Fix a bug in the making of the individual model (when there is no target in the participants columns).

config.yaml

@@ -732,7 +732,7 @@ PARAMS_FOR_ANALYSIS:
 
   TARGET:
     COMPUTE: True
-    LABEL: PANAS_negative_affect_mean
+    LABEL: appraisal_stressfulness_event_mean
     ALL_LABELS: [appraisal_stressfulness_event_mean, appraisal_threat_mean, appraisal_challenge_mean]
                 # PANAS_positive_affect_mean, PANAS_negative_affect_mean, JCQ_job_demand_mean, JCQ_job_control_mean, JCQ_supervisor_support_mean, 
                 # JCQ_coworker_support_mean, appraisal_stressfulness_period_mean, appraisal_stressfulness_event_mean

src/features/all_cleaning_individual/straw/main.py

@@ -27,7 +27,10 @@ def straw_cleaning(sensor_data_files, provider):
     # (1) FILTER_OUT THE ROWS THAT DO NOT HAVE THE TARGET COLUMN AVAILABLE
     if config['PARAMS_FOR_ANALYSIS']['TARGET']['COMPUTE']:
         target = config['PARAMS_FOR_ANALYSIS']['TARGET']['LABEL'] # get target label from config
+        if 'phone_esm_straw_' + target in features:
             features = features[features['phone_esm_straw_' + target].notna()].reset_index(drop=True)
+        else:
+            return features
 
     # (2.1) QUALITY CHECK (DATA YIELD COLUMN) deletes the rows where E4 or phone data is low quality
     phone_data_yield_unit = provider["PHONE_DATA_YIELD_FEATURE"].split("_")[3].lower()

src/models/helper.py

@@ -1,5 +1,6 @@
 import pandas as pd
-
+import sys
+import warnings
 
 def retain_target_column(df_input: pd.DataFrame, target_variable_name: str):
     column_names = df_input.columns
@@ -8,9 +9,9 @@ def retain_target_column(df_input: pd.DataFrame, target_variable_name: str):
     esm_names = column_names[esm_names_index]
     target_variable_index = esm_names.str.contains(target_variable_name)
     if all(~target_variable_index):
-        raise ValueError("The requested target (", target_variable_name,
+        warnings.warn(f"The requested target (, {target_variable_name} ,)cannot be found in the dataset. Please check the names of phone_esm_ columns in z_all_sensor_features_cleaned_straw_py.csv")
-                         ")cannot be found in the dataset.",
+        return False
-                         "Please check the names of phone_esm_ columns in z_all_sensor_features_cleaned_straw_py.csv")
+
     sensor_features_plus_target = df_input.drop(esm_names, axis=1)
     sensor_features_plus_target["target"] = df_input[esm_names[target_variable_index]]
     # We will only keep one column related to phone_esm and that will be our target variable.

src/models/select_targets.py

@@ -7,4 +7,7 @@ target_variable_name = snakemake.params["target_variable"]
 
 model_input = retain_target_column(cleaned_sensor_features, target_variable_name)
 
+if not model_input:
+    pd.DataFrame().to_csv(snakemake.output[0])
+else:
     model_input.to_csv(snakemake.output[0], index=False)