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Add additional appraisal targets.

imputation_and_cleaning
Primoz 2022-11-15 10:14:07 +00:00
parent 621f11b2d9
commit ab803ee49c
3 changed files with 21 additions and 9 deletions
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4
config.yaml
View File

@ -242,7 +242,7 @@ PHONE_ESM:
STRAW:
COMPUTE: True
SCALES: ["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_stressfulness_period", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge"]
FEATURES: [mean]
SRC_SCRIPT: src/features/phone_esm/straw/main.py
@ -733,6 +733,6 @@ PARAMS_FOR_ANALYSIS:
TARGET:
COMPUTE: True
LABEL: PANAS_negative_affect_mean
ALL_LABELS: [appraisal_stressfulness_event_mean]
ALL_LABELS: [appraisal_stressfulness_event_mean, appraisal_threat_mean, appraisal_challenge_mean]
# PANAS_positive_affect_mean, PANAS_negative_affect_mean, JCQ_job_demand_mean, JCQ_job_control_mean, JCQ_supervisor_support_mean,
# JCQ_coworker_support_mean, appraisal_stressfulness_period_mean, appraisal_stressfulness_event_mean

23
src/features/all_cleaning_overall/straw/main.py
View File

@ -23,13 +23,24 @@ def straw_cleaning(sensor_data_files, provider, target):
graph_bf_af(features, "1target_rows_before")
# (1.0) OVERRIDE STRESSFULNESS EVENT TARGETS IF ERS TARGETS_METHOD IS "STRESS_EVENT"
if config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["TARGETS_METHOD"] == "stress_event" and \
"appraisal_stressfulness_event_mean" in config['PARAMS_FOR_ANALYSIS']['TARGET']['ALL_LABELS']:
if config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["TARGETS_METHOD"] == "stress_event":
stress_events_targets = pd.read_csv("data/external/stress_event_targets.csv")
features.drop(columns=['phone_esm_straw_appraisal_stressfulness_event_mean'], inplace=True)
features = features.merge(stress_events_targets.rename(columns={'label': 'local_segment_label'}), on=['local_segment_label'], how='inner') \
.rename(columns={'intensity': 'phone_esm_straw_appraisal_stressfulness_event_mean'})
if "appraisal_stressfulness_event_mean" in config['PARAMS_FOR_ANALYSIS']['TARGET']['ALL_LABELS']:
features.drop(columns=['phone_esm_straw_appraisal_stressfulness_event_mean'], inplace=True)
features = features.merge(stress_events_targets.rename(columns={'label': 'local_segment_label'}), on=['local_segment_label'], how='inner') \
.rename(columns={'appraisal_stressfulness_event': 'phone_esm_straw_appraisal_stressfulness_event_mean'})
if "appraisal_threat_mean" in config['PARAMS_FOR_ANALYSIS']['TARGET']['ALL_LABELS']:
features.drop(columns=['phone_esm_straw_appraisal_threat_mean'], inplace=True)
features = features.merge(stress_events_targets.rename(columns={'label': 'local_segment_label'}), on=['local_segment_label'], how='inner') \
.rename(columns={'appraisal_threat_mean': 'phone_esm_straw_appraisal_threat_mean'})
if "appraisal_challenge_mean" in config['PARAMS_FOR_ANALYSIS']['TARGET']['ALL_LABELS']:
features.drop(columns=['phone_esm_straw_appraisal_challenge_mean'], inplace=True)
features = features.merge(stress_events_targets.rename(columns={'label': 'local_segment_label'}), on=['local_segment_label'], how='inner') \
.rename(columns={'appraisal_challenge': 'phone_esm_straw_appraisal_challenge_mean'})
esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns

3
src/features/phone_esm/straw/main.py
View File

@ -42,7 +42,8 @@ def straw_features(sensor_data_files, time_segment, provider, filter_data_by_seg
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"]
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))