101 lines
3.5 KiB
Python
101 lines
3.5 KiB
Python
import pandas as pd
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import features.esm
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QUESTIONNAIRE_ID_SAM = {
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"event_stress": 87,
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"event_threat": 88,
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"event_challenge": 89,
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"event_time": 90,
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"event_duration": 91,
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"event_work_related": 92,
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"period_stress": 93,
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}
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QUESTIONNAIRE_ID_SAM_LOW = min(QUESTIONNAIRE_ID_SAM.values())
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QUESTIONNAIRE_ID_SAM_HIGH = max(QUESTIONNAIRE_ID_SAM.values())
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GROUP_QUESTIONNAIRES_BY = [
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"participant_id",
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"device_id",
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"esm_session",
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"questionnaire_id",
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]
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# Each questionnaire occurs only once within each esm_session on the same device within the same participant.
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def extract_stressful_events(df_esm: pd.DataFrame) -> pd.DataFrame:
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# 0. Select only questions from Stress Appraisal Measure.
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df_esm_preprocessed = features.esm.preprocess_esm(df_esm)
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df_esm_sam = df_esm_preprocessed[
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(df_esm_preprocessed["questionnaire_id"] >= QUESTIONNAIRE_ID_SAM_LOW)
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& (df_esm_preprocessed["questionnaire_id"] <= QUESTIONNAIRE_ID_SAM_HIGH)
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]
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df_esm_sam_clean = features.esm.clean_up_esm(df_esm_sam)
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# 1.
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df_esm_event_threat_challenge_mean_wide = calculate_threat_challenge_means(
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df_esm_sam_clean
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)
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# 2.
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df_esm_event_stress = detect_stressful_event(df_esm_sam_clean)
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# Join all the features related to the events.
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df_esm_events = df_esm_event_threat_challenge_mean_wide.join(
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df_esm_event_stress[
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GROUP_QUESTIONNAIRES_BY + ["event_present", "event_stressfulness"]
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].set_index(GROUP_QUESTIONNAIRES_BY)
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)
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return df_esm_events
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def calculate_threat_challenge_means(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
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# Select only threat and challenge assessments for events
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df_esm_event_threat_challenge = df_esm_sam_clean[
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(
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df_esm_sam_clean["questionnaire_id"]
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== QUESTIONNAIRE_ID_SAM.get("event_threat")
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)
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| (
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df_esm_sam_clean["questionnaire_id"]
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== QUESTIONNAIRE_ID_SAM.get("event_challenge")
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)
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]
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# Calculate mean of threat and challenge subscales for each ESM session.
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df_esm_event_threat_challenge_mean = (
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df_esm_event_threat_challenge.groupby(GROUP_QUESTIONNAIRES_BY)
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.esm_user_answer_numeric.agg("mean")
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.reset_index()
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.rename(columns={"esm_user_answer_numeric": "esm_numeric_mean"})
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)
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# Rename questionnaire ID to indicate their names.
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df_esm_event_threat_challenge_mean[
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"event_subscale"
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] = df_esm_event_threat_challenge_mean.questionnaire_id.astype(
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"category"
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).cat.rename_categories(
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{
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QUESTIONNAIRE_ID_SAM.get("event_threat"): "threat_mean",
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QUESTIONNAIRE_ID_SAM.get("event_challenge"): "challenge_mean",
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}
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)
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# Pivot a table so that each ESM session is represented by one row with threat and challenge means as two columns.
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df_esm_event_threat_challenge_mean_wide = pd.pivot(
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df_esm_event_threat_challenge_mean,
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index=GROUP_QUESTIONNAIRES_BY,
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columns=["event_subscale"],
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values=["esm_numeric_mean"],
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)
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return df_esm_event_threat_challenge_mean_wide
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def detect_stressful_event(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
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df_esm_event_stress = df_esm_sam_clean[
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df_esm_sam_clean["questionnaire_id"] == QUESTIONNAIRE_ID_SAM.get("event_stress")
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]
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df_esm_event_stress = df_esm_event_stress.assign(
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event_present=lambda x: x.esm_user_answer_numeric > 0,
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event_stressfulness=lambda x: x.esm_user_answer_numeric,
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)
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return df_esm_event_stress
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