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48578d8800
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c05b047c2d
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import numpy as np
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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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]
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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 to the previously calculated 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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# 3.
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df_esm_event_work_related = detect_event_work_related(df_esm_sam_clean)
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df_esm_events = df_esm_events.join(
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df_esm_event_work_related[
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GROUP_QUESTIONNAIRES_BY + ["event_work_related"]
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].set_index(GROUP_QUESTIONNAIRES_BY)
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)
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# 4.
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df_esm_event_time = convert_event_time(df_esm_sam_clean)
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df_esm_events = df_esm_events.join(
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df_esm_event_time[GROUP_QUESTIONNAIRES_BY + ["event_time"]].set_index(
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GROUP_QUESTIONNAIRES_BY
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)
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)
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# 5.
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df_esm_event_duration = extract_event_duration(df_esm_sam_clean)
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df_esm_events = df_esm_events.join(
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df_esm_event_duration[
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GROUP_QUESTIONNAIRES_BY + ["event_duration", "event_duration_info"]
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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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"""
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This function calculates challenge and threat (two Stress Appraisal Measure subscales) means,
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for each ESM session (within participants and devices).
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It creates a grouped dataframe with means in two columns.
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Parameters
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----------
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df_esm_sam_clean: pd.DataFrame
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A cleaned up dataframe of Stress Appraisal Measure items.
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Returns
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-------
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df_esm_event_threat_challenge_mean_wide: pd.DataFrame
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A dataframe of unique ESM sessions (by participants and devices) with threat and challenge means.
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"""
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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_wide = pd.pivot_table(
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df_esm_event_threat_challenge,
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index=["participant_id", "device_id", "esm_session"],
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columns=["questionnaire_id"],
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values=["esm_user_answer_numeric"],
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aggfunc="mean",
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)
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# Drop unnecessary column values.
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df_esm_event_threat_challenge_mean_wide.columns = df_esm_event_threat_challenge_mean_wide.columns.get_level_values(
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1
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)
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df_esm_event_threat_challenge_mean_wide.columns.name = None
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df_esm_event_threat_challenge_mean_wide.rename(
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columns={
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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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inplace=True,
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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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"""
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Participants were asked: "Was there a particular event that created tension in you?"
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The following options were available:
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0 - No,
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1 - Yes, slightly,
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2 - Yes, moderately,
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3 - Yes, considerably,
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4 - Yes, extremely.
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This function indicates whether there was a stressful event (True/False)
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and how stressful it was on a scale of 1 to 4.
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Parameters
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----------
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df_esm_sam_clean: pd.DataFrame
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A cleaned up dataframe of Stress Appraisal Measure items.
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Returns
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-------
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df_esm_event_stress: pd.DataFrame
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The same dataframe with two new columns:
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- event_present, indicating whether there was a stressful event at all,
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- event_stressfulness, a numeric answer (1-4) to the single item question.
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"""
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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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def detect_event_work_related(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
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"""
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This function simply adds a column indicating the answer to the question:
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"Was/is this event work-related?"
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Parameters
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----------
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df_esm_sam_clean: pd.DataFrame
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A cleaned up dataframe of Stress Appraisal Measure items.
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Returns
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-------
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df_esm_event_stress: pd.DataFrame
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The same dataframe with a new column event_work_related (True/False).
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"""
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df_esm_event_stress = df_esm_sam_clean[
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df_esm_sam_clean["questionnaire_id"]
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== QUESTIONNAIRE_ID_SAM.get("event_work_related")
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]
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df_esm_event_stress = df_esm_event_stress.assign(
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event_work_related=lambda x: x.esm_user_answer_numeric > 0
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)
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return df_esm_event_stress
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def convert_event_time(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
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"""
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This function only serves to convert the string datetime answer into a real datetime type.
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Errors during this conversion are coerced, meaning that non-datetime answers are assigned Not a Time (NaT).
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NOTE: Since the only available non-datetime answer to this question was "0 - I do not remember",
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the NaTs can be interpreted to mean this.
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Parameters
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----------
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df_esm_sam_clean: pd.DataFrame
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A cleaned up dataframe of Stress Appraisal Measure items.
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Returns
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-------
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df_esm_event_time: pd.DataFrame
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The same dataframe with a new column event_time of datetime type.
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"""
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df_esm_event_time = df_esm_sam_clean[
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df_esm_sam_clean["questionnaire_id"] == QUESTIONNAIRE_ID_SAM.get("event_time")
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].assign(
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event_time=lambda x: pd.to_datetime(
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x.esm_user_answer, errors="coerce", infer_datetime_format=True, exact=True
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)
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)
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return df_esm_event_time
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def extract_event_duration(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
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"""
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If participants indicated a stressful events, they were asked:
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"How long did this event last? (Answer in hours and minutes)"
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This function extracts this duration time and saves additional answers:
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0 - I do not remember,
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1 - It is still going on.
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Parameters
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----------
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df_esm_sam_clean: pd.DataFrame
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A cleaned up dataframe of Stress Appraisal Measure items.
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Returns
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-------
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df_esm_event_duration: pd.DataFrame
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The same dataframe with two new columns:
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- event_duration, a time part of a datetime,
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- event_duration_info, giving other options to this question:
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0 - I do not remember,
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1 - It is still going on
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"""
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df_esm_event_duration = df_esm_sam_clean[
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df_esm_sam_clean["questionnaire_id"]
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== QUESTIONNAIRE_ID_SAM.get("event_duration")
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].assign(
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event_duration=lambda x: pd.to_datetime(
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x.esm_user_answer.str.slice(start=0, stop=-6), errors="coerce"
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).dt.time
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)
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# TODO Explore the values recorded in event_duration and possibly fix mistakes.
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# For example, participants reported setting 23:50:00 instead of 00:50:00.
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# For the events that no duration was found (i.e. event_duration = NaT),
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# we can determine whether:
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# - this event is still going on ("1 - It is still going on")
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# - the participant couldn't remember it's duration ("0 - I do not remember")
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# Generally, these answers were converted to esm_user_answer_numeric in clean_up_esm,
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# but only the numeric types of questions and answers.
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# Since this was of "datetime" type, convert these specific answers here again.
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df_esm_event_duration["event_duration_info"] = np.nan
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df_esm_event_duration[
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df_esm_event_duration.event_duration.isna()
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] = df_esm_event_duration[df_esm_event_duration.event_duration.isna()].assign(
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event_duration_info=lambda x: x.esm_user_answer.str.slice(stop=1).astype(int)
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)
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return df_esm_event_duration
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# TODO: How many questions about the stressfulness of the period were asked and how does this relate to events?
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import pandas as pd
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import numpy as np
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from modelling_utils import get_matching_col_names, get_norm_all_participants_scaler
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def preprocess_numerical_features(train_numerical_features, test_numerical_features, scaler, flag):
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# fillna with mean
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if flag == "train":
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numerical_features = train_numerical_features.fillna(train_numerical_features.mean())
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elif flag == "test":
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numerical_features = test_numerical_features.fillna(train_numerical_features.mean())
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else:
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raise ValueError("flag should be 'train' or 'test'")
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# normalize
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if scaler != "notnormalized":
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scaler = get_norm_all_participants_scaler(train_numerical_features, scaler)
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numerical_features = pd.DataFrame(scaler.transform(numerical_features), index=numerical_features.index, columns=numerical_features.columns)
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return numerical_features
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def preprocess_categorical_features(categorical_features, mode_categorical_features):
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# fillna with mode
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categorical_features = categorical_features.fillna(mode_categorical_features)
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# one-hot encoding
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categorical_features = categorical_features.apply(lambda col: col.astype("category"))
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if not categorical_features.empty:
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categorical_features = pd.get_dummies(categorical_features)
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return categorical_features
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def split_numerical_categorical_features(features, categorical_feature_colnames):
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numerical_features = features.drop(categorical_feature_colnames, axis=1)
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categorical_features = features[categorical_feature_colnames].copy()
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return numerical_features, categorical_features
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def preproces_Features(train_numerical_features, test_numerical_features, categorical_features, mode_categorical_features, scaler, flag):
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numerical_features = preprocess_numerical_features(train_numerical_features, test_numerical_features, scaler, flag)
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categorical_features = preprocess_categorical_features(categorical_features, mode_categorical_features)
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features = pd.concat([numerical_features, categorical_features], axis=1)
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return features
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##############################################################
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# Summary of the workflow
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# Step 1. Read parameters and data
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# Step 2. Nested cross validation
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# Step 3. Model evaluation
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# Step 4. Save results, parameters, and metrics to CSV files
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##############################################################
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# For reproducibility
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np.random.seed(0)
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# Step 1. Read parameters and data
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# Read parameters
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model = snakemake.params["model"]
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scaler = snakemake.params["scaler"]
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cv_method = snakemake.params["cv_method"]
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categorical_operators = snakemake.params["categorical_operators"]
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categorical_colnames_demographic_features = snakemake.params["categorical_demographic_features"]
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model_hyperparams = snakemake.params["model_hyperparams"][model]
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# Read data and split
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data = pd.read_csv(snakemake.input["data"])
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index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
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if "pid" in data.columns:
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index_columns.append("pid")
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data.set_index(index_columns, inplace=True)
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data_x, data_y = data.drop("target", axis=1), data[["target"]]
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if "pid" in index_columns:
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categorical_feature_colnames = categorical_colnames_demographic_features + get_matching_col_names(categorical_operators, data_x)
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else:
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categorical_feature_colnames = get_matching_col_names(categorical_operators, data_x)
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# Split train and test, numerical and categorical features
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train_x, test_x = data_x, data_x
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train_numerical_features, train_categorical_features = split_numerical_categorical_features(train_x, categorical_feature_colnames)
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train_y, test_y = data_y, data_y
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test_numerical_features, test_categorical_features = split_numerical_categorical_features(test_x, categorical_feature_colnames)
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# Preprocess: impute and normalize
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mode_categorical_features = train_categorical_features.mode().iloc[0]
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train_x = preproces_Features(train_numerical_features, None, train_categorical_features, mode_categorical_features, scaler, "train")
|
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||||||
test_x = preproces_Features(train_numerical_features, test_numerical_features, test_categorical_features, mode_categorical_features, scaler, "test")
|
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train_x, test_x = train_x.align(test_x, join="outer", axis=1, fill_value=0) # in case we get rid off categorical columns
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|
|
@ -1,27 +0,0 @@
|
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from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler
|
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||||||
|
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|
|
||||||
def get_matching_col_names(operators, features):
|
|
||||||
col_names = []
|
|
||||||
for col in features.columns:
|
|
||||||
if any(operator in col for operator in operators):
|
|
||||||
col_names.append(col)
|
|
||||||
return col_names
|
|
||||||
|
|
||||||
|
|
||||||
# normalize based on all participants: return fitted scaler
|
|
||||||
def get_norm_all_participants_scaler(features, scaler_flag):
|
|
||||||
# MinMaxScaler
|
|
||||||
if scaler_flag == "minmaxscaler":
|
|
||||||
scaler = MinMaxScaler()
|
|
||||||
# StandardScaler
|
|
||||||
elif scaler_flag == "standardscaler":
|
|
||||||
scaler = StandardScaler()
|
|
||||||
# RobustScaler
|
|
||||||
elif scaler_flag == "robustscaler":
|
|
||||||
scaler = RobustScaler()
|
|
||||||
else:
|
|
||||||
# throw exception
|
|
||||||
raise ValueError("The normalization method is not predefined, please check if the PARAMS_FOR_ANALYSIS.NORMALIZED in config.yaml file is correct.")
|
|
||||||
scaler.fit(features)
|
|
||||||
return scaler
|
|
||||||
Loading…
Reference in New Issue