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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,88 @@ | ||
| from pathlib import Path | ||
| from typing import List, Optional | ||
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||
| import pandas as pd | ||
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||
| from clinicadl.splitter.split_utils import print_description_log | ||
| from clinicadl.utils.exceptions import ClinicaDLArgumentError, MAPSError | ||
|
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| def find_selection_metrics(maps_path: Path, split_name: str, split): | ||
| """Find which selection metrics are available in MAPS for a given split.""" | ||
|
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| split_path = maps_path / f"{split_name}-{split}" | ||
| if not split_path.is_dir(): | ||
| raise KeyError( | ||
| f"Training of split {split} was not performed." | ||
| f"Please execute maps_manager.train(split_list=[{split}])" | ||
| ) | ||
|
|
||
| return [ | ||
| metric.name.split("-")[1] | ||
| for metric in list(split_path.iterdir()) | ||
| if metric.name.startswith("best-") | ||
| ] | ||
|
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| def check_selection_metric( | ||
| maps_path: Path, split_name: str, split, selection_metric=None | ||
| ): | ||
| """Check that a given selection metric is available for a given split.""" | ||
| available_metrics = find_selection_metrics(maps_path, split_name, split) | ||
|
|
||
| if not selection_metric: | ||
| if len(available_metrics) > 1: | ||
| raise ClinicaDLArgumentError( | ||
| f"Several metrics are available for split {split}. " | ||
| f"Please choose which one you want to read among {available_metrics}" | ||
| ) | ||
| else: | ||
| selection_metric = available_metrics[0] | ||
| else: | ||
| if selection_metric not in available_metrics: | ||
| raise ClinicaDLArgumentError( | ||
| f"The metric {selection_metric} is not available." | ||
| f"Please choose among is the available metrics {available_metrics}." | ||
| ) | ||
| return selection_metric | ||
|
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|
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| def get_metrics( | ||
| maps_path: Path, | ||
| split_name: str, | ||
| data_group: str, | ||
| split: int = 0, | ||
| selection_metric: Optional[str] = None, | ||
| mode: str = "image", | ||
| verbose: bool = True, | ||
| ): | ||
| """ | ||
| Get the metrics corresponding to a group of participants identified by its data_group. | ||
| Args: | ||
| data_group (str): name of the data group used for the prediction task. | ||
| split (int): Index of the split used for training. | ||
| selection_metric (str): Metric used for best weights selection. | ||
| mode (str): level of the prediction | ||
| verbose (bool): if True will print associated prediction.log | ||
| Returns: | ||
| (dict[str:float]): Values of the metrics | ||
| """ | ||
| selection_metric = check_selection_metric( | ||
| maps_path, split_name, split, selection_metric | ||
| ) | ||
| if verbose: | ||
| print_description_log( | ||
| maps_path, split_name, data_group, split, selection_metric | ||
| ) | ||
| prediction_dir = ( | ||
| maps_path / f"{split_name}-{split}" / f"best-{selection_metric}" / data_group | ||
| ) | ||
| if not prediction_dir.is_dir(): | ||
| raise MAPSError( | ||
| f"No prediction corresponding to data group {data_group} was found." | ||
| ) | ||
| df = pd.read_csv( | ||
| prediction_dir / f"{data_group}_{mode}_level_metrics.tsv", sep="\t" | ||
| ) | ||
| return df.to_dict("records")[0] |
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