions to interpret.
target_node: Node from which the interpretation is computed.
save_individual: If True saves the individual map of each participant / session couple.
gpu: if true, it uses gpu.
n_proc: num_workers used in DataLoader
batch_size: batch size of the DataLoader
selection_metrics: list of metrics to find best models to be evaluated.
diagnoses: list of diagnoses to be tested if tsv_path is a folder.
multi_cohort: If True caps_directory is the path to a TSV file linking cohort names and paths.
verbose: level of verbosity (0: warning, 1: info, 2: debug).
overwrite: If True former definition of data group is erased.
overwrite_name: If True former interpretability map with the same name is erased.
"""
verbose_list = ["warning", "info", "debug"]
if verbose > 2:
verbose_str = "debug"
else:
verbose_str = verbose_list[verbose]
maps_manager = MapsManager(maps_dir, verbose=verbose_str)
maps_manager.interpret(
data_group=data_group,
name=name,
caps_directory=caps_directory,
tsv_path=tsv_path,
selection_metrics=selection_metrics,
diagnoses=diagnoses,
multi_cohort=multi_cohort,
target_node=target_node,
save_individual=save_individual,
batch_size=batch_size,
n_proc=n_proc,
gpu=gpu,
overwrite=overwrite,
overwrite_name=overwrite_name,
)