CI Metrics

clinicadl/prepare_data/prepare_data.py

@@ -154,18 +154,22 @@ def prepare_roi(file):
                     parameters["masks_location"],
                     parameters["roi_list"],
                     parameters["roi_mask_pattern"],
-                    None
-                    if parameters["use_uncropped_image"] is None
-                    else not parameters["use_uncropped_image"],
+                    (
+                        None
+                        if parameters["use_uncropped_image"] is None
+                        else not parameters["use_uncropped_image"]
+                    ),
                 )
 
             output_mode = extract_roi(
                 Path(file),
                 masks_location=parameters["masks_location"],
                 mask_pattern=parameters["roi_mask_pattern"],
-                cropped_input=None
-                if parameters["use_uncropped_image"] is None
-                else not parameters["use_uncropped_image"],
+                cropped_input=(
+                    None
+                    if parameters["use_uncropped_image"] is None
+                    else not parameters["use_uncropped_image"]
+                ),
                 roi_names=parameters["roi_list"],
                 uncrop_output=parameters["uncropped_roi"],
             )

clinicadl/quality_check/t1_linear/quality_check.py

@@ -1,6 +1,7 @@
 """
 This file contains all methods needed to perform the quality check procedure after t1-linear preprocessing.
 """
+
 from logging import getLogger
 from pathlib import Path
 

clinicadl/quality_check/t1_volume/quality_check.py

@@ -4,6 +4,7 @@
 2) percentage of non zero values < 15 % or > 50 %
 3) frontal similarity of T1 volume with the template < 0.40
 """
+
 from logging import getLogger
 from pathlib import Path
 

clinicadl/random_search/random_search.py

@@ -1,6 +1,7 @@
 """
 Launch a random network training.
 """
+
 from pathlib import Path
 
 from clinicadl.random_search.random_search_utils import get_space_dict, random_sampling

clinicadl/utils/cli_param/argument.py

@@ -1,4 +1,5 @@
 """Common CLI arguments used by ClinicaDL pipelines."""
+
 from pathlib import Path
 
 import click

clinicadl/utils/maps_manager/maps_manager.py

@@ -304,16 +304,16 @@ def predict(
                         multi_cohort=group_parameters["multi_cohort"],
                         label_presence=use_labels,
                         label=self.label if label is None else label,
-                        label_code=self.label_code
-                        if label_code == "default"
-                        else label_code,
+                        label_code=(
+                            self.label_code if label_code == "default" else label_code
+                        ),
                         cnn_index=network,
                     )
                     test_loader = DataLoader(
                         data_test,
-                        batch_size=batch_size
-                        if batch_size is not None
-                        else self.batch_size,
+                        batch_size=(
+                            batch_size if batch_size is not None else self.batch_size
+                        ),
                         shuffle=False,
                         sampler=DistributedSampler(
                             data_test,
@@ -371,16 +371,16 @@ def predict(
                     multi_cohort=group_parameters["multi_cohort"],
                     label_presence=use_labels,
                     label=self.label if label is None else label,
-                    label_code=self.label_code
-                    if label_code == "default"
-                    else label_code,
+                    label_code=(
+                        self.label_code if label_code == "default" else label_code
+                    ),
                 )
 
                 test_loader = DataLoader(
                     data_test,
-                    batch_size=batch_size
-                    if batch_size is not None
-                    else self.batch_size,
+                    batch_size=(
+                        batch_size if batch_size is not None else self.batch_size
+                    ),
                     shuffle=False,
                     sampler=DistributedSampler(
                         data_test,
@@ -1718,6 +1718,7 @@ def _test_loader(
         gpu=None,
         amp=False,
         network=None,
+        report_ci=True,
     ):
         """
         Launches the testing task on a dataset wrapped by a DataLoader and writes prediction TSV files.
@@ -1759,13 +1760,23 @@ def _test_loader(
             model = DDP(model)
 
             prediction_df, metrics = self.task_manager.test(
-                model, dataloader, criterion, use_labels=use_labels, amp=amp
+                model,
+                dataloader,
+                criterion,
+                use_labels=use_labels,
+                amp=amp,
+                report_ci=report_ci,
             )
             if use_labels:
                 if network is not None:
                     metrics[f"{self.mode}_id"] = network
+
+                loss_to_log = (
+                    metrics["Metric_values"][-1] if report_ci else metrics["loss"]
+                )
+
                 logger.info(
-                    f"{self.mode} level {data_group} loss is {metrics['loss']} for model selected on {selection_metric}"
+                    f"{self.mode} level {data_group} loss is {loss_to_log} for model selected on {selection_metric}"
                 )
 
             if cluster.master:
@@ -1790,6 +1801,7 @@ def _test_loader_ssda(
         gpu=None,
         network=None,
         target=False,
+        report_ci=True,
     ):
         """
         Launches the testing task on a dataset wrapped by a DataLoader and writes prediction TSV files.
@@ -1827,16 +1839,19 @@ def _test_loader_ssda(
                 network=network,
             )
             prediction_df, metrics = self.task_manager.test_da(
-                model,
-                dataloader,
-                criterion,
-                target=target,
+                model, dataloader, criterion, target=target, report_ci=report_ci
             )
             if use_labels:
                 if network is not None:
                     metrics[f"{self.mode}_id"] = network
+
+                if report_ci:
+                    loss_to_log = metrics["Metric_values"][-1]
+                else:
+                    loss_to_log = metrics["loss"]
+
                 logger.info(
-                    f"{self.mode} level {data_group} loss is {metrics['loss']} for model selected on {selection_metric}"
+                    f"{self.mode} level {data_group} loss is {loss_to_log} for model selected on {selection_metric}"
                 )
 
             # Replace here
@@ -2408,12 +2423,14 @@ def _write_data_group(
         self.write_parameters(
             group_path,
             {
-                "caps_directory": caps_directory
-                if caps_directory is not None
-                else self.caps_directory,
-                "multi_cohort": multi_cohort
-                if multi_cohort is not None
-                else self.multi_cohort,
+                "caps_directory": (
+                    caps_directory
+                    if caps_directory is not None
+                    else self.caps_directory
+                ),
+                "multi_cohort": (
+                    multi_cohort if multi_cohort is not None else self.multi_cohort
+                ),
             },
         )
 
@@ -2582,7 +2599,7 @@ def _mode_level_to_tsv(
         performance_path = (
             performance_dir / f"{data_group}_{self.mode}_level_prediction.tsv"
         )
-        if not performance_path.is_file():
+        if not performance_path.is_dir():
             results_df.to_csv(performance_path, index=False, sep="\t")
         else:
             results_df.to_csv(
@@ -2591,12 +2608,12 @@ def _mode_level_to_tsv(
 
         metrics_path = performance_dir / f"{data_group}_{self.mode}_level_metrics.tsv"
         if metrics is not None:
+            pd_metrics = pd.DataFrame(metrics).T
+
             if not metrics_path.is_file():
-                pd.DataFrame(metrics, index=[0]).to_csv(
-                    metrics_path, index=False, sep="\t"
-                )
+                pd_metrics.to_csv(metrics_path, index=False, sep="\t", header=False)
             else:
-                pd.DataFrame(metrics, index=[0]).to_csv(
+                pd_metrics.to_csv(
                     metrics_path, index=False, sep="\t", mode="a", header=False
                 )
 

clinicadl/utils/meta_maps/getter.py

@@ -1,6 +1,7 @@
 """
 Produces a tsv file to analyze the performance of one launch of the random search.
 """
+
 from pathlib import Path
 
 import pandas as pd