Changes in cleaning script:

- target extracted from config to remove rows where target is nan
- prepared sns.heatmap for further missing values analysis
- necessary changes in config and participant p01
- picture of heatmap which shows the values state after cleaning

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'] #['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']
 
 # See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files
 CREATE_PARTICIPANT_FILES:
@@ -690,7 +690,7 @@ ALL_CLEANING_INDIVIDUAL:
         COMPUTE: False
         TYPE: median # options: zero, mean, median or k-nearest
         MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
-      COLS_NAN_THRESHOLD: 1 # set to 1 to disable
+      COLS_NAN_THRESHOLD: 1 # set to 1 remove only columns that contains all NaN
       COLS_VAR_THRESHOLD: True
       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
@@ -725,7 +725,7 @@ ALL_CLEANING_OVERALL:
         COMPUTE: False
         TYPE: median # options: zero, mean, median or k-nearest
         MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
-      COLS_NAN_THRESHOLD: 1 # set to 1 to disable
+      COLS_NAN_THRESHOLD: 1 # set to 1 remove only columns that contains all NaN
       COLS_VAR_THRESHOLD: True
       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/external/participant_files/p01.yaml

@@ -1,9 +1,9 @@
 PHONE:
-  DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524] # the participant's AWARE device id
-  PLATFORMS: [android] # or ios
-  LABEL: MyTestP01 # any string
-  START_DATE: 2020-01-01 # this can also be empty
-  END_DATE: 2021-01-01 # this can also be empty
+  DEVICE_IDS: [70cc5183-97d4-4678-b81e-a34e491e2868,d5bbb2ab-2d60-4e72-a636-17655395c401,93fae5bc-e5a9-4751-b768-fd55c821f126]
+  PLATFORMS: [android,android,android]
+  LABEL: uploader_57312
+  START_DATE: 2020-09-24 11:56:45
+  END_DATE: 2020-10-24 19:19:37
 EMPATICA:
   DEVICE_IDS: [empatica1]
   LABEL: test01

src/features/all_cleaning_individual/straw/main.py

@@ -1,20 +1,27 @@
 import pandas as pd
 import numpy as np
 import math, sys
-from sklearn.impute import KNNImputer
+import yaml
 
-def straw_cleaning(sensor_data_files, provider, target=None):
+from sklearn.impute import KNNImputer
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+def straw_cleaning(sensor_data_files, provider):
     
     features = pd.read_csv(sensor_data_files["sensor_data"][0])
+    esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
 
-    esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target columns
+    with open('config.yaml', 'r') as stream:
+        config = yaml.load(stream, Loader=yaml.FullLoader)
+
+    #Filter-out all 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
+        features = features[features['phone_esm_straw_' + target].notna()].reset_index()
+
+    test_cols = [col for col in features.columns if 'phone_calls' in col or 'phone_messages' in col]
 
-    #Filter all rows that do not have the target column available
-    # get target from config or function parameter
-    if target is None:
-        features = features[features[esm_cols[0]].notna()]
-    else:
-        features = features[features['phone_esm_straw_' + target].notna()]
 
     # TODO: reorder the cleaning steps so it makes sense for the analysis
     # TODO: add conditions that differentiates cleaning steps for standardized and nonstandardized features, for this
@@ -58,7 +65,7 @@ def straw_cleaning(sensor_data_files, provider, target=None):
     if provider["DATA_YIELD_RATIO_THRESHOLD"]:
         features = features[features[data_yield_column] >= provider["DATA_YIELD_RATIO_THRESHOLD"]]
 
-    # Remove cols if threshold of NaN values is passed
+    # Remove cols if threshold of NaN values is passed (should be <= if even all NaN columns must be preserved)
     features = features.loc[:, features.isna().sum() < provider["COLS_NAN_THRESHOLD"] * features.shape[0]]
 
     # Remove cols where variance is 0
@@ -89,6 +96,12 @@ def straw_cleaning(sensor_data_files, provider, target=None):
     min_count =  math.ceil((1 - provider["ROWS_NAN_THRESHOLD"]) * features.shape[1]) # minimal not nan values in row
     features.dropna(axis=0, thresh=min_count, inplace=True)
 
+    sns.set(rc={"figure.figsize":(16, 8)})
+    sns.heatmap(features.isna(), cbar=False)
+    plt.savefig(f'features_nans.png', bbox_inches='tight')
+
+    sys.exit()
+
     return features
 
 def impute(df, method='zero'):