Switch to 30_before ERS with corresponding targets.

config.yaml

@@ -26,7 +26,7 @@ TIME_SEGMENTS: &time_segments
   INCLUDE_PAST_PERIODIC_SEGMENTS: TRUE # Only relevant if TYPE=PERIODIC, see docs
   TAILORED_EVENTS: # Only relevant if TYPE=EVENT
     COMPUTE: True
-    TARGETS_METHOD: "stress_event" # 30_before, 90_before, stress_event
+    SEGMENTING_METHOD: "30_before" # 30_before, 90_before, stress_event
 
 # See https://www.rapids.science/latest/setup/configuration/#timezone-of-your-study
 TIMEZONE: 
@@ -242,7 +242,7 @@ PHONE_ESM:
     STRAW:
       COMPUTE: True
       SCALES: ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support", 
-              "appraisal_stressfulness_period", "appraisal_stressfulness_event"]
+              "appraisal_stressfulness_period", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge"]
       FEATURES: [mean]
       SRC_SCRIPT: src/features/phone_esm/straw/main.py
 
@@ -710,7 +710,7 @@ ALL_CLEANING_OVERALL:
         COMPUTE: True
         MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
         CORR_THRESHOLD: 0.95
-      STANDARDIZATION: True
+      STANDARDIZATION: False
       SRC_SCRIPT: src/features/all_cleaning_overall/straw/main.py
 
 
@@ -732,7 +732,8 @@ PARAMS_FOR_ANALYSIS:
 
   TARGET:
     COMPUTE: True
-    LABEL: PANAS_negative_affect_mean
-    ALL_LABELS: [appraisal_stressfulness_event_mean]
+    LABEL: appraisal_stressfulness_event_mean
+    ALL_LABELS: [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, 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
+                # JCQ_coworker_support_mean, appraisal_stressfulness_period_mean, appraisal_stressfulness_event_mean, appraisal_threat_mean, appraisal_challenge_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()
@@ -138,8 +141,6 @@ def straw_cleaning(sensor_data_files, provider):
         if esm not in features:
             features[esm] = esm_cols[esm]
 
-    fe6 = features.copy()
-
     # (9) VERIFY IF THERE ARE ANY NANS LEFT IN THE DATAFRAME
     if features.isna().any().any():
         raise ValueError("There are still some NaNs present in the dataframe. Please check for implementation errors.")

src/features/all_cleaning_overall/straw/main.py

@@ -22,14 +22,29 @@ def straw_cleaning(sensor_data_files, provider, target):
     excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime']
 
     graph_bf_af(features, "1target_rows_before")
-    # (1.0) OVERRIDE STRESSFULNESS EVENT TARGETS IF ERS TARGETS_METHOD IS "STRESS_EVENT"
-    if config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["TARGETS_METHOD"] == "stress_event" and \
-        "appraisal_stressfulness_event_mean" in config['PARAMS_FOR_ANALYSIS']['TARGET']['ALL_LABELS']:
+
+    # (1.0) OVERRIDE STRESSFULNESS EVENT TARGETS IF ERS SEGMENTING_METHOD IS "STRESS_EVENT"
+    if config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["SEGMENTING_METHOD"] == "stress_event": 
     
         stress_events_targets = pd.read_csv("data/external/stress_event_targets.csv")   
+
+        if "appraisal_stressfulness_event_mean" in config['PARAMS_FOR_ANALYSIS']['TARGET']['ALL_LABELS']:
             features.drop(columns=['phone_esm_straw_appraisal_stressfulness_event_mean'], inplace=True)
-        features = features.merge(stress_events_targets.rename(columns={'label': 'local_segment_label'}), on=['local_segment_label'], how='inner') \
-                           .rename(columns={'intensity': 'phone_esm_straw_appraisal_stressfulness_event_mean'})
+            features = features.merge(stress_events_targets[["label", "appraisal_stressfulness_event"]] \
+                        .rename(columns={'label': 'local_segment_label'}), on=['local_segment_label'], how='inner') \
+                        .rename(columns={'appraisal_stressfulness_event': 'phone_esm_straw_appraisal_stressfulness_event_mean'})
+
+        if "appraisal_threat_mean" in config['PARAMS_FOR_ANALYSIS']['TARGET']['ALL_LABELS']:
+            features.drop(columns=['phone_esm_straw_appraisal_threat_mean'], inplace=True)
+            features = features.merge(stress_events_targets[["label", "appraisal_threat"]] \
+                        .rename(columns={'label': 'local_segment_label'}), on=['local_segment_label'], how='inner') \
+                        .rename(columns={'appraisal_threat': 'phone_esm_straw_appraisal_threat_mean'})
+
+        if "appraisal_challenge_mean" in config['PARAMS_FOR_ANALYSIS']['TARGET']['ALL_LABELS']:
+            features.drop(columns=['phone_esm_straw_appraisal_challenge_mean'], inplace=True)
+            features = features.merge(stress_events_targets[["label", "appraisal_challenge"]] \
+                        .rename(columns={'label': 'local_segment_label'}), on=['local_segment_label'], how='inner') \
+                        .rename(columns={'appraisal_challenge': 'phone_esm_straw_appraisal_challenge_mean'})
 
     esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
 
@@ -93,7 +108,7 @@ def straw_cleaning(sensor_data_files, provider, target):
     features[impute_w_sn2] = features[impute_w_sn2].fillna(1) # Special case of imputation - nominal/ordinal value
 
     impute_w_sn3 = [col for col in features.columns if "loglocationvariance" in col]
-    features[impute_w_sn2] = features[impute_w_sn2].fillna(-1000000) # Special case of imputation - loglocation
+    features[impute_w_sn3] = features[impute_w_sn3].fillna(-1000000) # Special case of imputation - loglocation
 
     # Impute location features
     impute_locations = [col for col in features \
@@ -203,6 +218,16 @@ def straw_cleaning(sensor_data_files, provider, target):
 
     graph_bf_af(features, "10correlation_drop")
 
+    # Transform categorical columns to category dtype
+
+    cat1 = [col for col in features.columns if "mostcommonactivity" in col]
+    if cat1: # Transform columns to category dtype (mostcommonactivity)
+        features[cat1] = features[cat1].astype(int).astype('category')
+
+    cat2 = [col for col in features.columns if "homelabel" in col]
+    if cat2: # Transform columns to category dtype (homelabel)
+        features[cat2] = features[cat2].astype(int).astype('category')
+
     # (10) VERIFY IF THERE ARE ANY NANS LEFT IN THE DATAFRAME
     if features.isna().any().any():
         raise ValueError("There are still some NaNs present in the dataframe. Please check for implementation errors.")
@@ -223,7 +248,7 @@ def impute(df, method='zero'):
         'knn': k_nearest(df) 
     }[method]
 
-def graph_bf_af(features, phase_name, plt_flag=True):
+def graph_bf_af(features, phase_name, plt_flag=False):
     if plt_flag:
         sns.set(rc={"figure.figsize":(16, 8)})
         sns.heatmap(features.isna(), cbar=False) #features.select_dtypes(include=np.number)

src/features/phone_esm/straw/main.py

@@ -42,7 +42,8 @@ def straw_features(sensor_data_files, time_segment, provider, filter_data_by_seg
     requested_features = provider["FEATURES"]
     # name of the features this function can compute
     requested_scales = provider["SCALES"]
-    base_features_names = ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support", "appraisal_stressfulness_period", "appraisal_stressfulness_event"]
+    base_features_names = ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support", 
+                            "appraisal_stressfulness_period", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge"]
     #TODO Check valid questionnaire and feature names.
     # the subset of requested features this function can compute
     features_to_compute = list(set(requested_features) & set(base_features_names))

src/features/phone_esm/straw/process_user_event_related_segments.py

@@ -10,6 +10,15 @@ from esm import classify_sessions_by_completion_time, preprocess_esm
 input_data_files = dict(snakemake.input)
 
 def format_timestamp(x):
+    """This method formates inputed timestamp into format "HH MM SS". Including spaces. If there is no hours or minutes present
+    that part is ignored, e.g., "MM SS" or just "SS". 
+
+    Args:
+        x (int): unix timestamp in seconds
+
+    Returns:
+        str: formatted timestamp using "HH MM SS" sintax
+    """
     tstring=""
     space = False
     if x//3600 > 0:
@@ -23,8 +32,23 @@ def format_timestamp(x):
     return tstring
 
 
-def extract_ers(esm_df, device_id): 
+def extract_ers(esm_df):
+    """This method has two major functionalities: 
+        (1) It prepares STRAW event-related segments file with the use of esm file. The execution protocol is depended on 
+            the segmenting method specified in the config.yaml file.
+        (2) It prepares and writes csv with targets and corresponding time segments labels. This is later used 
+            in the overall cleaning script (straw).
     
+    Details about each segmenting method are listed below by each corresponding condition. Refer to the RAPIDS documentation for the 
+    ERS file format: https://www.rapids.science/1.9/setup/configuration/#time-segments -> event segments
+
+    Args:
+        esm_df (DataFrame): read esm file that is dependend on the current participant.
+
+    Returns:
+        extracted_ers (DataFrame): dataframe with all necessary information to write event-related segments file 
+        in the correct format.
+    """
     pd.set_option("display.max_rows", 20)
     pd.set_option("display.max_columns", None)
 
@@ -40,23 +64,34 @@ def extract_ers(esm_df, device_id):
     esm_filtered_sessions = classified[classified["session_response"] == 'ema_completed'].reset_index()[['device_id', 'esm_session']]
     esm_df = esm_preprocessed.loc[(esm_preprocessed['device_id'].isin(esm_filtered_sessions['device_id'])) & (esm_preprocessed['esm_session'].isin(esm_filtered_sessions['esm_session']))]
     
-    targets_method = config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["TARGETS_METHOD"]
-    if targets_method in ["30_before", "90_before"]: # takes 30-minute peroid before the questionnaire + the duration of the questionnaire
+    segmenting_method = config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["SEGMENTING_METHOD"]
+    
+    if segmenting_method in ["30_before", "90_before"]: # takes 30-minute peroid before the questionnaire + the duration of the questionnaire
+        """ '30-minutes and 90-minutes before' have the same fundamental logic with couple of deviations that will be explained below.
+        Both take x-minute period before the questionnaire that is summed with the questionnaire duration.
+        All questionnaire durations over 15 minutes are excluded from the querying.
+        """
         # Extract time-relevant information
         extracted_ers = esm_df.groupby(["device_id", "esm_session"])['timestamp'].apply(lambda x: math.ceil((x.max() - x.min()) / 1000)).reset_index() # questionnaire length
-        extracted_ers["label"] = f"straw_event_{targets_method}_" + snakemake.params["pid"] + "_" + extracted_ers.index.astype(str).str.zfill(3) 
+        extracted_ers["label"] = f"straw_event_{segmenting_method}_" + snakemake.params["pid"] + "_" + extracted_ers.index.astype(str).str.zfill(3) 
         extracted_ers[['event_timestamp', 'device_id']] = esm_df.groupby(["device_id", "esm_session"])['timestamp'].min().reset_index()[['timestamp', 'device_id']]
         extracted_ers = extracted_ers[extracted_ers["timestamp"] <= 15 * 60].reset_index(drop=True) # ensure that the longest duration of the questionnaire anwsering is 15 min 
         extracted_ers["shift_direction"] = -1 
 
-        if targets_method == "30_before":
+        if segmenting_method == "30_before":
+            """The method 30-minutes before simply takes 30 minutes before the questionnaire and sums it with the questionnaire duration.
+            The timestamps are formatted with the help of format_timestamp() method.
+            """
             time_before_questionnaire = 30 * 60 # in seconds (30 minutes)
 
             extracted_ers["length"] = (extracted_ers["timestamp"] + time_before_questionnaire).apply(lambda x: format_timestamp(x))
             extracted_ers["shift"] = time_before_questionnaire
             extracted_ers["shift"] = extracted_ers["shift"].apply(lambda x: format_timestamp(x))
         
-        elif targets_method == "90_before":
+        elif segmenting_method == "90_before":
+            """The method 90-minutes before has an important condition. If the time between the current and the previous questionnaire is
+            longer then 90 minutes it takes 90 minutes, otherwise it takes the original time difference between the questionnaires.
+            """
             time_before_questionnaire = 90 * 60 # in seconds (90 minutes)
 
             extracted_ers[['end_event_timestamp', 'device_id']] = esm_df.groupby(["device_id", "esm_session"])['timestamp'].max().reset_index()[['timestamp', 'device_id']]
@@ -69,47 +104,75 @@ def extract_ers(esm_df, device_id):
             extracted_ers["length"] = (extracted_ers["timestamp"] + extracted_ers["diffs"]).apply(lambda x: format_timestamp(x))
             extracted_ers["shift"] = extracted_ers["diffs"].apply(lambda x: format_timestamp(x))
 
-    elif targets_method == "stress_event":
+    elif segmenting_method == "stress_event":
+        """This is a special case of the method as it consists of two important parts:
+            (1) Generating of the ERS file (same as the methods above) and
+            (2) Generating targets file alongside with the correct time segment labels.
+        
+        This extracts event-related segments, depended on the event time and duration specified by the participant in the next
+        questionnaire. Additionally, 5 minutes before the specified start time of this event is taken to take into a account the 
+        possiblity of the participant not remembering the start time percisely => this parameter can be manipulated with the variable
+        "time_before_event" which is defined below. 
+        
+        By default, this method also excludes all events that are longer then 2.5 hours so that the segments are easily comparable. 
+        """
         # Get and join required data
         extracted_ers = esm_df.groupby(["device_id", "esm_session"])['timestamp'].apply(lambda x: math.ceil((x.max() - x.min()) / 1000)).reset_index().rename(columns={'timestamp': 'session_length'}) # questionnaire end timestamp
         extracted_ers = extracted_ers[extracted_ers["session_length"] <= 15 * 60].reset_index(drop=True) # ensure that the longest duration of the questionnaire anwsering is 15 min
         session_end_timestamp = esm_df.groupby(['device_id', 'esm_session'])['timestamp'].max().to_frame().rename(columns={'timestamp': 'session_end_timestamp'}) # questionnaire end timestamp
         se_time = esm_df[esm_df.questionnaire_id == 90.].set_index(['device_id', 'esm_session'])['esm_user_answer'].to_frame().rename(columns={'esm_user_answer': 'se_time'})
         se_duration = esm_df[esm_df.questionnaire_id == 91.].set_index(['device_id', 'esm_session'])['esm_user_answer'].to_frame().rename(columns={'esm_user_answer': 'se_duration'})
-        se_intensity = esm_df[esm_df.questionnaire_id == 87.].set_index(['device_id', 'esm_session'])['esm_user_answer_numeric'].to_frame().rename(columns={'esm_user_answer_numeric': 'intensity'})
         
+        # Extracted 3 targets that will be transfered with the csv file to the cleaning script. 
+        se_stressfulness_event_tg = esm_df[esm_df.questionnaire_id == 87.].set_index(['device_id', 'esm_session'])['esm_user_answer_numeric'].to_frame().rename(columns={'esm_user_answer_numeric': 'appraisal_stressfulness_event'})
+        se_threat_tg = esm_df[esm_df.questionnaire_id == 88.].groupby(["device_id", "esm_session"]).mean()['esm_user_answer_numeric'].to_frame().rename(columns={'esm_user_answer_numeric': 'appraisal_threat'})
+        se_challenge_tg = esm_df[esm_df.questionnaire_id == 89.].groupby(["device_id", "esm_session"]).mean()['esm_user_answer_numeric'].to_frame().rename(columns={'esm_user_answer_numeric': 'appraisal_challenge'})
+
+        # All relevant features are joined by inner join to remove standalone columns (e.g., stressfulness event target has larger count)
         extracted_ers = extracted_ers.join(session_end_timestamp, on=['device_id', 'esm_session'], how='inner') \
                                      .join(se_time, on=['device_id', 'esm_session'], how='inner') \
                                      .join(se_duration, on=['device_id', 'esm_session'], how='inner') \
-                                     .join(se_intensity, on=['device_id', 'esm_session'], how='inner')
+                                     .join(se_stressfulness_event_tg, on=['device_id', 'esm_session'], how='inner') \
+                                     .join(se_threat_tg, on=['device_id', 'esm_session'], how='inner') \
+                                     .join(se_challenge_tg, on=['device_id', 'esm_session'], how='inner')
 
-        # Filter sessions that are not useful
-        extracted_ers = extracted_ers[(extracted_ers.se_time != "0 - Ne spomnim se") & (extracted_ers.se_duration != "0 - Ne spomnim se")]
+
+        # Filter sessions that are not useful. Because of the ambiguity this excludes: 
+        # (1) straw event times that are marked as "0 - I don't remember"
+        # (2) straw event durations that are marked as "0 - I don't remember" 
+        extracted_ers = extracted_ers[(~extracted_ers.se_time.str.startswith("0 - ")) & (~extracted_ers.se_duration.str.startswith("0 - "))]
 
         # Transform data into its final form, ready for the extraction
-        extracted_ers.reset_index(inplace=True)
+        extracted_ers.reset_index(drop=True, inplace=True)
 
         time_before_event = 5 * 60 # in seconds (5 minutes)
         extracted_ers['event_timestamp'] = pd.to_datetime(extracted_ers['se_time']).apply(lambda x: x.timestamp() * 1000).astype('int64')
         extracted_ers['shift_direction'] = -1
 
+        # Checks whether the duration is marked with "1 - It's still ongoing" which means that the end of the current questionnaire
+        # is taken as end time of the segment. Else the user input duration is taken. 
         extracted_ers['se_duration'] = \
             np.where(
-                extracted_ers['se_duration'] == "1 - Še vedno traja", 
+                extracted_ers['se_duration'].str.startswith("1 - "),
                 extracted_ers['session_end_timestamp'] - extracted_ers['event_timestamp'], 
                 extracted_ers['se_duration']
             )
 
+        # This converts the rows of timestamps in miliseconds and the row with datetime to timestamp in seconds.
         extracted_ers['se_duration'] = \
             extracted_ers['se_duration'].apply(lambda x: math.ceil(x / 1000) if isinstance(x, int) else (pd.to_datetime(x).hour * 60 + pd.to_datetime(x).minute) * 60) + time_before_event
 
-        extracted_ers = extracted_ers[extracted_ers["se_duration"] <= 2.5 * 60 * 60].reset_index(drop=True) # Exclude events that are longer than 2.5 hours
-
-        extracted_ers["label"] = f"straw_event_{targets_method}_" + snakemake.params["pid"] + "_" + extracted_ers.index.astype(str).str.zfill(3)
         extracted_ers['shift'] = format_timestamp(time_before_event)
         extracted_ers['length'] = extracted_ers['se_duration'].apply(lambda x: format_timestamp(x))
 
-        extracted_ers[["label", "intensity"]].to_csv(snakemake.output[1], index=False)
+        # Drop event_timestamp duplicates in case of user referencing the same event over multiple questionnaires
+        extracted_ers.drop_duplicates(subset=["event_timestamp"], keep='first', inplace=True)
+        extracted_ers.reset_index(drop=True, inplace=True)
+
+        extracted_ers["label"] = f"straw_event_{segmenting_method}_" + snakemake.params["pid"] + "_" + extracted_ers.index.astype(str).str.zfill(3)
+
+        # Write the csv of extracted ERS labels with targets related to stressfulness event   
+        extracted_ers[["label", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge"]].to_csv(snakemake.output[1], index=False)
         
     else:
         raise Exception("Please select correct target method for the event-related segments.")
@@ -118,14 +181,20 @@ def extract_ers(esm_df, device_id):
     return extracted_ers[["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"]]
 
 
-# Actual code execution
+"""
+Here the code is executed - this .py file is used both for extraction of the STRAW time_segments file for the individual
+participant, and also for merging all participant's files into one combined file which is later used for the time segments 
+to all sensors assignment.
+
+There are two files involved (see rules extract_event_information_from_esm and merge_event_related_segments_files in preprocessing.smk)
+(1) ERS file which contains all the information about the time segment timings and
+(2) targets file which has corresponding target value for the segment label which is later used to merge with other features in the cleaning script.
+For more information, see the comment in the method above.
+"""
 if snakemake.params["stage"] == "extract": 
     esm_df = pd.read_csv(input_data_files['esm_raw_input'])
 
-    with open(input_data_files['pid_file'], 'r') as stream:
-        pid_file = yaml.load(stream, Loader=yaml.FullLoader)
-
-    extracted_ers = extract_ers(esm_df, pid_file["PHONE"]["DEVICE_IDS"][0])
+    extracted_ers = extract_ers(esm_df)
 
     extracted_ers.to_csv(snakemake.output[0], index=False)
 
@@ -133,7 +202,7 @@ elif snakemake.params["stage"] == "merge":
 
     input_data_files = dict(snakemake.input)
     straw_events = pd.DataFrame(columns=["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"])
-    stress_events_targets = pd.DataFrame(columns=["label", "intensity"])
+    stress_events_targets = pd.DataFrame(columns=["label", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge"])
 
     for input_file in input_data_files["ers_files"]:
         ers_df = pd.read_csv(input_file)

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,
-                         ")cannot be found in the dataset.",
-                         "Please check the names of phone_esm_ columns in z_all_sensor_features_cleaned_straw_py.csv")
+        warnings.warn(f"The requested target (, {target_variable_name} ,)cannot be found in the dataset. Please check the names of phone_esm_ columns in cleaned python file")
+        return None
+
     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)
 
-model_input.to_csv(snakemake.output[0], index=False)
+if model_input is None:
+    pd.DataFrame().to_csv(snakemake.output[0])
+else:
+    model_input.to_csv(snakemake.output[0], index=False)