151 lines
3.3 KiB
Python
151 lines
3.3 KiB
Python
# ---
|
|
# jupyter:
|
|
# jupytext:
|
|
# formats: ipynb,py:percent
|
|
# text_representation:
|
|
# extension: .py
|
|
# format_name: percent
|
|
# format_version: '1.3'
|
|
# jupytext_version: 1.11.4
|
|
# kernelspec:
|
|
# display_name: straw2analysis
|
|
# language: python
|
|
# name: straw2analysis
|
|
# ---
|
|
|
|
# %%
|
|
# %matplotlib inline
|
|
import datetime
|
|
import os
|
|
import sys
|
|
|
|
import seaborn as sns
|
|
from sklearn import linear_model
|
|
from sklearn.model_selection import LeaveOneGroupOut, cross_val_score
|
|
|
|
nb_dir = os.path.split(os.getcwd())[0]
|
|
if nb_dir not in sys.path:
|
|
sys.path.append(nb_dir)
|
|
|
|
# %%
|
|
import participants.query_db
|
|
from features import esm, helper, proximity
|
|
|
|
# %% [markdown]
|
|
# # 1. Get the relevant data
|
|
|
|
# %%
|
|
participants_inactive_usernames = participants.query_db.get_usernames(
|
|
collection_start=datetime.date.fromisoformat("2020-08-01")
|
|
)
|
|
# Consider only two participants to simplify.
|
|
ptcp_2 = participants_inactive_usernames[0:2]
|
|
|
|
# %% [markdown]
|
|
# ## 1.1 Labels
|
|
|
|
# %%
|
|
df_esm = esm.get_esm_data(ptcp_2)
|
|
df_esm_preprocessed = esm.preprocess_esm(df_esm)
|
|
|
|
# %%
|
|
df_esm_PANAS = df_esm_preprocessed[
|
|
(df_esm_preprocessed["questionnaire_id"] == 8)
|
|
| (df_esm_preprocessed["questionnaire_id"] == 9)
|
|
]
|
|
df_esm_PANAS_clean = esm.clean_up_esm(df_esm_PANAS)
|
|
|
|
# %% [markdown]
|
|
# ## 1.2 Sensor data
|
|
|
|
# %%
|
|
df_proximity = proximity.get_proximity_data(ptcp_2)
|
|
df_proximity = helper.get_date_from_timestamp(df_proximity)
|
|
df_proximity = proximity.recode_proximity(df_proximity)
|
|
|
|
# %% [markdown]
|
|
# ## 1.3 Standardization/personalization
|
|
|
|
# %% [markdown]
|
|
# # 2. Grouping/segmentation
|
|
|
|
# %%
|
|
df_esm_PANAS_daily_means = (
|
|
df_esm_PANAS_clean.groupby(["participant_id", "date_lj", "questionnaire_id"])
|
|
.esm_user_answer_numeric.agg("mean")
|
|
.reset_index()
|
|
.rename(columns={"esm_user_answer_numeric": "esm_numeric_mean"})
|
|
)
|
|
|
|
# %%
|
|
df_esm_PANAS_daily_means = (
|
|
df_esm_PANAS_daily_means.pivot(
|
|
index=["participant_id", "date_lj"],
|
|
columns="questionnaire_id",
|
|
values="esm_numeric_mean",
|
|
)
|
|
.reset_index(col_level=1)
|
|
.rename(columns={8.0: "PA", 9.0: "NA"})
|
|
.set_index(["participant_id", "date_lj"])
|
|
)
|
|
|
|
|
|
# %%
|
|
df_proximity_daily_counts = proximity.count_proximity(
|
|
df_proximity, ["participant_id", "date_lj"]
|
|
)
|
|
|
|
# %%
|
|
df_proximity_daily_counts
|
|
|
|
# %% [markdown]
|
|
# # 3. Join features (and export to csv?)
|
|
|
|
# %%
|
|
df_full_data_daily_means = df_esm_PANAS_daily_means.join(
|
|
df_proximity_daily_counts
|
|
).reset_index()
|
|
|
|
# %% [markdown]
|
|
# # 4. Machine learning model and parameters
|
|
|
|
# %%
|
|
lin_reg_proximity = linear_model.LinearRegression()
|
|
|
|
# %% [markdown]
|
|
# ## 4.1 Validation method
|
|
|
|
# %%
|
|
logo = LeaveOneGroupOut()
|
|
logo.get_n_splits(
|
|
df_full_data_daily_means[["freq_prox_near", "prop_prox_near"]],
|
|
df_full_data_daily_means["PA"],
|
|
groups=df_full_data_daily_means["participant_id"],
|
|
)
|
|
|
|
# %% [markdown]
|
|
# ## 4.2 Fit results (export?)
|
|
|
|
# %%
|
|
cross_val_score(
|
|
lin_reg_proximity,
|
|
df_full_data_daily_means[["freq_prox_near", "prop_prox_near"]],
|
|
df_full_data_daily_means["PA"],
|
|
groups=df_full_data_daily_means["participant_id"],
|
|
cv=logo,
|
|
n_jobs=-1,
|
|
scoring="r2",
|
|
)
|
|
|
|
# %%
|
|
lin_reg_proximity.fit(
|
|
df_full_data_daily_means[["freq_prox_near", "prop_prox_near"]],
|
|
df_full_data_daily_means["PA"],
|
|
)
|
|
|
|
# %%
|
|
lin_reg_proximity.score(
|
|
df_full_data_daily_means[["freq_prox_near", "prop_prox_near"]],
|
|
df_full_data_daily_means["PA"],
|
|
)
|