Start machine learning pipeline example.

Select data and labels.

exploration/ex_ml_pipeline.py

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+# ---
+# 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
+
+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, 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 = proximity.recode_proximity(df_proximity)
+
+# %% [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"})
+)