Disable discarding rows if DATA_YIELD_RATIO_THRESHOLD==0.

config.yaml

@@ -3,7 +3,7 @@
 ########################################################################################################################
 
 # See https://www.rapids.science/latest/setup/configuration/#participant-files
-PIDS: ['p031', 'p032', 'p033', 'p034', 'p035', 'p036', 'p037', 'p038', 'p039', 'p040', 'p042', 'p043', 'p044', 'p045', 'p046', 'p049', 'p050', 'p052', 'p053', 'p054', 'p055', 'p057', 'p058', 'p059', 'p060', 'p061', 'p062', 'p064', 'p067', 'p068', 'p069', 'p070', 'p071', 'p072', 'p073', 'p074', 'p075', 'p076', 'p077', 'p078', 'p079', 'p080', 'p081', 'p082', 'p083', 'p084', 'p085', 'p086', 'p088', 'p089', 'p090', 'p091', 'p092', 'p093', 'p106', 'p107']
+PIDS: ['p01']
 
 # See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files
 CREATE_PARTICIPANT_FILES:
@@ -685,14 +685,14 @@ ALL_CLEANING_INDIVIDUAL:
         CORR_THRESHOLD: 0.95
       SRC_SCRIPT: src/features/all_cleaning_individual/rapids/main.R
     STRAW: # currently the same as RAPIDS provider with a change in selecting the imputation type
-      COMPUTE: False
+      COMPUTE: True
       IMPUTE_PHONE_SELECTED_EVENT_FEATURES:
         COMPUTE: False
         TYPE: median # options: zero, mean, median or k-nearest
         MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
       COLS_NAN_THRESHOLD: 0.3 # set to 1 to disable
       COLS_VAR_THRESHOLD: True
-      ROWS_NAN_THRESHOLD: 0 # set to 1 to disable
+      ROWS_NAN_THRESHOLD: 1 # set to 1 to disable
       DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
       DATA_YIELD_RATIO_THRESHOLD: 0 # set to 0 to disable
       DROP_HIGHLY_CORRELATED_FEATURES:
@@ -705,7 +705,7 @@ ALL_CLEANING_OVERALL:
   CLEAN_STANDARDIZED: True
   PROVIDERS:
     RAPIDS:
-      COMPUTE: False
+      COMPUTE: True
       IMPUTE_SELECTED_EVENT_FEATURES:
         COMPUTE: False
         MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
@@ -720,14 +720,14 @@ ALL_CLEANING_OVERALL:
         CORR_THRESHOLD: 0.95
       SRC_SCRIPT: src/features/all_cleaning_overall/rapids/main.R
     STRAW: # currently the same as RAPIDS provider with a change in selecting the imputation type
-      COMPUTE: False
+      COMPUTE: True
       IMPUTE_PHONE_SELECTED_EVENT_FEATURES:
         COMPUTE: False
         TYPE: median # options: zero, mean, median or k-nearest
         MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
       COLS_NAN_THRESHOLD: 0.3 # set to 1 to disable
       COLS_VAR_THRESHOLD: True
-      ROWS_NAN_THRESHOLD: 0 # set to 1 to disable
+      ROWS_NAN_THRESHOLD: 1 # set to 1 to disable
       DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
       DATA_YIELD_RATIO_THRESHOLD: 0 # set to 0 to disable
       DROP_HIGHLY_CORRELATED_FEATURES:
@@ -759,7 +759,7 @@ STANDARDIZATION: # Standardization for both providers is executed if only one of
 
 PARAMS_FOR_ANALYSIS:
   BASELINE:
-    COMPUTE: True
+    COMPUTE: False
     FOLDER: data/external/baseline
     CONTAINER: [results-survey637813_final.csv,  # Slovenia
                 results-survey358134_final.csv,  # Belgium 1
@@ -770,5 +770,5 @@ PARAMS_FOR_ANALYSIS:
     CATEGORICAL_FEATURES: [gender]
 
   TARGET:
-    COMPUTE: True
+    COMPUTE: False
     LABEL: PANAS_negative_affect_mean

src/features/all_cleaning_individual/rapids/main.R

@@ -39,8 +39,10 @@ rapids_cleaning <- function(sensor_data_files, provider){
     if(!data_yield_column %in% colnames(clean_features)){
         stop(paste0("Error: RAPIDS provider needs to clean data based on ", data_yield_column, " column, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyielded", data_yield_unit, "' in [FEATURES]."))
     }
+    if (data_yield_ratio_threshold > 0) {
         clean_features <- clean_features %>% 
         filter(.[[data_yield_column]] >= data_yield_ratio_threshold)
+    }
 
     # Drop columns with a percentage of NA values above cols_nan_threshold
     if(nrow(clean_features))

src/features/all_cleaning_individual/straw/main.py

@@ -36,6 +36,7 @@ def straw_cleaning(sensor_data_files, provider):
     if not data_yield_column in features.columns:
         raise KeyError(f"RAPIDS provider needs to impute the selected event features based on {data_yield_column} column, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyielded{data_yield_unit}' in [FEATURES].")
         
+    if provider["DATA_YIELD_RATIO_THRESHOLD"]:
         features = features[features[data_yield_column] >= provider["DATA_YIELD_RATIO_THRESHOLD"]]
 
     esm_cols = features.loc[:, features.columns.str.startswith('phone_esm')] # For later preservation of esm_cols

src/features/all_cleaning_overall/rapids/main.R

@@ -39,8 +39,10 @@ rapids_cleaning <- function(sensor_data_files, provider){
     if(!data_yield_column %in% colnames(clean_features)){
         stop(paste0("Error: RAPIDS provider needs to clean data based on ", data_yield_column, " column, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyielded", data_yield_unit, "' in [FEATURES]."))
     }
+    if (data_yield_ratio_threshold > 0) {
         clean_features <- clean_features %>% 
         filter(.[[data_yield_column]] >= data_yield_ratio_threshold)
+    }
 
     # Drop columns with a percentage of NA values above cols_nan_threshold
     if(nrow(clean_features))

src/features/all_cleaning_overall/straw/main.py

@@ -36,6 +36,7 @@ def straw_cleaning(sensor_data_files, provider):
     if not data_yield_column in features.columns:
         raise KeyError(f"RAPIDS provider needs to impute the selected event features based on {data_yield_column} column, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyielded{data_yield_unit}' in [FEATURES].")
         
+    if provider["DATA_YIELD_RATIO_THRESHOLD"]:
         features = features[features[data_yield_column] >= provider["DATA_YIELD_RATIO_THRESHOLD"]]
 
     esm_cols = features.loc[:, features.columns.str.startswith('phone_esm')] # For later preservation of esm_cols