Add additional appraisal targets.
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621f11b2d9
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@ -242,7 +242,7 @@ PHONE_ESM:
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STRAW:
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COMPUTE: True
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SCALES: ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support",
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"appraisal_stressfulness_period", "appraisal_stressfulness_event"]
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"appraisal_stressfulness_period", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge"]
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FEATURES: [mean]
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SRC_SCRIPT: src/features/phone_esm/straw/main.py
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@ -733,6 +733,6 @@ PARAMS_FOR_ANALYSIS:
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TARGET:
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COMPUTE: True
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LABEL: PANAS_negative_affect_mean
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ALL_LABELS: [appraisal_stressfulness_event_mean]
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ALL_LABELS: [appraisal_stressfulness_event_mean, appraisal_threat_mean, appraisal_challenge_mean]
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# PANAS_positive_affect_mean, PANAS_negative_affect_mean, JCQ_job_demand_mean, JCQ_job_control_mean, JCQ_supervisor_support_mean,
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# JCQ_coworker_support_mean, appraisal_stressfulness_period_mean, appraisal_stressfulness_event_mean
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@ -23,13 +23,24 @@ def straw_cleaning(sensor_data_files, provider, target):
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graph_bf_af(features, "1target_rows_before")
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# (1.0) OVERRIDE STRESSFULNESS EVENT TARGETS IF ERS TARGETS_METHOD IS "STRESS_EVENT"
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if config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["TARGETS_METHOD"] == "stress_event" and \
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"appraisal_stressfulness_event_mean" in config['PARAMS_FOR_ANALYSIS']['TARGET']['ALL_LABELS']:
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if config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["TARGETS_METHOD"] == "stress_event":
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stress_events_targets = pd.read_csv("data/external/stress_event_targets.csv")
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if "appraisal_stressfulness_event_mean" in config['PARAMS_FOR_ANALYSIS']['TARGET']['ALL_LABELS']:
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features.drop(columns=['phone_esm_straw_appraisal_stressfulness_event_mean'], inplace=True)
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features = features.merge(stress_events_targets.rename(columns={'label': 'local_segment_label'}), on=['local_segment_label'], how='inner') \
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.rename(columns={'intensity': 'phone_esm_straw_appraisal_stressfulness_event_mean'})
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.rename(columns={'appraisal_stressfulness_event': 'phone_esm_straw_appraisal_stressfulness_event_mean'})
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if "appraisal_threat_mean" in config['PARAMS_FOR_ANALYSIS']['TARGET']['ALL_LABELS']:
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features.drop(columns=['phone_esm_straw_appraisal_threat_mean'], inplace=True)
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features = features.merge(stress_events_targets.rename(columns={'label': 'local_segment_label'}), on=['local_segment_label'], how='inner') \
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.rename(columns={'appraisal_threat_mean': 'phone_esm_straw_appraisal_threat_mean'})
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if "appraisal_challenge_mean" in config['PARAMS_FOR_ANALYSIS']['TARGET']['ALL_LABELS']:
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features.drop(columns=['phone_esm_straw_appraisal_challenge_mean'], inplace=True)
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features = features.merge(stress_events_targets.rename(columns={'label': 'local_segment_label'}), on=['local_segment_label'], how='inner') \
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.rename(columns={'appraisal_challenge': 'phone_esm_straw_appraisal_challenge_mean'})
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esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
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@ -42,7 +42,8 @@ def straw_features(sensor_data_files, time_segment, provider, filter_data_by_seg
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requested_features = provider["FEATURES"]
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# name of the features this function can compute
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requested_scales = provider["SCALES"]
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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"]
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base_features_names = ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support", \
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"appraisal_stressfulness_period", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge"]
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#TODO Check valid questionnaire and feature names.
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# the subset of requested features this function can compute
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features_to_compute = list(set(requested_features) & set(base_features_names))
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