Save calculated features to csv files.

Add more prints.

config/environment.yml

@@ -15,6 +15,7 @@ dependencies:
   - psycopg2
   - python-dotenv
   - pytz
+  - pyprojroot
   - pyyaml
   - seaborn
   - scikit-learn

exploration/ex_ml_pipeline.py

@@ -16,6 +16,7 @@
 # %%
 # %matplotlib inline
 import datetime
+import importlib
 import os
 import sys
 
@@ -156,14 +157,25 @@ lin_reg_proximity.score(
 # %%
 from machine_learning import pipeline
 
+# %%
+importlib.reload(pipeline)
+
 # %%
 with open("../machine_learning/config/minimal_features.yaml", "r") as file:
     sensor_features_params = yaml.safe_load(file)
+print(sensor_features_params)
 
 # %%
 sensor_features = pipeline.SensorFeatures(**sensor_features_params)
 sensor_features.data_types
 
+# %%
+sensor_features.set_participants_label("nokia_0000003")
+
+# %%
+sensor_features.data_types = ["proximity", "communication"]
+sensor_features.participants_usernames = ptcp_2
+
 # %%
 sensor_features.get_sensor_data("proximity")
 
@@ -179,12 +191,19 @@ sensor_features.calculate_features()
 # %%
 sensor_features.get_features("proximity", "all")
 
+# %%
+sensor_features.get_features("communication", "all")
+
+# %%
+sensor_features.get_features("all", "all")
+
 # %%
 with open("../machine_learning/config/minimal_labels.yaml", "r") as file:
     labels_params = yaml.safe_load(file)
 
 # %%
 labels = pipeline.Labels(**labels_params)
+labels.participants_usernames = ptcp_2
 labels.questionnaires
 
 # %%
@@ -194,3 +213,25 @@ labels.set_labels()
 labels.get_labels("PANAS")
 
 # %%
+labels.aggregate_labels()
+
+# %%
+labels.get_aggregated_labels()
+
+# %%
+model_validation = pipeline.ModelValidation(
+    sensor_features.get_features("all", "all"),
+    labels.get_aggregated_labels(),
+    group_variable="participant_id",
+    cv_name="loso",
+)
+model_validation.model = linear_model.LinearRegression()
+model_validation.set_cv_method()
+
+# %%
+model_validation.cross_validate()
+
+# %%
+model_validation.groups
+
+# %%

exploration/expl_communication.py

@@ -13,14 +13,15 @@
 #     name: straw2analysis
 # ---
 
+# %%
+import importlib
+
 # %%
 # %matplotlib inline
 import os
 import sys
 
 import matplotlib.pyplot as plt
-
-# %%
 import seaborn as sns
 
 nb_dir = os.path.split(os.getcwd())[0]
@@ -28,21 +29,29 @@ if nb_dir not in sys.path:
     sys.path.append(nb_dir)
 
 # %%
-from features.communication import *
+from features import communication, helper
+
+# %%
+importlib.reload(communication)
 
 # %% [markdown]
 # # Example of communication data and feature calculation
 
 # %%
-df_calls = get_call_data(["nokia_0000003"])
+df_calls = communication.get_call_data(["nokia_0000003"])
 print(df_calls)
 
 # %%
-count_comms(df_calls)
+df_calls = helper.get_date_from_timestamp(df_calls)
+communication.count_comms(df_calls, ["date_lj"])
 
 # %%
-df_sms = get_sms_data(["nokia_0000003"])
-count_comms(df_sms)
+df_sms = communication.get_sms_data(["nokia_0000003"])
+df_sms = helper.get_date_from_timestamp(df_sms)
+communication.count_comms(df_sms, ["date_lj"])
+
+# %%
+communication.calls_sms_features(df_calls, df_sms, ["date_lj"])
 
 # %% [markdown]
 # # Call data

exploration/expl_proximity.py

@@ -16,6 +16,7 @@
 # %%
 # %matplotlib inline
 import datetime
+import importlib
 import os
 import sys
 
@@ -32,13 +33,16 @@ import participants.query_db
 TZ_LJ = timezone("Europe/Ljubljana")
 
 # %%
-from features.proximity import *
+from features import helper, proximity
+
+# %%
+importlib.reload(proximity)
 
 # %% [markdown]
 # # Basic characteristics
 
 # %%
-df_proximity_nokia = get_proximity_data(["nokia_0000003"])
+df_proximity_nokia = proximity.get_proximity_data(["nokia_0000003"])
 print(df_proximity_nokia)
 
 # %%
@@ -53,7 +57,7 @@ df_proximity_nokia.double_proximity.value_counts()
 
 # %%
 participants_inactive_usernames = participants.query_db.get_usernames()
-df_proximity_inactive = get_proximity_data(participants_inactive_usernames)
+df_proximity_inactive = proximity.get_proximity_data(participants_inactive_usernames)
 
 # %%
 df_proximity_inactive.double_proximity.describe()
@@ -110,3 +114,13 @@ df_proximity_combinations[
     (df_proximity_combinations[5.0] != 0)
     & (df_proximity_combinations[5.00030517578125] != 0)
 ]
+
+# %% [markdown]
+# # Features
+
+# %%
+df_proximity_inactive = helper.get_date_from_timestamp(df_proximity_inactive)
+
+# %%
+df_proximity_features = proximity.count_proximity(df_proximity_inactive, ["date_lj"])
+display(df_proximity_features)

features/communication.py

@@ -8,14 +8,21 @@ from setup import db_engine, session
 call_types = {1: "incoming", 2: "outgoing", 3: "missed"}
 sms_types = {1: "received", 2: "sent"}
 
-FEATURES_CALLS = (
-    ["no_calls_all"]
-    + ["no_" + call_type for call_type in call_types.values()]
-    + ["duration_total_" + call_types.get(1), "duration_total_" + call_types.get(2)]
-    + ["duration_max_" + call_types.get(1), "duration_max_" + call_types.get(2)]
-    + ["no_" + call_types.get(1) + "_ratio", "no_" + call_types.get(2) + "_ratio"]
-    + ["no_contacts_calls"]
-)
+FILL_NA_CALLS = {
+    "no_calls_all": 0,
+    "no_" + call_types.get(1): 0,
+    "no_" + call_types.get(2): 0,
+    "no_" + call_types.get(3): 0,
+    "duration_total_" + call_types.get(1): 0,
+    "duration_total_" + call_types.get(2): 0,
+    "duration_max_" + call_types.get(1): 0,
+    "duration_max_" + call_types.get(2): 0,
+    "no_" + call_types.get(1) + "_ratio": 1 / 3,  # Three different types
+    "no_" + call_types.get(2) + "_ratio": 1 / 3,
+    "no_contacts_calls": 0,
+}
+
+FEATURES_CALLS = list(FILL_NA_CALLS.keys())
 
 # FEATURES_CALLS =
 # ["no_calls_all",
@@ -23,19 +30,24 @@ FEATURES_CALLS = (
 # "duration_total_incoming", "duration_total_outgoing",
 # "duration_max_incoming", "duration_max_outgoing",
 # "no_incoming_ratio", "no_outgoing_ratio",
-# "no_contacts"]
+# "no_contacts_calls"]
+
+FILL_NA_SMS = {
+    "no_sms_all": 0,
+    "no_" + sms_types.get(1): 0,
+    "no_" + sms_types.get(2): 0,
+    "no_" + sms_types.get(1) + "_ratio": 1 / 2,  # Two different types
+    "no_" + sms_types.get(2) + "_ratio": 1 / 2,
+    "no_contacts_sms": 0,
+}
+
+FEATURES_SMS = list(FILL_NA_SMS.keys())
 
-FEATURES_SMS = (
-    ["no_sms_all"]
-    + ["no_" + sms_type for sms_type in sms_types.values()]
-    + ["no_" + sms_types.get(1) + "_ratio", "no_" + sms_types.get(2) + "_ratio"]
-    + ["no_contacts_sms"]
-)
 # FEATURES_SMS =
 # ["no_sms_all",
 #  "no_received", "no_sent",
 #  "no_received_ratio", "no_sent_ratio",
-#  "no_contacts"]
+#  "no_contacts_sms"]
 
 FEATURES_CALLS_SMS_PROP = [
     "proportion_calls_all",
@@ -45,8 +57,15 @@ FEATURES_CALLS_SMS_PROP = [
     "proportion_calls_missed_sms_received",
 ]
 
+FILL_NA_CALLS_SMS_PROP = {
+    key: 1 / 2 for key in FEATURES_CALLS_SMS_PROP
+}  # All of the form of a / (a + b).
+
 FEATURES_CALLS_SMS_ALL = FEATURES_CALLS + FEATURES_SMS + FEATURES_CALLS_SMS_PROP
 
+FILL_NA_CALLS_SMS_ALL = FILL_NA_CALLS | FILL_NA_SMS | FILL_NA_CALLS_SMS_PROP
+# As per PEP-584 a union for dicts was implemented in Python 3.9.0.
+
 
 def get_call_data(usernames: Collection) -> pd.DataFrame:
     """
@@ -137,7 +156,7 @@ def enumerate_contacts(comm_df: pd.DataFrame) -> pd.DataFrame:
     return comm_df
 
 
-def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
+def count_comms(comm_df: pd.DataFrame, group_by=None) -> pd.DataFrame:
     """
     Calculate frequencies (and duration) of messages (or calls), grouped by their types.
 
@@ -145,6 +164,9 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
     ----------
     comm_df: pd.DataFrame
         A dataframe of calls or SMSes.
+    group_by: list
+        A list of strings, specifying by which parameters to group.
+        By default, the features are calculated per participant, but could be "date_lj" etc.
 
     Returns
     -------
@@ -157,11 +179,13 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
         * the number of messages by type (received, sent), and
         * the number of communication contacts by type. 
     """
+    if group_by is None:
+        group_by = []
     if "call_type" in comm_df:
         data_type = "calls"
         comm_counts = (
-            comm_df.value_counts(subset=["participant_id", "call_type"])
-            .unstack()
+            comm_df.value_counts(subset=group_by + ["participant_id", "call_type"])
+            .unstack(level="call_type", fill_value=0)
             .rename(columns=call_types)
             .add_prefix("no_")
         )
@@ -174,17 +198,17 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
         )
         # Ratio of incoming and outgoing calls to all calls.
         comm_duration_total = (
-            comm_df.groupby(["participant_id", "call_type"])
+            comm_df.groupby(group_by + ["participant_id", "call_type"])
             .sum()["call_duration"]
-            .unstack()
+            .unstack(level="call_type", fill_value=0)
             .rename(columns=call_types)
             .add_prefix("duration_total_")
         )
         # Total call duration by type.
         comm_duration_max = (
-            comm_df.groupby(["participant_id", "call_type"])
+            comm_df.groupby(group_by + ["participant_id", "call_type"])
             .max()["call_duration"]
-            .unstack()
+            .unstack(level="call_type", fill_value=0)
             .rename(columns=call_types)
             .add_prefix("duration_max_")
         )
@@ -202,8 +226,8 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
     elif "message_type" in comm_df:
         data_type = "sms"
         comm_counts = (
-            comm_df.value_counts(subset=["participant_id", "message_type"])
-            .unstack()
+            comm_df.value_counts(subset=group_by + ["participant_id", "message_type"])
+            .unstack(level="message_type", fill_value=0)
             .rename(columns=sms_types)
             .add_prefix("no_")
         )
@@ -218,7 +242,7 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
         raise KeyError("The dataframe contains neither call_type or message_type")
     comm_contacts_counts = (
         enumerate_contacts(comm_df)
-        .groupby(["participant_id"])
+        .groupby(group_by + ["participant_id"])
         .nunique()["contact_id"]
         .rename("no_contacts_" + data_type)
     )
@@ -270,7 +294,9 @@ def contact_features(comm_df: pd.DataFrame) -> pd.DataFrame:
     return contacts_count
 
 
-def calls_sms_features(df_calls: pd.DataFrame, df_sms: pd.DataFrame) -> pd.DataFrame:
+def calls_sms_features(
+    df_calls: pd.DataFrame, df_sms: pd.DataFrame, group_by=None
+) -> pd.DataFrame:
     """
     Calculates additional features relating calls and sms data.
 
@@ -280,6 +306,9 @@ def calls_sms_features(df_calls: pd.DataFrame, df_sms: pd.DataFrame) -> pd.DataF
         A dataframe of calls (return of get_call_data).
     df_sms: pd.DataFrame
         A dataframe of SMSes (return of get_sms_data).
+    group_by: list
+        A list of strings, specifying by which parameters to group.
+        By default, the features are calculated per participant, but could be "date_lj" etc.
 
     Returns
     -------
@@ -297,24 +326,38 @@ def calls_sms_features(df_calls: pd.DataFrame, df_sms: pd.DataFrame) -> pd.DataF
         * proportion_calls_contacts:
             proportion of calls contacts in total number of communication contacts
     """
-    count_calls = count_comms(df_calls)
-    count_sms = count_comms(df_sms)
-    count_joined = count_calls.join(count_sms).assign(
-        proportion_calls_all=(
-            lambda x: x.no_calls_all / (x.no_calls_all + x.no_sms_all)
-        ),
-        proportion_calls_incoming=(
-            lambda x: x.no_incoming / (x.no_incoming + x.no_received)
-        ),
-        proportion_calls_missed_sms_received=(
-            lambda x: x.no_missed / (x.no_missed + x.no_received)
-        ),
-        proportion_calls_outgoing=(
-            lambda x: x.no_outgoing / (x.no_outgoing + x.no_sent)
-        ),
-        proportion_calls_contacts=(
-            lambda x: x.no_contacts_calls / (x.no_contacts_calls + x.no_contacts_sms)
+    if group_by is None:
+        group_by = []
+    count_calls = count_comms(df_calls, group_by)
+    count_sms = count_comms(df_sms, group_by)
+    count_joined = (
+        count_calls.merge(
+            count_sms,
+            how="outer",
+            left_index=True,
+            right_index=True,
+            validate="one_to_one",
         )
-        # Calculate new features and create additional columns
+        .fillna(0, downcast="infer")
+        .assign(
+            proportion_calls_all=(
+                lambda x: x.no_calls_all / (x.no_calls_all + x.no_sms_all)
+            ),
+            proportion_calls_incoming=(
+                lambda x: x.no_incoming / (x.no_incoming + x.no_received)
+            ),
+            proportion_calls_missed_sms_received=(
+                lambda x: x.no_missed / (x.no_missed + x.no_received)
+            ),
+            proportion_calls_outgoing=(
+                lambda x: x.no_outgoing / (x.no_outgoing + x.no_sent)
+            ),
+            proportion_calls_contacts=(
+                lambda x: x.no_contacts_calls
+                / (x.no_contacts_calls + x.no_contacts_sms)
+            )
+            # Calculate new features and create additional columns
+        )
+        .fillna(0.5, downcast="infer")
     )
     return count_joined

features/proximity.py

@@ -5,7 +5,12 @@ import pandas as pd
 from config.models import Participant, Proximity
 from setup import db_engine, session
 
-FEATURES_PROXIMITY = ["freq_prox_near", "prop_prox_near"]
+FILL_NA_PROXIMITY = {
+    "freq_prox_near": 0,
+    "prop_prox_near": 1 / 2,  # Of the form of a / (a + b).
+}
+
+FEATURES_PROXIMITY = list(FILL_NA_PROXIMITY.keys())
 
 
 def get_proximity_data(usernames: Collection) -> pd.DataFrame:
@@ -78,11 +83,11 @@ def count_proximity(
         A dataframe with the count of "near" proximity values and their relative count.
     """
     if group_by is None:
-        group_by = ["participant_id"]
+        group_by = []
     if "bool_prox_near" not in df_proximity:
         df_proximity = recode_proximity(df_proximity)
     df_proximity["bool_prox_far"] = ~df_proximity["bool_prox_near"]
-    df_proximity_features = df_proximity.groupby(group_by).sum()[
+    df_proximity_features = df_proximity.groupby(["participant_id"] + group_by).sum()[
         ["bool_prox_near", "bool_prox_far"]
     ]
     df_proximity_features = df_proximity_features.assign(

machine_learning/config/minimal_labels.yaml

@@ -1,4 +1,4 @@
-grouping_variable: date_lj
+grouping_variable: [date_lj]
 labels:
   PANAS:
     - PA

machine_learning/config/prox_comm_PANAS_features.yaml

@@ -0,0 +1,6 @@
+grouping_variable: [date_lj]
+features:
+  proximity:
+    all
+  communication:
+    all

machine_learning/config/prox_comm_PANAS_labels.yaml

@@ -0,0 +1,5 @@
+grouping_variable: [date_lj]
+labels:
+  PANAS:
+    - PA
+    - NA

machine_learning/pipeline.py

@@ -1,13 +1,25 @@
 import datetime
+import warnings
 from collections.abc import Collection
+from pathlib import Path
 
+import numpy as np
 import pandas as pd
-from sklearn.model_selection import cross_val_score
+import yaml
+from pyprojroot import here
+from sklearn import linear_model
+from sklearn.model_selection import LeaveOneGroupOut, cross_val_score
 
 import participants.query_db
 from features import communication, esm, helper, proximity
 from machine_learning import QUESTIONNAIRE_IDS, QUESTIONNAIRE_IDS_RENAME
 
+WARNING_PARTICIPANTS_LABEL = (
+    "Before calculating features, please set participants label using self.set_participants_label() "
+    "to be used as a filename prefix when exporting data. "
+    "The filename will be of the form: %participants_label_%grouping_variable_%data_type.csv"
+)
+
 
 class SensorFeatures:
     def __init__(
@@ -16,16 +28,22 @@ class SensorFeatures:
         features: dict,
         participants_usernames: Collection = None,
     ):
-        self.grouping_variable = grouping_variable
+
+        self.grouping_variable_name = grouping_variable
+        self.grouping_variable = [grouping_variable]
 
         self.data_types = features.keys()
 
+        self.participants_label: str = ""
         if participants_usernames is None:
             participants_usernames = participants.query_db.get_usernames(
                 collection_start=datetime.date.fromisoformat("2020-08-01")
             )
+            self.participants_label = "all"
         self.participants_usernames = participants_usernames
 
+        self.df_features_all = pd.DataFrame()
+
         self.df_proximity = pd.DataFrame()
         self.df_proximity_counts = pd.DataFrame()
 
@@ -33,19 +51,28 @@ class SensorFeatures:
         self.df_sms = pd.DataFrame()
         self.df_calls_sms = pd.DataFrame()
 
+        self.folder = None
+        self.filename_prefix = ""
+        self.construct_export_path()
+        print("SensorFeatures initialized.")
+
     def set_sensor_data(self):
+        print("Querying database ...")
         if "proximity" in self.data_types:
             self.df_proximity = proximity.get_proximity_data(
                 self.participants_usernames
             )
+            print("Got proximity data from the DB.")
             self.df_proximity = helper.get_date_from_timestamp(self.df_proximity)
             self.df_proximity = proximity.recode_proximity(self.df_proximity)
         if "communication" in self.data_types:
             self.df_calls = communication.get_call_data(self.participants_usernames)
             self.df_calls = helper.get_date_from_timestamp(self.df_calls)
+            print("Got calls data from the DB.")
 
             self.df_sms = communication.get_sms_data(self.participants_usernames)
             self.df_sms = helper.get_date_from_timestamp(self.df_sms)
+            print("Got sms data from the DB.")
 
     def get_sensor_data(self, data_type) -> pd.DataFrame:
         if data_type == "proximity":
@@ -56,15 +83,41 @@ class SensorFeatures:
             raise KeyError("This data type has not been implemented.")
 
     def calculate_features(self):
+        print("Calculating features ...")
+        if not self.participants_label:
+            raise ValueError(WARNING_PARTICIPANTS_LABEL)
         if "proximity" in self.data_types:
             self.df_proximity_counts = proximity.count_proximity(
-                self.df_proximity, ["participant_id", self.grouping_variable]
+                self.df_proximity, self.grouping_variable
             )
+            self.df_features_all = safe_outer_merge_on_index(
+                self.df_features_all, self.df_proximity_counts
+            )
+            print("Calculated proximity features.")
+        to_csv_with_settings(
+            self.df_proximity, self.folder, self.filename_prefix, data_type="prox"
+        )
+
         if "communication" in self.data_types:
             self.df_calls_sms = communication.calls_sms_features(
-                df_calls=self.df_calls, df_sms=self.df_sms
+                df_calls=self.df_calls,
+                df_sms=self.df_sms,
+                group_by=self.grouping_variable,
             )
-            # TODO Think about joining dataframes.
+            self.df_features_all = safe_outer_merge_on_index(
+                self.df_features_all, self.df_calls_sms
+            )
+            print("Calculated communication features.")
+        to_csv_with_settings(
+            self.df_calls_sms, self.folder, self.filename_prefix, data_type="comm"
+        )
+
+        self.df_features_all.fillna(
+            value=proximity.FILL_NA_PROXIMITY, inplace=True, downcast="infer",
+        )
+        self.df_features_all.fillna(
+            value=communication.FILL_NA_CALLS_SMS_ALL, inplace=True, downcast="infer",
+        )
 
     def get_features(self, data_type, feature_names) -> pd.DataFrame:
         if data_type == "proximity":
@@ -75,14 +128,28 @@ class SensorFeatures:
             if feature_names == "all":
                 feature_names = communication.FEATURES_CALLS_SMS_ALL
             return self.df_calls_sms[feature_names]
+        elif data_type == "all":
+            return self.df_features_all
         else:
             raise KeyError("This data type has not been implemented.")
 
+    def construct_export_path(self):
+        if not self.participants_label:
+            warnings.warn(WARNING_PARTICIPANTS_LABEL, UserWarning)
+        self.folder = here("machine_learning/intermediate_results/features", warn=True)
+        self.filename_prefix = (
+            self.participants_label + "_" + self.grouping_variable_name
+        )
+
+    def set_participants_label(self, label: str):
+        self.participants_label = label
+        self.construct_export_path()
+
 
 class Labels:
     def __init__(
         self,
-        grouping_variable: str,
+        grouping_variable: list,
         labels: dict,
         participants_usernames: Collection = None,
     ):
@@ -101,9 +168,15 @@ class Labels:
         self.df_esm_interest = pd.DataFrame()
         self.df_esm_clean = pd.DataFrame()
 
+        self.df_esm_means = pd.DataFrame()
+        print("Labels initialized.")
+
     def set_labels(self):
+        print("Querying database ...")
         self.df_esm = esm.get_esm_data(self.participants_usernames)
+        print("Got ESM data from the DB.")
         self.df_esm_preprocessed = esm.preprocess_esm(self.df_esm)
+        print("ESM data preprocessed.")
         if "PANAS" in self.questionnaires:
             self.df_esm_interest = self.df_esm_preprocessed[
                 (
@@ -116,6 +189,7 @@ class Labels:
                 )
             ]
         self.df_esm_clean = esm.clean_up_esm(self.df_esm_interest)
+        print("ESM data cleaned.")
 
     def get_labels(self, questionnaire):
         if questionnaire == "PANAS":
@@ -123,109 +197,131 @@ class Labels:
         else:
             raise KeyError("This questionnaire has not been implemented as a label.")
 
-
-class MachineLearningPipeline:
-    def __init__(
-        self,
-        labels_questionnaire,
-        labels_scale,
-        data_types,
-        participants_usernames=None,
-        feature_names=None,
-        grouping_variable=None,
-    ):
-        if participants_usernames is None:
-            participants_usernames = participants.query_db.get_usernames(
-                collection_start=datetime.date.fromisoformat("2020-08-01")
+    def aggregate_labels(self):
+        print("Aggregating labels ...")
+        self.df_esm_means = (
+            self.df_esm_clean.groupby(
+                ["participant_id", "questionnaire_id"] + self.grouping_variable
             )
-        self.participants_usernames = participants_usernames
-        self.labels_questionnaire = labels_questionnaire
-        self.data_types = data_types
-
-        if feature_names is None:
-            self.feature_names = []
-        self.df_features = pd.DataFrame()
-        self.labels_scale = labels_scale
-        self.df_labels = pd.DataFrame()
-        self.grouping_variable = grouping_variable
-        self.df_groups = pd.DataFrame()
-
-        self.model = None
-        self.validation_method = None
-
-        self.df_esm = pd.DataFrame()
-        self.df_esm_preprocessed = pd.DataFrame()
-        self.df_esm_interest = pd.DataFrame()
-        self.df_esm_clean = pd.DataFrame()
-
-        self.df_full_data_daily_means = pd.DataFrame()
-        self.df_esm_daily_means = pd.DataFrame()
-        self.df_proximity_daily_counts = pd.DataFrame()
-
-    # def get_labels(self):
-    #     self.df_esm = esm.get_esm_data(self.participants_usernames)
-    #     self.df_esm_preprocessed = esm.preprocess_esm(self.df_esm)
-    #     if self.labels_questionnaire == "PANAS":
-    #         self.df_esm_interest = self.df_esm_preprocessed[
-    #             (
-    #                 self.df_esm_preprocessed["questionnaire_id"]
-    #                 == QUESTIONNAIRE_IDS.get("PANAS").get("PA")
-    #             )
-    #             | (
-    #                 self.df_esm_preprocessed["questionnaire_id"]
-    #                 == QUESTIONNAIRE_IDS.get("PANAS").get("NA")
-    #             )
-    #         ]
-    #     self.df_esm_clean = esm.clean_up_esm(self.df_esm_interest)
-
-    # def aggregate_daily(self):
-    #     self.df_esm_daily_means = (
-    #         self.df_esm_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"})
-    #     )
-    #     self.df_esm_daily_means = (
-    #         self.df_esm_daily_means.pivot(
-    #             index=["participant_id", "date_lj"],
-    #             columns="questionnaire_id",
-    #             values="esm_numeric_mean",
-    #         )
-    #         .reset_index(col_level=1)
-    #         .rename(columns=QUESTIONNAIRE_IDS_RENAME)
-    #         .set_index(["participant_id", "date_lj"])
-    #     )
-    #     self.df_full_data_daily_means = self.df_esm_daily_means.copy()
-    #     if "proximity" in self.data_types:
-    #         self.df_proximity_daily_counts = proximity.count_proximity(
-    #             self.df_proximity, ["participant_id", "date_lj"]
-    #         )
-    #         self.df_full_data_daily_means = self.df_full_data_daily_means.join(
-    #             self.df_proximity_daily_counts
-    #         )
-
-    def assign_columns(self):
-        self.df_features = self.df_full_data_daily_means[self.feature_names]
-        self.df_labels = self.df_full_data_daily_means[self.labels_scale]
-        if self.grouping_variable:
-            self.df_groups = self.df_full_data_daily_means[self.grouping_variable]
-        else:
-            self.df_groups = None
-
-    def validate_model(self):
-        if self.model is None:
-            raise AttributeError(
-                "Please, specify a machine learning model first, by setting the .model attribute."
-            )
-        if self.validation_method is None:
-            raise AttributeError(
-                "Please, specify a cross validation method first, by setting the .validation_method attribute."
-            )
-        cross_val_score(
-            estimator=self.model,
-            X=self.df_features,
-            y=self.df_labels,
-            groups=self.df_groups,
-            cv=self.validation_method,
-            n_jobs=-1,
+            .esm_user_answer_numeric.agg("mean")
+            .reset_index()
+            .rename(columns={"esm_user_answer_numeric": "esm_numeric_mean"})
         )
+        self.df_esm_means = (
+            self.df_esm_means.pivot(
+                index=["participant_id"] + self.grouping_variable,
+                columns="questionnaire_id",
+                values="esm_numeric_mean",
+            )
+            .reset_index(col_level=1)
+            .rename(columns=QUESTIONNAIRE_IDS_RENAME)
+            .set_index(["participant_id"] + self.grouping_variable)
+        )
+        print("Labels aggregated.")
+
+    def get_aggregated_labels(self):
+        return self.df_esm_means
+
+
+class ModelValidation:
+    def __init__(self, X, y, group_variable=None, cv_name="loso"):
+        self.model = None
+        self.cv = None
+
+        idx_common = X.index.intersection(y.index)
+        self.y = y.loc[idx_common, "NA"]
+        # TODO Handle the case of multiple labels.
+        self.X = X.loc[idx_common]
+        self.groups = self.y.index.get_level_values(group_variable)
+
+        self.cv_name = cv_name
+        print("ModelValidation initialized.")
+
+    def set_cv_method(self):
+        if self.cv_name == "loso":
+            self.cv = LeaveOneGroupOut()
+            self.cv.get_n_splits(X=self.X, y=self.y, groups=self.groups)
+        print("Validation method set.")
+
+    def cross_validate(self):
+        print("Running cross validation ...")
+        if self.model is None:
+            raise TypeError(
+                "Please, specify a machine learning model first, by setting the .model attribute. "
+                "E.g. self.model = sklearn.linear_model.LinearRegression()"
+            )
+        if self.cv is None:
+            raise TypeError(
+                "Please, specify a cross validation method first, by using set_cv_method() first."
+            )
+        if self.X.isna().any().any() or self.y.isna().any().any():
+            raise ValueError(
+                "NaNs were found in either X or y. Please, check your data before continuing."
+            )
+        return cross_val_score(
+            estimator=self.model,
+            X=self.X,
+            y=self.y,
+            groups=self.groups,
+            cv=self.cv,
+            n_jobs=-1,
+            scoring="r2",
+        )
+
+
+def safe_outer_merge_on_index(left, right):
+    if left.empty:
+        return right
+    elif right.empty:
+        return left
+    else:
+        return pd.merge(
+            left,
+            right,
+            how="outer",
+            left_index=True,
+            right_index=True,
+            validate="one_to_one",
+        )
+
+
+def to_csv_with_settings(
+    df: pd.DataFrame, folder: Path, filename_prefix: str, data_type: str
+) -> None:
+    export_filename = filename_prefix + "_" + data_type + ".csv"
+    full_path = folder / export_filename
+    df.to_csv(
+        path_or_buf=full_path,
+        sep=",",
+        na_rep="NA",
+        header=True,
+        index=False,
+        encoding="utf-8",
+    )
+    print("Exported the dataframe to " + str(full_path))
+
+
+if __name__ == "__main__":
+    with open("./config/prox_comm_PANAS_features.yaml", "r") as file:
+        sensor_features_params = yaml.safe_load(file)
+    sensor_features = SensorFeatures(**sensor_features_params)
+    sensor_features.set_sensor_data()
+    sensor_features.calculate_features()
+
+    with open("./config/prox_comm_PANAS_labels.yaml", "r") as file:
+        labels_params = yaml.safe_load(file)
+    labels = Labels(**labels_params)
+    labels.set_labels()
+    labels.aggregate_labels()
+
+    model_validation = ModelValidation(
+        sensor_features.get_features("all", "all"),
+        labels.get_aggregated_labels(),
+        group_variable="participant_id",
+        cv_name="loso",
+    )
+    model_validation.model = linear_model.LinearRegression()
+    model_validation.set_cv_method()
+    model_loso_r2 = model_validation.cross_validate()
+    print(model_loso_r2)
+    print(np.mean(model_loso_r2))