[WIP] Prepare a function to classify adherence and illustrate steps in Jupyter Notebook.

communication
junos 2021-06-07 19:32:38 +02:00
parent 224dedaced
commit d5cd76f05a
2 changed files with 186 additions and 14 deletions

View File

@ -32,7 +32,9 @@ from features.esm import *
# Only take data from the main part of the study. The pilot data have different structure, there were especially many additions to ESM_JSON.
# %%
participants_inactive_usernames = participants.query_db.get_usernames(collection_start=datetime.date.fromisoformat("2020-08-01"))
participants_inactive_usernames = participants.query_db.get_usernames(
collection_start=datetime.date.fromisoformat("2020-08-01")
)
df_esm_inactive = get_esm_data(participants_inactive_usernames)
# %%
@ -59,7 +61,9 @@ df_esm_preprocessed.columns
# One approach would be to count distinct session IDs which are incremented for each group of EMAs. However, since not every question answered counts as a fulfilled EMA, some unique session IDs should be eliminated first.
# %%
session_counts = df_esm_preprocessed.groupby(["participant_id","esm_session"]).count()["id"]
session_counts = df_esm_preprocessed.groupby(["participant_id", "esm_session"]).count()[
"id"
]
# %% [markdown]
# Group data by participant_id and esm_session and count the number of instances (by id). Session counts are therefore counts of how many times a specific session ID appears *within* a specific participant.
@ -73,9 +77,13 @@ sns.displot(session_counts.to_numpy(), binwidth=1, height=8)
# ### Unique session IDs
# %%
df_session_counts = pd.DataFrame(session_counts).rename(columns={"id": "esm_session_count"})
df_session_counts = pd.DataFrame(session_counts).rename(
columns={"id": "esm_session_count"}
)
df_session_1 = df_session_counts[(df_session_counts["esm_session_count"] == 1)]
df_esm_unique_session = df_session_1.join(df_esm_preprocessed.set_index(["participant_id","esm_session"]))
df_esm_unique_session = df_session_1.join(
df_esm_preprocessed.set_index(["participant_id", "esm_session"])
)
# %%
df_esm_unique_session["esm_user_answer"].value_counts()
@ -85,10 +93,14 @@ df_esm_unique_session["esm_user_answer"].value_counts()
# What do the answers "Ne" represent?
# %%
df_esm_unique_session.query("esm_user_answer == 'Ne'")[[ "esm_trigger", "esm_instructions", "esm_user_answer"]].head()
df_esm_unique_session.query("esm_user_answer == 'Ne'")[
["esm_trigger", "esm_instructions", "esm_user_answer"]
].head()
# %%
df_esm_unique_session.loc[df_esm_unique_session["esm_user_answer"].str.contains("Ne"), "esm_trigger"].value_counts()
df_esm_unique_session.loc[
df_esm_unique_session["esm_user_answer"].str.contains("Ne"), "esm_trigger"
].value_counts()
# %% [markdown]
# These are all "first" questions of EMAs which serve as a way to postpone the daytime or evening EMAs.
@ -103,22 +115,31 @@ df_esm_unique_session.loc[df_esm_unique_session["esm_user_answer"].str.contains(
# There are some session IDs that only appear twice or three times.
# %%
df_session_counts[(df_session_counts["esm_session_count"] < 4) & (df_session_counts["esm_session_count"] > 1)]
df_session_counts[
(df_session_counts["esm_session_count"] < 4)
& (df_session_counts["esm_session_count"] > 1)
]
# %% [markdown]
# Some represent the morning EMAs that only contained three questions.
# %%
df_esm_preprocessed.query("participant_id == 89 & esm_session == 158")[[ "esm_trigger", "esm_instructions", "esm_user_answer"]]
df_esm_preprocessed.query("participant_id == 89 & esm_session == 158")[
["esm_trigger", "esm_instructions", "esm_user_answer"]
]
# %%
df_esm_preprocessed.query("participant_id == 89 & esm_session == 157")[[ "esm_trigger", "esm_instructions", "esm_user_answer"]]
df_esm_preprocessed.query("participant_id == 89 & esm_session == 157")[
["esm_trigger", "esm_instructions", "esm_user_answer"]
]
# %% [markdown]
# Others represent interrupted EMA sessions.
# %%
df_esm_preprocessed.query("participant_id == 31 & esm_session == 77")[[ "esm_trigger", "esm_instructions", "esm_user_answer"]]
df_esm_preprocessed.query("participant_id == 31 & esm_session == 77")[
["esm_trigger", "esm_instructions", "esm_user_answer"]
]
# %% [markdown]
# ### Long sessions
@ -127,7 +148,9 @@ df_esm_preprocessed.query("participant_id == 31 & esm_session == 77")[[ "esm_tri
df_session_counts[(df_session_counts["esm_session_count"] > 40)]
# %%
df_esm_preprocessed.query("participant_id == 83").sort_values("_id")[[ "esm_trigger","datetime_lj", "_id", "username", "device_id"]]
df_esm_preprocessed.query("participant_id == 83").sort_values("_id")[
["esm_trigger", "datetime_lj", "_id", "username", "device_id"]
]
# %% [markdown]
# Both, session ID and \_ID (and others) reset on application reinstall. Here, it can be seen that the application was reinstalled on 2 April (actually, the phone was replaced as reported by the participant).
@ -135,11 +158,108 @@ df_esm_preprocessed.query("participant_id == 83").sort_values("_id")[[ "esm_trig
# Session IDs should therefore be grouped while taking the device ID into account.
# %%
session_counts_device = df_esm_preprocessed.groupby(["participant_id", "device_id", "esm_session"]).count()["id"]
session_counts_device = df_esm_preprocessed.groupby(
["participant_id", "device_id", "esm_session"]
).count()["id"]
sns.displot(session_counts_device.to_numpy(), binwidth=1, height=8)
# %% [markdown]
# ## Other possibilities
# %% [markdown]
# There are also answers that describe what happened to a pending question: "Removed%"
# Prepare a dataframe with session response as determined from other indices.
# %%
import numpy as np
df_session_counts = pd.DataFrame(session_counts_device).rename(
columns={"id": "esm_session_count"}
)
df_session_counts["session_response"] = np.NaN
session_group_by = df_esm_preprocessed.groupby(
["participant_id", "device_id", "esm_session"]
)
df_session_counts.count()
# %% [markdown]
# ### ESM statuses
# %% [markdown]
# The status of the ESM can be: 0-new, 1-dismissed, 2-answered, 3-expired, 4-visible, or 5-branched.
#
# Which statuses appear in the data?
# %%
df_esm_preprocessed["esm_status"].value_counts()
# %% [markdown]
# Most of the ESMs were answered (2). We can group all others as unanswered.
# %%
contains_status_not_2 = session_group_by.apply(lambda x: (x.esm_status != 2).any())
df_session_counts.loc[contains_status_not_2, "session_response"] = "esm_unanswered"
# %%
df_session_counts.count()
# %% [markdown]
# ### Day finished or off
# %%
non_session = session_group_by.apply(
lambda x: (
(x.esm_user_answer == "DayFinished3421") | (x.esm_user_answer == "DayOff3421")
).any()
)
df_session_counts.loc[non_session, "session_response"] = "day_finished"
# %%
df_session_counts.count()
# %% [markdown]
# ### Removed
# %% [markdown]
# There are also answers that explicitly describe what happened to a pending question that start with "Removed%".
# %%
esm_removed = session_group_by.apply(
lambda x: (x.esm_user_answer.str.contains("Removed")).any()
)
# %%
df_session_counts.loc[esm_removed]
# %%
df_session_counts.loc[esm_removed, "session_response"].value_counts()
# %% [markdown]
# It turns out that these had been accounted for with ESM statuses.
# %% [markdown]
# ### Evening_last
# %% [markdown]
# When the evening EMA session comes to an end, the trigger should reflect this, that is, it should say `evening_last`.
# %%
finished_sessions = session_group_by.apply(
lambda x: (x.esm_trigger.str.endswith("_last")).any()
)
df_session_counts.loc[finished_sessions, "session_response"] = "esm_finished"
# %%
df_session_counts.count()
# %%
df_esm_preprocessed["esm_trigger"].value_counts()
# %%
sns.displot(
df_session_counts[df_session_counts.session_response.isna()],
x="esm_session_count",
binwidth=1,
height=8,
)
# %%

View File

@ -1,6 +1,7 @@
import datetime
from collections.abc import Collection
import numpy as np
import pandas as pd
from pytz import timezone
@ -55,3 +56,54 @@ def preprocess_esm(df_esm: pd.DataFrame) -> pd.DataFrame:
columns=["esm_trigger"]
) # The esm_trigger column is already present in the main df.
return df_esm.join(df_esm_json)
def classify_sessions_adherence(df_esm_preprocessed: pd.DataFrame) -> pd.DataFrame:
"""
For each distinct EMA session, determine how the participant responded to it.
Possible outcomes are: esm_unanswered
This is done in several steps.
#TODO Finish the documentation.
Parameters
----------
df_esm_preprocessed: pd.DataFrame
A preprocessed dataframe of esm data, which must include the session ID (esm_session).
Returns
-------
some dataframe
"""
sessions_grouped = df_esm_preprocessed.groupby(
["participant_id", "device_id", "esm_session"]
)
df_session_counts = pd.DataFrame(sessions_grouped.count()["id"]).rename(
columns={"id": "esm_session_count"}
)
df_session_counts["session_response"] = np.NaN
esm_not_answered = sessions_grouped.apply(lambda x: (x.esm_status != 2).any())
df_session_counts.loc[esm_not_answered, "session_response"] = "esm_unanswered"
non_session = sessions_grouped.apply(
lambda x: (
(x.esm_user_answer == "DayFinished3421")
| (x.esm_user_answer == "DayOff3421")
).any()
)
df_session_counts.loc[non_session, "session_response"] = "day_finished"
finished_sessions = sessions_grouped.apply(
lambda x: (x.esm_trigger.str.endswith("_last")).any()
)
df_session_counts.loc[finished_sessions, "session_response"] = "esm_finished"
# TODO Look at evening-evening_last sequence, if everything is caught with finished sessions
# TODO What can be done about morning EMA, perhaps morning-morning_first (sic!) sequence?
# TODO What can be done about workday EMA.
return sessions_grouped.count()