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Add esm_SAM (only copied?).

models
junos 2022-08-19 14:02:54 +02:00
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src/features/phone_esm/straw/esm_SAM.py 100644
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import numpy as np
import pandas as pd
import features.esm
QUESTIONNAIRE_ID_SAM = {
"event_stress": 87,
"event_threat": 88,
"event_challenge": 89,
"event_time": 90,
"event_duration": 91,
"event_work_related": 92,
"period_stress": 93,
}
QUESTIONNAIRE_ID_SAM_LOW = min(QUESTIONNAIRE_ID_SAM.values())
QUESTIONNAIRE_ID_SAM_HIGH = max(QUESTIONNAIRE_ID_SAM.values())
GROUP_QUESTIONNAIRES_BY = [
"participant_id",
"device_id",
"esm_session",
]
# Each questionnaire occurs only once within each esm_session on the same device within the same participant.
def extract_stressful_events(df_esm: pd.DataFrame) -> pd.DataFrame:
# 0. Select only questions from Stress Appraisal Measure.
df_esm_preprocessed = features.esm.preprocess_esm(df_esm)
df_esm_sam = df_esm_preprocessed[
(df_esm_preprocessed["questionnaire_id"] >= QUESTIONNAIRE_ID_SAM_LOW)
& (df_esm_preprocessed["questionnaire_id"] <= QUESTIONNAIRE_ID_SAM_HIGH)
]
df_esm_sam_clean = features.esm.clean_up_esm(df_esm_sam)
# 1.
df_esm_event_threat_challenge_mean_wide = calculate_threat_challenge_means(
df_esm_sam_clean
)
# 2.
df_esm_event_stress = detect_stressful_event(df_esm_sam_clean)
# Join to the previously calculated features related to the events.
df_esm_events = df_esm_event_threat_challenge_mean_wide.join(
df_esm_event_stress[
GROUP_QUESTIONNAIRES_BY + ["event_present", "event_stressfulness"]
].set_index(GROUP_QUESTIONNAIRES_BY)
)
# 3.
df_esm_event_work_related = detect_event_work_related(df_esm_sam_clean)
df_esm_events = df_esm_events.join(
df_esm_event_work_related[
GROUP_QUESTIONNAIRES_BY + ["event_work_related"]
].set_index(GROUP_QUESTIONNAIRES_BY)
)
# 4.
df_esm_event_time = convert_event_time(df_esm_sam_clean)
df_esm_events = df_esm_events.join(
df_esm_event_time[GROUP_QUESTIONNAIRES_BY + ["event_time"]].set_index(
GROUP_QUESTIONNAIRES_BY
)
)
# 5.
df_esm_event_duration = extract_event_duration(df_esm_sam_clean)
df_esm_events = df_esm_events.join(
df_esm_event_duration[
GROUP_QUESTIONNAIRES_BY + ["event_duration", "event_duration_info"]
].set_index(GROUP_QUESTIONNAIRES_BY)
)
return df_esm_events
def calculate_threat_challenge_means(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
"""
This function calculates challenge and threat (two Stress Appraisal Measure subscales) means,
for each ESM session (within participants and devices).
It creates a grouped dataframe with means in two columns.
Parameters
----------
df_esm_sam_clean: pd.DataFrame
A cleaned up dataframe of Stress Appraisal Measure items.
Returns
-------
df_esm_event_threat_challenge_mean_wide: pd.DataFrame
A dataframe of unique ESM sessions (by participants and devices) with threat and challenge means.
"""
# Select only threat and challenge assessments for events
df_esm_event_threat_challenge = df_esm_sam_clean[
(
df_esm_sam_clean["questionnaire_id"]
== QUESTIONNAIRE_ID_SAM.get("event_threat")
)
| (
df_esm_sam_clean["questionnaire_id"]
== QUESTIONNAIRE_ID_SAM.get("event_challenge")
)
]
# Calculate mean of threat and challenge subscales for each ESM session.
df_esm_event_threat_challenge_mean_wide = pd.pivot_table(
df_esm_event_threat_challenge,
index=["participant_id", "device_id", "esm_session"],
columns=["questionnaire_id"],
values=["esm_user_answer_numeric"],
aggfunc="mean",
)
# Drop unnecessary column values.
df_esm_event_threat_challenge_mean_wide.columns = df_esm_event_threat_challenge_mean_wide.columns.get_level_values(
1
)
df_esm_event_threat_challenge_mean_wide.columns.name = None
df_esm_event_threat_challenge_mean_wide.rename(
columns={
QUESTIONNAIRE_ID_SAM.get("event_threat"): "threat_mean",
QUESTIONNAIRE_ID_SAM.get("event_challenge"): "challenge_mean",
},
inplace=True,
)
return df_esm_event_threat_challenge_mean_wide
def detect_stressful_event(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
"""
Participants were asked: "Was there a particular event that created tension in you?"
The following options were available:
0 - No,
1 - Yes, slightly,
2 - Yes, moderately,
3 - Yes, considerably,
4 - Yes, extremely.
This function indicates whether there was a stressful event (True/False)
and how stressful it was on a scale of 1 to 4.
Parameters
----------
df_esm_sam_clean: pd.DataFrame
A cleaned up dataframe of Stress Appraisal Measure items.
Returns
-------
df_esm_event_stress: pd.DataFrame
The same dataframe with two new columns:
- event_present, indicating whether there was a stressful event at all,
- event_stressfulness, a numeric answer (1-4) to the single item question.
"""
df_esm_event_stress = df_esm_sam_clean[
df_esm_sam_clean["questionnaire_id"] == QUESTIONNAIRE_ID_SAM.get("event_stress")
]
df_esm_event_stress = df_esm_event_stress.assign(
event_present=lambda x: x.esm_user_answer_numeric > 0,
event_stressfulness=lambda x: x.esm_user_answer_numeric,
)
return df_esm_event_stress
def detect_event_work_related(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
"""
This function simply adds a column indicating the answer to the question:
"Was/is this event work-related?"
Parameters
----------
df_esm_sam_clean: pd.DataFrame
A cleaned up dataframe of Stress Appraisal Measure items.
Returns
-------
df_esm_event_stress: pd.DataFrame
The same dataframe with a new column event_work_related (True/False).
"""
df_esm_event_stress = df_esm_sam_clean[
df_esm_sam_clean["questionnaire_id"]
== QUESTIONNAIRE_ID_SAM.get("event_work_related")
]
df_esm_event_stress = df_esm_event_stress.assign(
event_work_related=lambda x: x.esm_user_answer_numeric > 0
)
return df_esm_event_stress
def convert_event_time(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
"""
This function only serves to convert the string datetime answer into a real datetime type.
Errors during this conversion are coerced, meaning that non-datetime answers are assigned Not a Time (NaT).
NOTE: Since the only available non-datetime answer to this question was "0 - I do not remember",
the NaTs can be interpreted to mean this.
Parameters
----------
df_esm_sam_clean: pd.DataFrame
A cleaned up dataframe of Stress Appraisal Measure items.
Returns
-------
df_esm_event_time: pd.DataFrame
The same dataframe with a new column event_time of datetime type.
"""
df_esm_event_time = df_esm_sam_clean[
df_esm_sam_clean["questionnaire_id"] == QUESTIONNAIRE_ID_SAM.get("event_time")
].assign(
event_time=lambda x: pd.to_datetime(
x.esm_user_answer, errors="coerce", infer_datetime_format=True, exact=True
)
)
return df_esm_event_time
def extract_event_duration(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
"""
If participants indicated a stressful events, they were asked:
"How long did this event last? (Answer in hours and minutes)"
This function extracts this duration time and saves additional answers:
0 - I do not remember,
1 - It is still going on.
Parameters
----------
df_esm_sam_clean: pd.DataFrame
A cleaned up dataframe of Stress Appraisal Measure items.
Returns
-------
df_esm_event_duration: pd.DataFrame
The same dataframe with two new columns:
- event_duration, a time part of a datetime,
- event_duration_info, giving other options to this question:
0 - I do not remember,
1 - It is still going on
"""
df_esm_event_duration = df_esm_sam_clean[
df_esm_sam_clean["questionnaire_id"]
== QUESTIONNAIRE_ID_SAM.get("event_duration")
].assign(
event_duration=lambda x: pd.to_datetime(
x.esm_user_answer.str.slice(start=0, stop=-6), errors="coerce"
).dt.time
)
# TODO Explore the values recorded in event_duration and possibly fix mistakes.
# For example, participants reported setting 23:50:00 instead of 00:50:00.
# For the events that no duration was found (i.e. event_duration = NaT),
# we can determine whether:
# - this event is still going on ("1 - It is still going on")
# - the participant couldn't remember it's duration ("0 - I do not remember")
# Generally, these answers were converted to esm_user_answer_numeric in clean_up_esm,
# but only the numeric types of questions and answers.
# Since this was of "datetime" type, convert these specific answers here again.
df_esm_event_duration["event_duration_info"] = np.nan
df_esm_event_duration[
df_esm_event_duration.event_duration.isna()
] = df_esm_event_duration[df_esm_event_duration.event_duration.isna()].assign(
event_duration_info=lambda x: x.esm_user_answer.str.slice(stop=1).astype(int)
)
return df_esm_event_duration
# TODO: How many questions about the stressfulness of the period were asked and how does this relate to events?