Update docstrings

Author: ncaptier

scripts/_init_scripts.py

@@ -28,6 +28,7 @@
 
 class PredictionTask:
     """
+    Load data and define associated predictive pipelines.
 
     Parameters
     ----------

scripts/collect_shap.py

@@ -105,9 +105,15 @@ def _fun_parallel(prediction_task, X, y, r, disable_infos):
 
     Returns
     -------
-    shap_dict:
-
-    coefs_dict:
+    shap_dict: dictionary
+        Dictionary whose keys correspond to the different modalities (e.g., "RNA", "clinical") and the items correspond
+        to pandas dataframe of size (n_samples, n_features) that contain the SHAP values collected across the test sets
+        of the cross-validation scheme.
+
+    coefs_dict: dictionary or None
+        Dictionary whose keys correspond to the different modalities (e.g., "RNA", "clinical") and the items correspond
+        to arrays of size (n_folds, n_features) that contain the linear coefficients collected across the different
+        folds of the cross-validation scheme. None if the classifier type is not linear.
     """
 
     cv = StratifiedKFold(n_splits=10, shuffle=True)

scripts/collect_shap_survival.py

@@ -113,9 +113,15 @@ def _fun_parallel(prediction_task, X, y, r, disable_infos):
 
     Returns
     -------
-    shap_dict:
-
-    coefs_dict:
+    shap_dict: dictionary
+        Dictionary whose keys correspond to the different modalities (e.g., "RNA", "clinical") and the items correspond
+        to pandas dataframe of size (n_samples, n_features) that contain the SHAP values collected across the test sets
+        of the cross-validation scheme.
+
+    coefs_dict: dictionary or None
+        Dictionary whose keys correspond to the different modalities (e.g., "RNA", "clinical") and the items correspond
+        to arrays of size (n_folds, n_features) that contain the linear coefficients collected across the different
+        folds of the cross-validation scheme. None if the survival model type is not linear.
     """
 
     cv = CensoredKFold(n_splits=10, shuffle=True)