rapids/src/features/phone_esm/straw/main.py

67 lines
2.7 KiB
Python

import pandas as pd
QUESTIONNAIRE_IDS = {
"sleep_quality": 1,
"PANAS_positive_affect": 8,
"PANAS_negative_affect": 9,
"JCQ_job_demand": 10,
"JCQ_job_control": 11,
"JCQ_supervisor_support": 12,
"JCQ_coworker_support": 13,
"PFITS_supervisor": 14,
"PFITS_coworkers": 15,
"UWES_vigor": 16,
"UWES_dedication": 17,
"UWES_absorption": 18,
"COPE_active": 19,
"COPE_support": 20,
"COPE_emotions": 21,
"balance_life_work": 22,
"balance_work_life": 23,
"recovery_experience_detachment": 24,
"recovery_experience_relaxation": 25,
"symptoms": 26,
"appraisal_stressfulness_event": 87,
"appraisal_threat": 88,
"appraisal_challenge": 89,
"appraisal_event_time": 90,
"appraisal_event_duration": 91,
"appraisal_event_work_related": 92,
"appraisal_stressfulness_period": 93,
"late_work": 94,
"work_hours": 95,
"left_work": 96,
"activities": 97,
"coffee_breaks": 98,
"at_work_yet": 99,
}
def straw_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
esm_data = pd.read_csv(sensor_data_files["sensor_data"])
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", "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))
esm_features = pd.DataFrame(columns=["local_segment"] + features_to_compute)
if not esm_data.empty:
esm_data = filter_data_by_segment(esm_data, time_segment)
if not esm_data.empty:
esm_features = pd.DataFrame()
for scale in requested_scales:
questionnaire_id = QUESTIONNAIRE_IDS[scale]
mask = esm_data["questionnaire_id"] == questionnaire_id
esm_features[scale + "_mean"] = esm_data.loc[mask].groupby(["local_segment"])["esm_user_score"].mean()
#TODO Create the column esm_user_score in esm_clean. Currently, this is only done when reversing.
esm_features = esm_features.reset_index()
if 'index' in esm_features: # In calse of empty esm_features df
esm_features.rename(columns={'index': 'local_segment'}, inplace=True)
return esm_features