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Prepare method-based logic for ERS generating.

imputation_and_cleaning
Primoz 2022-11-04 09:11:53 +00:00
parent eefa9f3f4d
commit 62f46ea376
2 changed files with 16 additions and 13 deletions
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2
config.yaml
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@ -26,7 +26,7 @@ TIME_SEGMENTS: &time_segments
INCLUDE_PAST_PERIODIC_SEGMENTS: TRUE # Only relevant if TYPE=PERIODIC, see docs INCLUDE_PAST_PERIODIC_SEGMENTS: TRUE # Only relevant if TYPE=PERIODIC, see docs
TAILORED_EVENTS: # Only relevant if TYPE=EVENT TAILORED_EVENTS: # Only relevant if TYPE=EVENT
COMPUTE: True COMPUTE: True
TARGETS_METHOD: "none" TARGETS_METHOD: "thirty_before"
# See https://www.rapids.science/latest/setup/configuration/#timezone-of-your-study # See https://www.rapids.science/latest/setup/configuration/#timezone-of-your-study
TIMEZONE: TIMEZONE:

27
src/features/phone_esm/straw/process_user_event_related_segments.py
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@ -30,7 +30,6 @@ def extract_ers_from_file(esm_df, device_id):
with open('config.yaml', 'r') as stream: with open('config.yaml', 'r') as stream:
config = yaml.load(stream, Loader=yaml.FullLoader) config = yaml.load(stream, Loader=yaml.FullLoader)
targets_method = config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["TARGETS_METHOD"]
esm_preprocessed = clean_up_esm(preprocess_esm(esm_df)) esm_preprocessed = clean_up_esm(preprocess_esm(esm_df))
@ -39,19 +38,23 @@ def extract_ers_from_file(esm_df, device_id):
esm_filtered_sessions = classified[classified["session_response"] == 'ema_completed'].reset_index()[['device_id', 'esm_session']] 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']))] 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']))]
# Kako ugotoviti, kje je bilo vprašanje na distressed?
# Extract time-relevant information # Extract time-relevant information
time_before_questionnaire = 30 * 60 # in seconds (30 minutes) targets_method = config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["TARGETS_METHOD"]
extracted_ers = esm_df.groupby(["device_id", "esm_session"])['timestamp'].apply(lambda x: math.ceil((x.max() - x.min()) / 1000)).reset_index() # in rounded up in seconds if targets_method == "thirty_before": # takes 30-minute peroid before the questionnaire + the duration of the questionnaire
extracted_ers[['event_timestamp', 'device_id']] = esm_df.groupby(["device_id", "esm_session"])['timestamp'].min().reset_index()[['timestamp', 'device_id']] time_before_questionnaire = 30 * 60 # in seconds (30 minutes)
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["label"] = "straw_event_" + snakemake.params["pid"] + "_" + extracted_ers.index.astype(str).str.zfill(3) extracted_ers = esm_df.groupby(["device_id", "esm_session"])['timestamp'].apply(lambda x: math.ceil((x.max() - x.min()) / 1000)).reset_index() # in rounded up in seconds
extracted_ers["length"] = (extracted_ers["timestamp"] + time_before_questionnaire).apply(lambda x: format_timestamp(x)) extracted_ers[['event_timestamp', 'device_id']] = esm_df.groupby(["device_id", "esm_session"])['timestamp'].min().reset_index()[['timestamp', 'device_id']]
extracted_ers["shift"] = time_before_questionnaire 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"] = extracted_ers["shift"].apply(lambda x: format_timestamp(x)) extracted_ers["label"] = "straw_event_" + snakemake.params["pid"] + "_" + extracted_ers.index.astype(str).str.zfill(3)
extracted_ers["shift_direction"] = -1 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))
extracted_ers["shift_direction"] = -1
else:
extracted_ers = pd.DataFrame(columns=["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"])
# sys.exit() # sys.exit()
return extracted_ers[["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"]] return extracted_ers[["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"]]