Merge branch 'imputation_and_cleaning' of https://repo.ijs.si/junoslukan/rapids into imputation_and_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: ['p03'] #['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:
@@ -21,9 +21,12 @@ CREATE_PARTICIPANT_FILES:
 
 # See https://www.rapids.science/latest/setup/configuration/#time-segments
 TIME_SEGMENTS: &time_segments
-  TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT
+  TYPE: EVENT # FREQUENCY, PERIODIC, EVENT
-  FILE: "data/external/timesegments_daily.csv"
+  FILE: "data/external/straw_events.csv"
   INCLUDE_PAST_PERIODIC_SEGMENTS: TRUE # Only relevant if TYPE=PERIODIC, see docs
+  TAILORED_EVENTS: # Only relevant if TYPE=EVENT
+    COMPUTE: True
+    PARAMETER_ONE: "something"
 
 # See https://www.rapids.science/latest/setup/configuration/#timezone-of-your-study
 TIMEZONE: 

rules/preprocessing.smk

@@ -249,3 +249,26 @@ rule empatica_readable_datetime:
         "data/raw/{pid}/empatica_{sensor}_with_datetime.csv"
     script:
         "../src/data/datetime/readable_datetime.R"
+
+
+rule extract_event_information_from_esm:
+    input:
+        esm_raw_input = "data/raw/{pid}/phone_esm_raw.csv",
+        pid_file = "data/external/participant_files/{pid}.yaml"
+    params:
+        stage = "extract",
+        pid = "{pid}"
+    output:
+        "data/raw/ers/{pid}_ers.csv"
+    script:
+        "../src/features/phone_esm/straw/process_user_event_related_segments.py"
+
+rule create_event_related_segments_file:
+    input:
+        ers_files = expand("data/raw/ers/{pid}_ers.csv", pid=config["PIDS"])
+    params:
+        stage = "merge"
+    output:
+        "data/external/straw_events.csv"
+    script:
+        "../src/features/phone_esm/straw/process_user_event_related_segments.py"

src/features/all_cleaning_individual/straw/main.py

@@ -100,7 +100,7 @@ def straw_cleaning(sensor_data_files, provider):
         col.startswith('phone_screen_rapids_') or
         col.startswith('phone_wifi_visible')]
 
-    features[impute_zero] = impute(features[impute_zero], method="zero")
+    features[impute_zero+list(esm_cols.columns)] = features[impute_zero+list(esm_cols.columns)].fillna(0)
 
     ## (5) STANDARDIZATION 
     if provider["STANDARDIZATION"]:

src/features/all_cleaning_overall/straw/main.py

@@ -16,6 +16,9 @@ def straw_cleaning(sensor_data_files, provider, target):
 
     features = features[features['local_segment_label'] == 'working_day'] # Filtriranje ustreznih časovnih segmentov
     
+    # print(features)
+    # sys.exit()
+    
     esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
 
     with open('config.yaml', 'r') as stream:
@@ -29,7 +32,11 @@ def straw_cleaning(sensor_data_files, provider, target):
     if config['PARAMS_FOR_ANALYSIS']['TARGET']['COMPUTE']:
         features = features[features['phone_esm_straw_' + target].notna()].reset_index(drop=True)
     
+    if features.empty:
+        return pd.DataFrame(columns=excluded_columns)
+
     graph_bf_af(features, "2target_rows_after")
+    print("HERE1", target, features["pid"])
 
     # (2) QUALITY CHECK (DATA YIELD COLUMN) drops the rows where E4 or phone data is low quality
     phone_data_yield_unit = provider["PHONE_DATA_YIELD_FEATURE"].split("_")[3].lower()
@@ -41,13 +48,12 @@ def straw_cleaning(sensor_data_files, provider, target):
         raise KeyError(f"RAPIDS provider needs to clean the selected event features based on {phone_data_yield_column} and empatica_data_yield columns. For phone data yield, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyielded{data_yield_unit}' in [FEATURES].")
 
     hist = features[["empatica_data_yield", phone_data_yield_column]].hist()
-    plt.legend()
     plt.savefig(f'phone_E4_histogram.png', bbox_inches='tight')
 
     # Drop rows where phone data yield is less then given threshold
     if provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"]:
-        print("\nThreshold:", provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"])
+        # print("\nThreshold:", provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"])
-        print("Phone features data yield stats:", features[phone_data_yield_column].describe(), "\n")
+        # print("Phone features data yield stats:", features[phone_data_yield_column].describe(), "\n")
         # print(features[phone_data_yield_column].sort_values())
         hist = features[phone_data_yield_column].hist(bins=5)
         plt.close()
@@ -55,13 +61,17 @@ def straw_cleaning(sensor_data_files, provider, target):
 
     # Drop rows where empatica data yield is less then given threshold
     if provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]:
-        print("\nThreshold:", provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"])
+        # print("\nThreshold:", provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"])
-        print("E4 features data yield stats:", features["empatica_data_yield"].describe(), "\n")
+        # print("E4 features data yield stats:", features["empatica_data_yield"].describe(), "\n")
         # print(features["empatica_data_yield"].sort_values())
         features = features[features["empatica_data_yield"] >= provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]].reset_index(drop=True)
 
+
     graph_bf_af(features, "3data_yield_drop_rows")
 
+    if features.empty:
+        return pd.DataFrame(columns=excluded_columns)
+
     # (3) CONTEXTUAL IMPUTATION
 
     # Impute selected phone features with a high number
@@ -85,7 +95,7 @@ def straw_cleaning(sensor_data_files, provider, target):
     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
 
-    # Impute selected phone features with 0
+    # Impute selected phone features with 0 + impute ESM features with 0
     impute_zero = [col for col in features if \
         col.startswith('phone_applications_foreground_rapids_') or
         col.startswith('phone_battery_rapids_') or
@@ -95,23 +105,23 @@ def straw_cleaning(sensor_data_files, provider, target):
         col.startswith('phone_messages_rapids_') or
         col.startswith('phone_screen_rapids_') or
         col.startswith('phone_wifi_visible')]
-    features[impute_zero] = impute(features[impute_zero], method="zero")
         
-    graph_bf_af(features, "5zero_imp")
+    features[impute_zero+list(esm_cols.columns)] = features[impute_zero+list(esm_cols.columns)].fillna(0)
+
+    graph_bf_af(features, "4context_imp")
  
     # (4) REMOVE COLS IF THEIR NAN THRESHOLD IS PASSED (should be <= if even all NaN columns must be preserved - this solution now drops columns with all NaN rows)
     esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
 
     features = features.loc[:, features.isna().sum() < provider["COLS_NAN_THRESHOLD"] * features.shape[0]]
 
-    graph_bf_af(features, "6too_much_nans_cols")
+    graph_bf_af(features, "5too_much_nans_cols")
-
     # (5) REMOVE COLS WHERE VARIANCE IS 0
 
     if provider["COLS_VAR_THRESHOLD"]:
         features.drop(features.std()[features.std() == 0].index.values, axis=1, inplace=True)
 
-    graph_bf_af(features, "7variance_drop")
+    graph_bf_af(features, "6variance_drop")
 
     # Preserve esm cols if deleted (has to come after drop cols operations)
     for esm in esm_cols:
@@ -122,9 +132,13 @@ def straw_cleaning(sensor_data_files, provider, target):
     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) # Thresh => at least this many not-nans
 
-    graph_bf_af(features, "8too_much_nans_rows")
+    graph_bf_af(features, "7too_much_nans_rows")
 
-    # (7) STANDARDIZATION 
+    if features.empty:
+        return pd.DataFrame(columns=excluded_columns)
+
+
+    # (7) STANDARDIZATION TODO: exclude nominal features from standardization 
 
     if provider["STANDARDIZATION"]:
         # Expected warning within this code block
@@ -133,14 +147,15 @@ def straw_cleaning(sensor_data_files, provider, target):
             features.loc[:, ~features.columns.isin(excluded_columns + ["pid"])] = \
                 features.loc[:, ~features.columns.isin(excluded_columns)].groupby('pid').transform(lambda x: StandardScaler().fit_transform(x.values[:,np.newaxis]).ravel())
 
-    graph_bf_af(features, "9standardization")
+    graph_bf_af(features, "8standardization")
 
     # (8) IMPUTATION: IMPUTE DATA WITH KNN METHOD
     features.reset_index(drop=True, inplace=True)
     impute_cols = [col for col in features.columns if col not in excluded_columns and col != "pid"]
+
     features[impute_cols] = impute(features[impute_cols], method="knn")
 
-    graph_bf_af(features, "10knn_after")
+    graph_bf_af(features, "9knn_after")
 
 
     # (9) DROP HIGHLY CORRELATED FEATURES

src/features/phone_esm/straw/process_user_event_related_segments.py

@@ -0,0 +1,60 @@
+import pandas as pd
+import numpy as np
+import datetime
+
+import math, sys, yaml
+
+from esm_preprocess import preprocess_esm, clean_up_esm
+
+input_data_files = dict(snakemake.input)
+
+def extract_ers_from_file(esm_df, device_id): # TODO: kako se bodo pridobili device_id? Bo torej potreben tudi p0??.yaml?
+
+    pd.set_option("display.max_rows", None)
+
+    # extracted_ers = pd.DataFrame(columns=["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"])
+
+    esm_df = clean_up_esm(preprocess_esm(esm_df))
+
+    # Take only during work sessions
+    during_work = esm_df[esm_df["esm_trigger"].str.contains("during_work", na=False)]
+    esm_trigger_group = esm_df.groupby("esm_session").agg(pd.Series.mode)['esm_trigger'] # Get most frequent esm_trigger within particular session  
+    esm_filtered_sessions = list(esm_trigger_group[esm_trigger_group == 'during_work'].index) # Take only sessions that contains during work  
+    esm_df = esm_df[esm_df["esm_session"].isin(esm_filtered_sessions)]
+
+    # Extract time-relevant information
+    extracted_ers = esm_df.groupby("esm_session")['timestamp'].apply(lambda x: math.ceil((x.max() - x.min()) / 1000)).reset_index() # in rounded up seconds
+    time_before_questionnaire = 30 * 60 # in seconds (30 minutes)
+
+    extracted_ers["label"] = "straw_event_" + snakemake.params["pid"] + "_" + extracted_ers["esm_session"].astype(str)
+    extracted_ers["event_timestamp"] = esm_df.groupby("esm_session")['timestamp'].min().reset_index()['timestamp']
+    extracted_ers["length"] = (extracted_ers["timestamp"] + time_before_questionnaire).apply(lambda x: f"{x//3600}H {x % 3600 // 60}M {x % 60}S" if x//3600 > 0 else f"{x % 3600 // 60}M {x % 60}S")
+    extracted_ers["shift"] = time_before_questionnaire
+    extracted_ers["shift"] = extracted_ers["shift"].apply(lambda x: f"{x//3600}H {x % 3600 // 60}M {x % 60}S" if x//3600 > 0 else f"{x % 3600 // 60}M {x % 60}S") 
+    extracted_ers["shift_direction"] = -1 
+    extracted_ers["device_id"] = device_id 
+
+    return extracted_ers[["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"]]
+
+# TODO: potrebno preveriti kako se izvaja iskanje prek device_id -> na tem temelji tudi proces ekstrahiranja ERS
+
+if snakemake.params["stage"] == "extract": # TODO: najprej preveri ustreznost umeščenosti v RAPIDS pipelineu
+    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_from_file(esm_df, pid_file["PHONE"]["DEVICE_IDS"][0])
+
+    extracted_ers.to_csv(snakemake.output[0], index=False)
+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"])
+    
+    for input_file in input_data_files["ers_files"]:
+        ers_df = pd.read_csv(input_file)
+        straw_events = pd.concat([straw_events, ers_df], axis=0, ignore_index=True)
+
+    straw_events.to_csv(snakemake.output[0], index=False)
+

src/models/merge_features_and_targets_for_population_model.py

@@ -12,9 +12,13 @@ for baseline_features_path in snakemake.input["demographic_features"]:
     all_baseline_features = pd.concat([all_baseline_features, baseline_features], axis=0)
 
 # merge sensor features and baseline features
+if not sensor_features.empty:
     features = sensor_features.merge(all_baseline_features, on="pid", how="left")
 
     target_variable_name = snakemake.params["target_variable"]
     model_input = retain_target_column(features, target_variable_name)
 
     model_input.to_csv(snakemake.output[0], index=False)
+    
+else:
+    sensor_features.to_csv(snakemake.output[0], index=False)