Prepare a function to run all models from an input.

# Conflicts:
#	exploration/ex_ml_pipeline.py

.gitignore

@@ -5,3 +5,7 @@ __pycache__/
 /exploration/*.ipynb
 /config/*.ipynb
 /statistical_analysis/*.ipynb
+/machine_learning/intermediate_results/
+/data/features/
+/data/baseline/
+/data/*input*.csv

.gitmodules

@@ -0,0 +1,4 @@
+[submodule "rapids"]
+	path = rapids
+	url = https://repo.ijs.si/junoslukan/rapids.git
+	branch = master

.idea/misc.xml

@@ -4,4 +4,17 @@
   <component name="PyCharmProfessionalAdvertiser">
     <option name="shown" value="true" />
   </component>
+  <component name="RMarkdownSettings">
+    <option name="renderProfiles">
+      <map>
+        <entry key="file://$PROJECT_DIR$/rapids/src/visualization/merge_heatmap_sensors_per_minute_per_time_segment.Rmd">
+          <value>
+            <RMarkdownRenderProfile>
+              <option name="outputDirectoryUrl" value="file://$PROJECT_DIR$/rapids/src/visualization" />
+            </RMarkdownRenderProfile>
+          </value>
+        </entry>
+      </map>
+    </option>
+  </component>
 </project>

.idea/snakemake-settings.xml

@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="SmkProjectSettings" sdk="Python 3.10 (snakemake)" enabled="true" />
+</project>

.idea/vcs.xml

@@ -2,5 +2,6 @@
 <project version="4">
   <component name="VcsDirectoryMappings">
     <mapping directory="$PROJECT_DIR$" vcs="Git" />
+    <mapping directory="$PROJECT_DIR$/rapids" vcs="Git" />
   </component>
 </project>

README.md

@@ -27,9 +27,135 @@ To install:
    ipython kernel install --user --name=straw2analysis
    ```
 
-2. Provide an .env file to be used by `python-dotenv` which should be placed in the top folder of the application 
+2. Provide a file called `.env` to be used by `python-dotenv` which should be placed in the top folder of the application 
    and should have the form:
    
    ```
    DB_PASSWORD=database-password
    ```
+   
+# RAPIDS
+
+To install RAPIDS, follow the [instructions on their webpage](https://www.rapids.science/1.6/setup/installation/). 
+
+Here, I include additional information related to the installation and specific to the STRAW2analysis project.
+The installation was tested on Windows using Ubuntu 20.04 on Windows Subsystem for Linux ([WSL2](https://docs.microsoft.com/en-us/windows/wsl/install)).
+
+## Custom configuration
+### Credentials
+
+As mentioned under [Database in RAPIDS documentation](https://www.rapids.science/1.6/snippets/database/), a `credentials.yaml` file is needed to connect to a database.
+It should contain:
+
+```yaml
+PSQL_STRAW:
+  database: staw
+  host: 212.235.208.113
+  password: password
+  port: 5432
+  user: staw_db
+```
+
+where`password` needs to be specified as well.
+
+## Possible installation issues
+### Missing dependencies for RPostgres
+
+To install `RPostgres` R package (used to connect to the PostgreSQL database), an error might occur:
+
+```text
+------------------------- ANTICONF ERROR ---------------------------
+Configuration failed because libpq was not found. Try installing:
+   * deb: libpq-dev (Debian, Ubuntu, etc)
+   * rpm: postgresql-devel (Fedora, EPEL)
+   * rpm: postgreql8-devel, psstgresql92-devel, postgresql93-devel, or postgresql94-devel (Amazon Linux)
+   * csw: postgresql_dev (Solaris)
+   * brew: libpq (OSX)
+If libpq is already installed, check that either:
+  (i)  'pkg-config' is in your PATH AND PKG_CONFIG_PATH contains a libpq.pc file; or
+  (ii) 'pg_config' is in your PATH.
+If neither can detect , you can set INCLUDE_DIR
+and LIB_DIR manually via:
+  R CMD INSTALL --configure-vars='INCLUDE_DIR=... LIB_DIR=...'
+--------------------------[ ERROR MESSAGE ]----------------------------
+  <stdin>:1:10: fatal error: libpq-fe.h: No such file or directory
+compilation terminated.
+```
+
+The library requires `libpq` for compiling from source, so install accordingly.
+
+### Timezone environment variable for tidyverse (relevant for WSL2)
+
+One of the R packages, `tidyverse` might need access to the `TZ` environment variable during the installation.
+On Ubuntu 20.04 on WSL2 this triggers the following error:
+
+```text
+> install.packages('tidyverse')
+
+ERROR: configuration failed for package ‘xml2’
+System has not been booted with systemd as init system (PID 1). Can't operate.
+Failed to create bus connection: Host is down
+Warning in system("timedatectl", intern = TRUE) :
+  running command 'timedatectl' had status 1
+Error in loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]]) :
+  namespace ‘xml2’ 1.3.1 is already loaded, but >= 1.3.2 is required
+Calls: <Anonymous> ... namespaceImportFrom -> asNamespace -> loadNamespace
+Execution halted
+ERROR: lazy loading failed for package ‘tidyverse’
+```
+
+This happens because WSL2 does not use the `timedatectl` service, which provides this variable.
+
+```bash
+~$ timedatectl
+System has not been booted with systemd as init system (PID 1). Can't operate.
+Failed to create bus connection: Host is down
+```
+
+and later 
+
+```bash 
+Warning message:
+In system("timedatectl", intern = TRUE) :
+  running command 'timedatectl' had status 1
+Execution halted
+```
+
+This can be amended by setting the environment variable manually before attempting to install `tidyverse`:
+
+```bash
+export TZ='Europe/Ljubljana'
+```
+
+## Possible runtime issues
+### Unix end of line characters
+
+Upon running rapids, an error might occur:
+
+```bash
+/usr/bin/env: ‘python3\r’: No such file or directory
+```
+
+This is due to Windows style end of line characters. 
+To amend this, I added a `.gitattributes` files to force `git` to checkout `rapids` using Unix EOL characters.
+If this still fails, `dos2unix` can be used to change them.
+
+### System has not been booted with systemd as init system (PID 1)
+
+See [the installation issue above](#Timezone-environment-variable-for-tidyverse-(relevant-for-WSL2)).
+
+## Update RAPIDS
+
+To update RAPIDS, first pull and merge [origin]( https://github.com/carissalow/rapids), such as with:
+
+```commandline
+git fetch --progress "origin" refs/heads/master
+git merge --no-ff origin/master
+```
+
+Next, update the conda and R virtual environment.
+
+```bash
+R -e 'renv::restore(repos = c(CRAN = "https://packagemanager.rstudio.com/all/__linux__/focal/latest"))'
+```
+

config/environment.yml

@@ -12,9 +12,11 @@ dependencies:
   - mypy
   - nodejs
   - pandas
-  - psycopg2
+  - psycopg2 >= 2.9.1
   - python-dotenv
   - pytz
+  - pyprojroot
+  - pyyaml
   - seaborn
   - scikit-learn
   - sqlalchemy

config/models.py

@@ -166,12 +166,43 @@ class Application(Base, AWAREsensor):
 
 
 class Barometer(Base, AWAREsensor):
+    """
+    Contains the barometer sensor data.
+
+    Attributes
+    ----------
+    double_values_0: float
+        The ambient air pressure in mbar (hPa)
+    accuracy: int
+        Sensor’s accuracy level, either 1, 2, or 3 (see [SensorManager](https://developer.android.com/reference/android/hardware/SensorManager.html#SENSOR_STATUS_ACCURACY_HIGH))
+    """
+
     double_values_0 = Column(Float, nullable=False)
     accuracy = Column(SmallInteger, nullable=True)
     label = Column(String, nullable=True)
 
 
 class BarometerSensor(Base, AWAREsensor):
+    """
+    Contains the barometer sensor capabilities.
+
+    Attributes
+    ----------
+    double_sensor_maximum_range: float
+        Maximum sensor value possible
+    double_sensor_minimum_delay: float
+        Minimum sampling delay in microseconds
+    sensor_name: str
+    double_sensor_power_ma: float
+        Sensor’s power drain in mA
+    double_sensor_resolution: float
+        Sensor’s resolution in sensor’s units
+    sensor_type: str
+    sensor_vendor: str
+        Sensor’s manufacturer
+    sensor_version: str
+    """
+
     __tablename__ = "barometer_sensor"
     # Since this table is not really important,
     # I will leave all columns as nullable. (nullable=True by default.)
@@ -257,6 +288,19 @@ class Imperfection(Base):
 
 
 class LightSensor(Base, AWAREsensor):
+    """
+    Contains the light sensor data.
+    Note: Even though this table is named light_sensor, it actually contains what AWARE calls light data
+        (rather than the data about the sensor's capabilities). Cf. Barometer(Sensor) and Temperature(Sensor).
+
+    Attributes
+    ----------
+    double_light_lux: float
+        The ambient luminance in lux units
+    accuracy: int
+        Sensor’s accuracy level, either 1, 2, or 3 (see [SensorManager](https://developer.android.com/reference/android/hardware/SensorManager.html#SENSOR_STATUS_ACCURACY_HIGH))
+    """
+
     __tablename__ = "light_sensor"
     double_light_lux = Column(Float, nullable=False)
     accuracy = Column(Integer, nullable=True)
@@ -376,12 +420,43 @@ class SMS(Base, AWAREsensor):
 
 
 class Temperature(Base, AWAREsensor):
+    """
+    Contains the temperature sensor data.
+
+    Attributes
+    ----------
+    temperature_celsius: float
+        Measured temperature in °C
+    accuracy: int
+        Sensor’s accuracy level, either 1, 2, or 3 (see [SensorManager](https://developer.android.com/reference/android/hardware/SensorManager.html#SENSOR_STATUS_ACCURACY_HIGH))
+    """
+
     temperature_celsius = Column(Float, nullable=False)
     accuracy = Column(SmallInteger, nullable=True)
     label = Column(String, nullable=True)
 
 
 class TemperatureSensor(Base, AWAREsensor):
+    """
+    Contains the temperature sensor capabilities.
+
+    Attributes
+    ----------
+    double_sensor_maximum_range: float
+        Maximum sensor value possible
+    double_sensor_minimum_delay: float
+        Minimum sampling delay in microseconds
+    sensor_name: str
+    double_sensor_power_ma: float
+        Sensor’s power drain in mA
+    double_sensor_resolution: float
+        Sensor’s resolution in sensor’s units
+    sensor_type: str
+    sensor_vendor: str
+        Sensor’s manufacturer
+    sensor_version: str
+    """
+
     # I left all of these nullable,
     # as we haven't seen any data from this sensor anyway.
     __tablename__ = "temperature_sensor"

exploration/debug_heatmap.py

@@ -0,0 +1,323 @@
+# ---
+# jupyter:
+#   jupytext:
+#     formats: ipynb,py:percent
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.13.0
+#   kernelspec:
+#     display_name: straw2analysis
+#     language: python
+#     name: straw2analysis
+# ---
+
+# %%
+import os, sys
+import importlib
+import pandas as pd
+import numpy as np
+
+# import plotly.graph_objects as go
+from importlib import util
+from pathlib import Path
+import yaml
+
+# %%
+phone_data_yield = pd.read_csv(
+    "../rapids/data/interim/p011/phone_yielded_timestamps_with_datetime.csv",
+    parse_dates=["local_date_time"],
+)
+time_segments_labels = pd.read_csv(
+    "../rapids/data/interim/time_segments/p011_time_segments_labels.csv"
+)
+
+# %%
+phone_data_yield["assigned_segments"] = phone_data_yield[
+    "assigned_segments"
+].str.replace(r"_RR\d+SS#", "#")
+time_segments_labels["label"] = time_segments_labels["label"].str.replace(
+    r"_RR\d+SS$", ""
+)
+
+
+# %% tags=[]
+def filter_data_by_segment(data, time_segment):
+    data.dropna(subset=["assigned_segments"], inplace=True)
+    if data.shape[0] == 0:  # data is empty
+        data["local_segment"] = data["timestamps_segment"] = None
+        return data
+
+    datetime_regex = "[0-9]{4}[\-|\/][0-9]{2}[\-|\/][0-9]{2} [0-9]{2}:[0-9]{2}:[0-9]{2}"
+    timestamps_regex = "[0-9]{13}"
+    segment_regex = "\[({}#{},{};{},{})\]".format(
+        time_segment, datetime_regex, datetime_regex, timestamps_regex, timestamps_regex
+    )
+    data["local_segment"] = data["assigned_segments"].str.extract(
+        segment_regex, expand=True
+    )
+    data = data.drop(columns=["assigned_segments"])
+    data = data.dropna(subset=["local_segment"])
+    if (
+        data.shape[0] == 0
+    ):  # there are no rows belonging to time_segment after droping na
+        data["timestamps_segment"] = None
+    else:
+        data[["local_segment", "timestamps_segment"]] = data["local_segment"].str.split(
+            pat=";", n=1, expand=True
+        )
+
+    # chunk episodes
+    if (
+        (not data.empty)
+        and ("start_timestamp" in data.columns)
+        and ("end_timestamp" in data.columns)
+    ):
+        data = chunk_episodes(data)
+
+    return data
+
+
+# %% tags=[]
+time_segment = "daily"
+phone_data_yield_per_segment = filter_data_by_segment(phone_data_yield, time_segment)
+
+# %%
+phone_data_yield.tail()
+
+# %%
+phone_data_yield_per_segment.tail()
+
+
+# %%
+def getDataForPlot(phone_data_yield_per_segment):
+    # calculate the length (in minute) of per segment instance
+    phone_data_yield_per_segment["length"] = (
+        phone_data_yield_per_segment["timestamps_segment"]
+        .str.split(",")
+        .apply(lambda x: int((int(x[1]) - int(x[0])) / (1000 * 60)))
+    )
+    # calculate the number of sensors logged at least one row of data per minute.
+    phone_data_yield_per_segment = (
+        phone_data_yield_per_segment.groupby(
+            ["local_segment", "length", "local_date", "local_hour", "local_minute"]
+        )[["sensor", "local_date_time"]]
+        .max()
+        .reset_index()
+    )
+    # extract local start datetime of the segment from "local_segment" column
+    phone_data_yield_per_segment["local_segment_start_datetimes"] = pd.to_datetime(
+        phone_data_yield_per_segment["local_segment"].apply(
+            lambda x: x.split("#")[1].split(",")[0]
+        )
+    )
+    # calculate the number of minutes after local start datetime of the segment
+    phone_data_yield_per_segment["minutes_after_segment_start"] = (
+        (
+            phone_data_yield_per_segment["local_date_time"]
+            - phone_data_yield_per_segment["local_segment_start_datetimes"]
+        )
+        / pd.Timedelta(minutes=1)
+    ).astype("int")
+
+    # impute missing rows with 0
+    columns_for_full_index = phone_data_yield_per_segment[
+        ["local_segment_start_datetimes", "length"]
+    ].drop_duplicates(keep="first")
+    columns_for_full_index = columns_for_full_index.apply(
+        lambda row: [
+            [row["local_segment_start_datetimes"], x] for x in range(row["length"] + 1)
+        ],
+        axis=1,
+    )
+    full_index = []
+    for columns in columns_for_full_index:
+        full_index = full_index + columns
+    full_index = pd.MultiIndex.from_tuples(
+        full_index,
+        names=("local_segment_start_datetimes", "minutes_after_segment_start"),
+    )
+    phone_data_yield_per_segment = (
+        phone_data_yield_per_segment.set_index(
+            ["local_segment_start_datetimes", "minutes_after_segment_start"]
+        )
+        .reindex(full_index)
+        .reset_index()
+        .fillna(0)
+    )
+
+    # transpose the dataframe per local start datetime of the segment and discard the useless index layer
+    phone_data_yield_per_segment = phone_data_yield_per_segment.groupby(
+        "local_segment_start_datetimes"
+    )[["minutes_after_segment_start", "sensor"]].apply(
+        lambda x: x.set_index("minutes_after_segment_start").transpose()
+    )
+    phone_data_yield_per_segment.index = phone_data_yield_per_segment.index.get_level_values(
+        "local_segment_start_datetimes"
+    )
+    return phone_data_yield_per_segment
+
+
+# %%
+data_for_plot_per_segment = getDataForPlot(phone_data_yield_per_segment)
+
+# %%
+# calculate the length (in minute) of per segment instance
+phone_data_yield_per_segment["length"] = (
+    phone_data_yield_per_segment["timestamps_segment"]
+    .str.split(",")
+    .apply(lambda x: int((int(x[1]) - int(x[0])) / (1000 * 60)))
+)
+
+# %%
+phone_data_yield_per_segment.tail()
+
+# %%
+# calculate the number of sensors logged at least one row of data per minute.
+phone_data_yield_per_segment = (
+    phone_data_yield_per_segment.groupby(
+        ["local_segment", "length", "local_date", "local_hour", "local_minute"]
+    )[["sensor", "local_date_time"]]
+    .max()
+    .reset_index()
+)
+
+# %%
+# extract local start datetime of the segment from "local_segment" column
+phone_data_yield_per_segment["local_segment_start_datetimes"] = pd.to_datetime(
+    phone_data_yield_per_segment["local_segment"].apply(
+        lambda x: x.split("#")[1].split(",")[0]
+    )
+)
+
+# %%
+# calculate the number of minutes after local start datetime of the segment
+phone_data_yield_per_segment["minutes_after_segment_start"] = (
+    (
+        phone_data_yield_per_segment["local_date_time"]
+        - phone_data_yield_per_segment["local_segment_start_datetimes"]
+    )
+    / pd.Timedelta(minutes=1)
+).astype("int")
+
+# %%
+columns_for_full_index = phone_data_yield_per_segment[
+    ["local_segment_start_datetimes", "length"]
+].drop_duplicates(keep="first")
+columns_for_full_index = columns_for_full_index.apply(
+    lambda row: [
+        [row["local_segment_start_datetimes"], x] for x in range(row["length"] + 1)
+    ],
+    axis=1,
+)
+
+# %%
+full_index = []
+for columns in columns_for_full_index:
+    full_index = full_index + columns
+full_index = pd.MultiIndex.from_tuples(
+    full_index, names=("local_segment_start_datetimes", "minutes_after_segment_start")
+)
+
+# %%
+phone_data_yield_per_segment.tail()
+
+# %% [markdown]
+# # A workaround
+
+# %%
+phone_data_yield_per_segment["local_segment_start_datetimes", "minutes_after_segment_start"] = phone_data_yield_per_segment[
+    ["local_segment_start_datetimes", "minutes_after_segment_start"]
+].drop_duplicates(keep="first")
+
+# %%
+phone_data_yield_per_segment.set_index(
+    ["local_segment_start_datetimes", "minutes_after_segment_start"],
+    verify_integrity=True,
+).reindex(full_index)
+
+# %%
+phone_data_yield_per_segment.head()
+
+
+# %% [markdown]
+# # Retry
+
+# %%
+def getDataForPlot(phone_data_yield_per_segment):
+    # calculate the length (in minute) of per segment instance
+    phone_data_yield_per_segment["length"] = (
+        phone_data_yield_per_segment["timestamps_segment"]
+        .str.split(",")
+        .apply(lambda x: int((int(x[1]) - int(x[0])) / (1000 * 60)))
+    )
+    # calculate the number of sensors logged at least one row of data per minute.
+    phone_data_yield_per_segment = (
+        phone_data_yield_per_segment.groupby(
+            ["local_segment", "length", "local_date", "local_hour", "local_minute"]
+        )[["sensor", "local_date_time"]]
+        .max()
+        .reset_index()
+    )
+    # extract local start datetime of the segment from "local_segment" column
+    phone_data_yield_per_segment["local_segment_start_datetimes"] = pd.to_datetime(
+        phone_data_yield_per_segment["local_segment"].apply(
+            lambda x: x.split("#")[1].split(",")[0]
+        )
+    )
+    # calculate the number of minutes after local start datetime of the segment
+    phone_data_yield_per_segment["minutes_after_segment_start"] = (
+        (
+            phone_data_yield_per_segment["local_date_time"]
+            - phone_data_yield_per_segment["local_segment_start_datetimes"]
+        )
+        / pd.Timedelta(minutes=1)
+    ).astype("int")
+
+    # impute missing rows with 0
+    columns_for_full_index = phone_data_yield_per_segment[
+        ["local_segment_start_datetimes", "length"]
+    ].drop_duplicates(keep="first")
+    columns_for_full_index = columns_for_full_index.apply(
+        lambda row: [
+            [row["local_segment_start_datetimes"], x] for x in range(row["length"] + 1)
+        ],
+        axis=1,
+    )
+    full_index = []
+    for columns in columns_for_full_index:
+        full_index = full_index + columns
+    full_index = pd.MultiIndex.from_tuples(
+        full_index,
+        names=("local_segment_start_datetimes", "minutes_after_segment_start"),
+    )
+    phone_data_yield_per_segment = phone_data_yield_per_segment.drop_duplicates(subset=["local_segment_start_datetimes", "minutes_after_segment_start"],keep="first")
+    phone_data_yield_per_segment = (
+        phone_data_yield_per_segment.set_index(
+            ["local_segment_start_datetimes", "minutes_after_segment_start"]
+        )
+        .reindex(full_index)
+        .reset_index()
+        .fillna(0)
+    )
+
+    # transpose the dataframe per local start datetime of the segment and discard the useless index layer
+    phone_data_yield_per_segment = phone_data_yield_per_segment.groupby(
+        "local_segment_start_datetimes"
+    )[["minutes_after_segment_start", "sensor"]].apply(
+        lambda x: x.set_index("minutes_after_segment_start").transpose()
+    )
+    phone_data_yield_per_segment.index = phone_data_yield_per_segment.index.get_level_values(
+        "local_segment_start_datetimes"
+    )
+    return phone_data_yield_per_segment
+
+
+# %%
+phone_data_yield_per_segment = filter_data_by_segment(phone_data_yield, time_segment)
+
+# %%
+data_for_plot_per_segment = getDataForPlot(phone_data_yield_per_segment)
+
+# %%

exploration/ex_all_feat_ml_pipeline.py

@@ -0,0 +1,473 @@
+# ---
+# jupyter:
+#   jupytext:
+#     formats: ipynb,py:percent
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.13.0
+#   kernelspec:
+#     display_name: straw2analysis
+#     language: python
+#     name: straw2analysis
+# ---
+
+# %% jupyter={"source_hidden": true}
+# %matplotlib inline
+import datetime
+import importlib
+import os
+import sys
+
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+import yaml
+from pyprojroot import here
+from sklearn import linear_model, svm, kernel_ridge, gaussian_process, ensemble
+from sklearn.model_selection import LeaveOneGroupOut, cross_val_score
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.impute import SimpleImputer
+from xgboost import XGBRegressor
+
+nb_dir = os.path.split(os.getcwd())[0]
+if nb_dir not in sys.path:
+    sys.path.append(nb_dir)
+
+import machine_learning.features_sensor
+import machine_learning.labels
+import machine_learning.model
+
+# %% [markdown]
+# # RAPIDS models
+
+# %% [markdown]
+# ## PANAS negative affect
+
+# %% jupyter={"source_hidden": true}
+# model_input = pd.read_csv("../data/input_PANAS_NA.csv") # Nestandardizirani podatki
+model_input = pd.read_csv("../data/z_input_PANAS_NA.csv") # Standardizirani podatki
+
+# %% [markdown]
+# ### NaNs before dropping cols and rows
+
+# %% jupyter={"source_hidden": true}
+sns.set(rc={"figure.figsize":(16, 8)})
+sns.heatmap(model_input.sort_values('pid').set_index('pid').isna(), cbar=False)
+
+# %% jupyter={"source_hidden": true}
+nan_cols = list(model_input.loc[:, model_input.isna().all()].columns)
+nan_cols
+
+# %% jupyter={"source_hidden": true}
+model_input.dropna(axis=1, how="all", inplace=True)
+model_input.dropna(axis=0, how="any", subset=["target"], inplace=True)
+
+# %% [markdown]
+# ### NaNs after dropping NaN cols and rows where target is NaN
+
+# %% jupyter={"source_hidden": true}
+sns.set(rc={"figure.figsize":(16, 8)})
+sns.heatmap(model_input.sort_values('pid').set_index('pid').isna(), cbar=False)
+
+# %% jupyter={"source_hidden": true}
+index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
+#if "pid" in model_input.columns:
+#    index_columns.append("pid")
+model_input.set_index(index_columns, inplace=True)
+
+data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"]
+
+# %% jupyter={"source_hidden": true}
+categorical_feature_colnames = ["gender", "startlanguage"]
+
+# %% jupyter={"source_hidden": true}
+categorical_features = data_x[categorical_feature_colnames].copy()
+
+# %% jupyter={"source_hidden": true}
+mode_categorical_features = categorical_features.mode().iloc[0]
+
+# %% jupyter={"source_hidden": true}
+# fillna with mode
+categorical_features = categorical_features.fillna(mode_categorical_features)
+
+# %% jupyter={"source_hidden": true}
+# one-hot encoding
+categorical_features = categorical_features.apply(lambda col: col.astype("category"))
+if not categorical_features.empty:
+    categorical_features = pd.get_dummies(categorical_features)
+
+# %% jupyter={"source_hidden": true}
+numerical_features = data_x.drop(categorical_feature_colnames, axis=1)
+
+# %% jupyter={"source_hidden": true}
+train_x = pd.concat([numerical_features, categorical_features], axis=1)
+
+# %% jupyter={"source_hidden": true}
+train_x.dtypes
+
+# %% jupyter={"source_hidden": true}
+logo = LeaveOneGroupOut()
+logo.get_n_splits(
+    train_x,
+    data_y,
+    groups=data_groups,
+)
+
+# %% jupyter={"source_hidden": true}
+sum(data_y.isna())
+
+# %% [markdown]
+# ### Linear Regression
+
+# %% jupyter={"source_hidden": true}
+lin_reg_rapids = linear_model.LinearRegression()
+
+# %% jupyter={"source_hidden": true}
+imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
+
+# %% jupyter={"source_hidden": true}
+lin_reg_scores = cross_val_score(
+    lin_reg_rapids,
+    X=imputer.fit_transform(train_x),
+    y=data_y,
+    groups=data_groups,
+    cv=logo,
+    n_jobs=-1,
+    scoring='r2'
+)
+lin_reg_scores
+np.median(lin_reg_scores)
+
+# %% [markdown]
+# ### Ridge regression
+
+# %% jupyter={"source_hidden": true}
+ridge_reg = linear_model.Ridge(alpha=.5)
+
+# %% tags=[] jupyter={"source_hidden": true}
+ridge_reg_scores = cross_val_score(
+    ridge_reg,
+    X=imputer.fit_transform(train_x),
+    y=data_y,
+    groups=data_groups,
+    cv=logo,
+    n_jobs=-1,
+    scoring="r2"
+)
+np.median(ridge_reg_scores)
+
+# %% [markdown]
+# ### Lasso
+
+# %% jupyter={"source_hidden": true}
+lasso_reg = linear_model.Lasso(alpha=0.1)
+
+# %% jupyter={"source_hidden": true}
+lasso_reg_score = cross_val_score(
+    lasso_reg,
+    X=imputer.fit_transform(train_x),
+    y=data_y,
+    groups=data_groups,
+    cv=logo,
+    n_jobs=-1,
+    scoring="r2"
+)
+np.median(lasso_reg_score)
+
+# %% [markdown]
+# ### Bayesian Ridge
+
+# %% jupyter={"source_hidden": true}
+bayesian_ridge_reg = linear_model.BayesianRidge()
+
+# %% jupyter={"source_hidden": true}
+bayesian_ridge_reg_score = cross_val_score(
+    bayesian_ridge_reg,
+    X=imputer.fit_transform(train_x),
+    y=data_y,
+    groups=data_groups,
+    cv=logo,
+    n_jobs=-1,
+    scoring="r2"
+)
+np.median(bayesian_ridge_reg_score)
+
+# %% [markdown]
+# ### RANSAC (outlier robust regression)
+
+# %% jupyter={"source_hidden": true}
+ransac_reg = linear_model.RANSACRegressor()
+
+# %% jupyter={"source_hidden": true}
+np.median(
+    cross_val_score(
+    ransac_reg,
+    X=imputer.fit_transform(train_x),
+    y=data_y,
+    groups=data_groups,
+    cv=logo,
+    n_jobs=-1,
+    scoring="r2"
+    )
+)
+
+# %% [markdown]
+# ### Support vector regression
+
+# %% jupyter={"source_hidden": true}
+svr = svm.SVR()
+
+# %% jupyter={"source_hidden": true}
+np.median(
+    cross_val_score(
+    svr,
+    X=imputer.fit_transform(train_x),
+    y=data_y,
+    groups=data_groups,
+    cv=logo,
+    n_jobs=-1,
+    scoring="r2"
+    )
+)
+
+# %% [markdown]
+# ### Kernel Ridge regression
+
+# %% jupyter={"source_hidden": true}
+kridge = kernel_ridge.KernelRidge()
+
+# %% jupyter={"source_hidden": true}
+np.median(
+    cross_val_score(
+        kridge,
+        X=imputer.fit_transform(train_x),
+        y=data_y,
+        groups=data_groups,
+        cv=logo,
+        n_jobs=-1,
+        scoring="r2"
+    )
+)
+# %% [markdown]
+# ### Gaussian Process Regression
+
+# %% jupyter={"source_hidden": true}
+gpr = gaussian_process.GaussianProcessRegressor()
+
+# %% jupyter={"source_hidden": true}
+
+np.median(
+    cross_val_score(
+        gpr,
+        X=imputer.fit_transform(train_x),
+        y=data_y,
+        groups=data_groups,
+        cv=logo,
+        n_jobs=-1,
+        scoring="r2"
+    )
+)
+# %%
+def insert_row(df, row):
+    return pd.concat([df, pd.DataFrame([row], columns=df.columns)], ignore_index=True)
+
+# %%
+def run_all_models(input_csv):
+    # Prepare data
+    model_input = pd.read_csv(input_csv)
+    model_input.dropna(axis=1, how="all", inplace=True)
+    model_input.dropna(axis=0, how="any", subset=["target"], inplace=True)
+
+    index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
+    model_input.set_index(index_columns, inplace=True)
+
+    data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"]
+
+    categorical_feature_colnames = ["gender", "startlanguage"]
+    categorical_features = data_x[categorical_feature_colnames].copy()
+    mode_categorical_features = categorical_features.mode().iloc[0]
+    # fillna with mode
+    categorical_features = categorical_features.fillna(mode_categorical_features)
+    # one-hot encoding
+    categorical_features = categorical_features.apply(lambda col: col.astype("category"))
+    if not categorical_features.empty:
+        categorical_features = pd.get_dummies(categorical_features)
+
+    numerical_features = data_x.drop(categorical_feature_colnames, axis=1)
+
+    train_x = pd.concat([numerical_features, categorical_features], axis=1)
+    imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
+    train_x_imputed = imputer.fit_transform(train_x)
+
+    # Prepare cross validation
+    logo = LeaveOneGroupOut()
+    logo.get_n_splits(
+        train_x,
+        data_y,
+        groups=data_groups,
+    )
+    scores = pd.DataFrame(columns=["method", "median", "max"])
+
+    # Validate models
+    lin_reg_rapids = linear_model.LinearRegression()
+    lin_reg_scores = cross_val_score(
+        lin_reg_rapids,
+        X=train_x_imputed,
+        y=data_y,
+        groups=data_groups,
+        cv=logo,
+        n_jobs=-1,
+        scoring='r2'
+    )
+    print("Linear regression:")
+    print(np.median(lin_reg_scores))
+    scores = insert_row(scores, ["Linear regression",np.median(lin_reg_scores),np.max(lin_reg_scores)])
+
+    ridge_reg = linear_model.Ridge(alpha=.5)
+    ridge_reg_scores = cross_val_score(
+        ridge_reg,
+        X=train_x_imputed,
+        y=data_y,
+        groups=data_groups,
+        cv=logo,
+        n_jobs=-1,
+        scoring="r2"
+    )
+    print("Ridge regression")
+    print(np.median(ridge_reg_scores))
+    scores = insert_row(scores, ["Ridge regression",np.median(ridge_reg_scores),np.max(ridge_reg_scores)])
+
+    lasso_reg = linear_model.Lasso(alpha=0.1)
+    lasso_reg_score = cross_val_score(
+        lasso_reg,
+        X=train_x_imputed,
+        y=data_y,
+        groups=data_groups,
+        cv=logo,
+        n_jobs=-1,
+        scoring="r2"
+    )
+    print("Lasso regression")
+    print(np.median(lasso_reg_score))
+    scores = insert_row(scores, ["Lasso regression",np.median(lasso_reg_score),np.max(lasso_reg_score)])
+
+    bayesian_ridge_reg = linear_model.BayesianRidge()
+    bayesian_ridge_reg_score = cross_val_score(
+        bayesian_ridge_reg,
+        X=train_x_imputed,
+        y=data_y,
+        groups=data_groups,
+        cv=logo,
+        n_jobs=-1,
+        scoring="r2"
+    )
+    print("Bayesian Ridge")
+    print(np.median(bayesian_ridge_reg_score))
+    scores = insert_row(scores, ["Bayesian Ridge",np.median(bayesian_ridge_reg_score),np.max(bayesian_ridge_reg_score)])
+
+    ransac_reg = linear_model.RANSACRegressor()
+    ransac_reg_score = cross_val_score(
+        ransac_reg,
+        X=train_x_imputed,
+        y=data_y,
+        groups=data_groups,
+        cv=logo,
+        n_jobs=-1,
+        scoring="r2"
+    )
+    print("RANSAC (outlier robust regression)")
+    print(np.median(ransac_reg_score))
+    scores = insert_row(scores, ["RANSAC",np.median(ransac_reg_score),np.max(ransac_reg_score)])
+
+    svr = svm.SVR()
+    svr_score = cross_val_score(
+        svr,
+        X=train_x_imputed,
+        y=data_y,
+        groups=data_groups,
+        cv=logo,
+        n_jobs=-1,
+        scoring="r2"
+    )
+    print("Support vector regression")
+    print(np.median(svr_score))
+    scores = insert_row(scores, ["Support vector regression",np.median(svr_score),np.max(svr_score)])
+
+    kridge = kernel_ridge.KernelRidge()
+    kridge_score = cross_val_score(
+        kridge,
+        X=train_x_imputed,
+        y=data_y,
+        groups=data_groups,
+        cv=logo,
+        n_jobs=-1,
+        scoring="r2"
+    )
+    print("Kernel Ridge regression")
+    print(np.median(kridge_score))
+    scores = insert_row(scores, ["Kernel Ridge regression",np.median(kridge_score),np.max(kridge_score)])
+
+    gpr = gaussian_process.GaussianProcessRegressor()
+    gpr_score = cross_val_score(
+        gpr,
+        X=train_x_imputed,
+        y=data_y,
+        groups=data_groups,
+        cv=logo,
+        n_jobs=-1,
+        scoring="r2"
+    )
+    print("Gaussian Process Regression")
+    print(np.median(gpr_score))
+    scores = insert_row(scores, ["Gaussian Process Regression",np.median(gpr_score),np.max(gpr_score)])
+
+    rfr = ensemble.RandomForestRegressor(max_features=0.3, n_jobs=-1)
+    rfr_score = cross_val_score(
+        rfr,
+        X=train_x_imputed,
+        y=data_y,
+        groups=data_groups,
+        cv=logo,
+        n_jobs=-1,
+        scoring="r2"
+    )
+    print("Random Forest Regression")
+    print(np.median(rfr_score))
+    scores = insert_row(scores, ["Random Forest Regression",np.median(rfr_score),np.max(rfr_score)])
+
+    xgb = XGBRegressor()
+    xgb_score = cross_val_score(
+        xgb,
+        X=train_x_imputed,
+        y=data_y,
+        groups=data_groups,
+        cv=logo,
+        n_jobs=-1,
+        scoring="r2"
+    )
+    print("XGBoost Regressor")
+    print(np.median(xgb_score))
+    scores = insert_row(scores, ["XGBoost Regressor",np.median(xgb_score),np.max(xgb_score)])
+
+    ada = ensemble.AdaBoostRegressor()
+    ada_score = cross_val_score(
+        ada,
+        X=train_x_imputed,
+        y=data_y,
+        groups=data_groups,
+        cv=logo,
+        n_jobs=-1,
+        scoring="r2"
+    )
+    print("ADA Boost Regressor")
+    print(np.median(ada_score))
+    scores = insert_row(scores, ["ADA Boost Regressor",np.median(ada_score),np.max(ada_score)])
+
+    return scores
+
+
+
+

exploration/expl_ambient.py

@@ -6,7 +6,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.11.4
+#       jupytext_version: 1.13.0
 #   kernelspec:
 #     display_name: straw2analysis
 #     language: python
@@ -21,7 +21,6 @@ import sys
 
 import seaborn as sns
 from pytz import timezone
-from tabulate import tabulate
 
 nb_dir = os.path.split(os.getcwd())[0]
 if nb_dir not in sys.path:
@@ -32,18 +31,18 @@ import participants.query_db
 TZ_LJ = timezone("Europe/Ljubljana")
 
 # %%
-from features.light import *
+from features.ambient import *
 
 # %% [markdown]
-# # Basic characteristics
+# # Light
 
 # %%
-df_light_nokia = get_light_data(["nokia_0000003"])
+df_light_nokia = get_ambient_data(["nokia_0000003"], "light")
 print(df_light_nokia)
 
 # %%
 participants_inactive_usernames = participants.query_db.get_usernames()
-df_light_inactive = get_light_data(participants_inactive_usernames)
+df_light_inactive = get_ambient_data(participants_inactive_usernames, "light")
 
 # %%
 df_light_inactive.accuracy.value_counts()
@@ -103,7 +102,7 @@ df_light_nokia.loc[df_light_nokia["double_light_lux"] == 0, ["datetime_lj"]]
 # Zeroes are present during the day. It does happens when the sensor is physically blocked.
 
 # %% [markdown]
-# # Differences between participants
+# ## Differences between participants
 
 # %%
 df_light_participants = (
@@ -166,3 +165,74 @@ sns.displot(data=df_light_participants, x="std_rel", binwidth=0.005)
 # Relative variability is homogeneous.
 #
 # This means that light data needs to be standardized. Min/max standardization would probably fit best.
+
+# %% [markdown]
+# # Barometer
+
+# %% [markdown]
+# ## Barometer sensor
+
+# %%
+df_barometer_sensor_samsung = get_ambient_data(["samsung_0000002"], "barometer_sensor")
+df_barometer_sensor_samsung.shape
+
+# %% [markdown]
+# Even though we have many values for this sensor, they are all repeated as seen below.
+
+# %%
+barometer_sensor_cols = df_barometer_sensor_samsung.columns.to_list()
+barometer_sensor_cols.remove("id")
+barometer_sensor_cols.remove("_id")
+barometer_sensor_cols.remove("timestamp")
+barometer_sensor_cols.remove("device_id")
+print(df_barometer_sensor_samsung.drop_duplicates(subset=barometer_sensor_cols))
+
+# %% [markdown]
+# ## Barometer data
+
+# %%
+df_barometer_samsung = get_ambient_data(["samsung_0000002"], "barometer")
+print(df_barometer_samsung)
+
+# %%
+df_barometer_inactive = get_ambient_data(participants_inactive_usernames, "barometer")
+
+# %%
+df_barometer_inactive.accuracy.value_counts()
+
+# %%
+df_barometer_inactive.participant_id.nunique()
+
+# %%
+df_barometer_inactive.double_values_0.describe()
+
+# %% [markdown]
+# From [Wikipedia](https://en.wikipedia.org/wiki/Atmospheric_pressure#Mean_sea-level_pressure):
+#
+# > The lowest measurable sea-level pressure is found at the centers of tropical cyclones and tornadoes, with a record low of 870 mbar (87 kPa; 26 inHg).
+
+# %%
+df_barometer_inactive[df_barometer_inactive["double_values_0"] < 870]
+
+# %%
+sns.displot(
+    data=df_barometer_inactive[df_barometer_inactive["double_values_0"] > 870],
+    x="double_values_0",
+    binwidth=10,
+    height=8,
+)
+
+# %% [markdown]
+# # Temperature data
+
+# %% [markdown]
+# ## Temperature sensor
+
+# %% [markdown]
+# This table is empty.
+
+# %% [markdown]
+# ## Temperature data
+
+# %% [markdown]
+# This table is empty.

exploration/expl_app_categories.py

@@ -6,7 +6,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.11.4
+#       jupytext_version: 1.13.0
 #   kernelspec:
 #     display_name: straw2analysis
 #     language: python
@@ -74,3 +74,29 @@ rows_os_manufacturer = df_category_not_found["package_name"].str.contains(
 # %%
 with pd.option_context("display.max_rows", None, "display.max_columns", None):
     display(df_category_not_found.loc[~rows_os_manufacturer])
+
+# %% [markdown]
+# # Export categories
+
+# %% [markdown]
+# Rename all of "not_found" to "system" or "other".
+
+# %%
+df_app_categories_to_export = df_app_categories.copy()
+rows_os_manufacturer_full = (df_app_categories_to_export["package_name"].str.contains(
+    "|".join(manufacturers + custom_rom + other), case=False
+)) & (df_app_categories_to_export["play_store_genre"] == "not_found")
+df_app_categories_to_export.loc[rows_os_manufacturer_full, "play_store_genre"] = "System"
+
+# %%
+rows_not_found = (df_app_categories_to_export["play_store_genre"] == "not_found")
+df_app_categories_to_export.loc[rows_not_found, "play_store_genre"] = "Other"
+
+# %%
+df_app_categories_to_export["play_store_genre"].value_counts()
+
+# %%
+df_app_categories_to_export.rename(columns={"play_store_genre": "genre"},inplace=True)
+df_app_categories_to_export.to_csv("../data/app_categories.csv", columns=["package_hash","genre"],index=False)
+
+# %%

exploration/expl_esm_labels.py

@@ -7,7 +7,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.11.2
+#       jupytext_version: 1.13.0
 #   kernelspec:
 #     display_name: straw2analysis
 #     language: python
@@ -17,6 +17,7 @@
 # %%
 import os
 import sys
+import datetime
 
 import seaborn as sns
 
@@ -26,6 +27,7 @@ if nb_dir not in sys.path:
 import participants.query_db
 from features.esm import *
 from features.esm_JCQ import *
+from features.esm_SAM import *
 
 # %%
 participants_inactive_usernames = participants.query_db.get_usernames(
@@ -99,6 +101,12 @@ df_esm_PANAS_summary_participant[df_esm_PANAS_summary_participant["std"] < 0.1]
 # %% [markdown]
 # # Stress appraisal measure
 
+# %%
+df_SAM_all = extract_stressful_events(df_esm_inactive)
+
+# %%
+df_SAM_all.head()
+
 # %%
 df_esm_SAM = df_esm_preprocessed[
     (df_esm_preprocessed["questionnaire_id"] >= 87)

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/ambient.py

@@ -0,0 +1,91 @@
+from collections.abc import Collection
+
+import pandas as pd
+
+from config.models import (
+    Barometer,
+    BarometerSensor,
+    LightSensor,
+    Participant,
+    Temperature,
+    TemperatureSensor,
+)
+from setup import db_engine, session
+
+MINIMUM_PRESSURE_MB = 870
+# The lowest measurable sea-level pressure is found at the centers of tropical cyclones and tornadoes,
+# with a record low of 870 mbar (87 kPa; 26 inHg).
+
+
+def get_ambient_data(usernames: Collection, sensor=None) -> pd.DataFrame:
+    """
+    Read the data from any of the ambient sensor tables and return it in a dataframe.
+
+    Parameters
+    ----------
+    usernames: Collection
+        A list of usernames to put into the WHERE condition.
+    sensor: str
+        One of: barometer, barometer_sensor, light, temperature, temperature_sensor.
+        Here, the _sensor tables describe the phone sensors, such as their range, dela, resolution, vendor etc.,
+        whereas barometer, light, and temperature describe the measured characteristics of the environment.
+
+    Returns
+    -------
+    df_ambient: pd.DataFrame
+        A dataframe of ambient sensor data.
+    """
+    if sensor == "barometer":
+        query_ambient = session.query(Barometer, Participant.username).filter(
+            Participant.id == Barometer.participant_id
+        )
+    elif sensor == "barometer_sensor":
+        query_ambient = session.query(BarometerSensor, Participant.username).filter(
+            Participant.id == BarometerSensor.participant_id
+        )
+    elif sensor == "light":
+        query_ambient = session.query(LightSensor, Participant.username).filter(
+            Participant.id == LightSensor.participant_id
+        )
+    # Note that LightSensor and its light_sensor table are incorrectly named.
+    # In this table, we actually find light data, i.e. double_light_lux, the ambient luminance in lux,
+    # and NOT light sensor data (its range, dela, resolution, vendor etc.) as the name suggests.
+    # We do not have light sensor data saved in the database.
+    elif sensor == "temperature":
+        query_ambient = session.query(Temperature, Participant.username).filter(
+            Participant.id == Temperature.participant_id
+        )
+    elif sensor == "temperature_sensor":
+        query_ambient = session.query(TemperatureSensor, Participant.username).filter(
+            Participant.id == TemperatureSensor.participant_id
+        )
+    else:
+        raise KeyError(
+            "Specify one of the ambient sensors: "
+            "barometer, barometer_sensor, light, temperature, or temperature_sensor."
+        )
+
+    query_ambient = query_ambient.filter(Participant.username.in_(usernames))
+    with db_engine.connect() as connection:
+        df_ambient = pd.read_sql(query_ambient.statement, connection)
+    return df_ambient
+
+
+def clean_pressure(df_ambient: pd.DataFrame) -> pd.DataFrame:
+    """
+    Simply removes values lower than MINIMUM_PRESSURE_MB (lowest measured pressure).
+
+    Parameters
+    ----------
+    df_ambient: pd.DataFrame
+        A dataframe of barometer data, which includes measured pressure in double_values_0.
+
+    Returns
+    -------
+    df_ambient: pd.DataFrame
+        The same dataframe with rows with low values of pressure removed.
+    """
+    if "double_values_0" not in df_ambient:
+        raise KeyError("The DF does not seem to hold barometer data.")
+    df_ambient = df_ambient[df_ambient["double_values_0"] > MINIMUM_PRESSURE_MB]
+    return df_ambient

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",
@@ -23,21 +30,26 @@ 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_CONTACT = [
+FEATURES_CALLS_SMS_PROP = [
     "proportion_calls_all",
     "proportion_calls_incoming",
     "proportion_calls_outgoing",
@@ -45,6 +57,15 @@ FEATURES_CONTACT = [
     "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:
     """

features/light.py

@@ -1,30 +0,0 @@
-from collections.abc import Collection
-
-import pandas as pd
-
-from config.models import LightSensor, Participant
-from setup import db_engine, session
-
-
-def get_light_data(usernames: Collection) -> pd.DataFrame:
-    """
-    Read the data from the light sensor table and return it in a dataframe.
-
-    Parameters
-    ----------
-    usernames: Collection
-        A list of usernames to put into the WHERE condition.
-
-    Returns
-    -------
-    df_light: pd.DataFrame
-        A dataframe of light data.
-    """
-    query_light = (
-        session.query(LightSensor, Participant.username)
-        .filter(Participant.id == LightSensor.participant_id)
-        .filter(Participant.username.in_(usernames))
-    )
-    with db_engine.connect() as connection:
-        df_light = pd.read_sql(query_light.statement, connection)
-    return df_light

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(

features/timezone.py

@@ -0,0 +1,30 @@
+from collections.abc import Collection
+
+import pandas as pd
+
+from config.models import Participant, Timezone
+from setup import db_engine, session
+
+
+def get_timezone_data(usernames: Collection) -> pd.DataFrame:
+    """
+    Read the data from the proximity sensor table and return it in a dataframe.
+
+    Parameters
+    ----------
+    usernames: Collection
+        A list of usernames to put into the WHERE condition.
+
+    Returns
+    -------
+    df_proximity: pd.DataFrame
+        A dataframe of proximity data.
+    """
+    query_timezone = (
+        session.query(Timezone, Participant.username)
+        .filter(Participant.id == Timezone.participant_id)
+        .filter(Participant.username.in_(usernames))
+    )
+    with db_engine.connect() as connection:
+        df_timezone = pd.read_sql(query_timezone.statement, connection)
+    return df_timezone

images/dag_calls_nokia_example.svg

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+<text text-anchor="middle" x="405" y="-159.5" font-family="sans" font-size="10.00">merge_sensor_features_for_individual_participants</text>
+</g>
+<!-- 9&#45;&gt;10 -->
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+<text text-anchor="middle" x="425" y="-87.5" font-family="sans" font-size="10.00">merge_sensor_features_for_all_participants</text>
+</g>
+<!-- 10&#45;&gt;11 -->
+<g id="edge19" class="edge">
+<title>10&#45;&gt;11</title>
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images/dag_participants_files.svg

@@ -0,0 +1,68 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
+ "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<!-- Generated by graphviz version 2.43.0 (0)
+ -->
+<!-- Title: snakemake_dag Pages: 1 -->
+<svg width="414pt" height="396pt"
+ viewBox="0.00 0.00 414.00 396.00" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
+<g id="graph0" class="graph" transform="scale(1 1) rotate(0) translate(4 392)">
+<title>snakemake_dag</title>
+<polygon fill="white" stroke="transparent" points="-4,4 -4,-392 410,-392 410,4 -4,4"/>
+<!-- 0 -->
+<g id="node1" class="node">
+<title>0</title>
+<text text-anchor="start" x="81" y="-71.6" font-family="sans" font-weight="bold" font-size="18.00">create_participants_files</text>
+<text text-anchor="start" x="81" y="-47.8" font-family="sans" font-size="10.00"> </text>
+<text text-anchor="start" x="85" y="-47.8" font-family="sans" font-weight="bold" font-size="14.00">↪ input</text>
+<text text-anchor="start" x="143" y="-47.8" font-family="sans" font-size="10.00"> </text>
+<text text-anchor="start" x="81" y="-28" font-family="monospace" font-size="10.00">data/external/example_participants.csv</text>
+<text text-anchor="start" x="319" y="-10" font-family="sans" font-size="10.00"> &#160;</text>
+<polygon fill="#acd957" stroke="#acd957" points="75,-62 75,-62 333,-62 333,-62 75,-62"/>
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+</g>
+<!-- 1 -->
+<g id="node2" class="node">
+<title>1</title>
+<text text-anchor="start" x="77" y="-221.6" font-family="sans" font-weight="bold" font-size="18.00">prepare_participants_csv</text>
+<text text-anchor="start" x="77" y="-197.8" font-family="sans" font-size="10.00"> </text>
+<text text-anchor="start" x="81" y="-197.8" font-family="sans" font-weight="bold" font-size="14.00">↪ input</text>
+<text text-anchor="start" x="139" y="-197.8" font-family="sans" font-size="10.00"> </text>
+<text text-anchor="start" x="77" y="-178" font-family="monospace" font-size="10.00">data/external/example_usernames.csv</text>
+<text text-anchor="start" x="251" y="-157.8" font-family="sans" font-size="10.00"> </text>
+<text text-anchor="start" x="255" y="-157.8" font-family="sans" font-weight="bold" font-size="14.00">output →</text>
+<text text-anchor="start" x="325" y="-157.8" font-family="sans" font-size="10.00"> </text>
+<text text-anchor="start" x="77" y="-138" font-family="monospace" font-size="10.00">data/external/example_participants.csv</text>
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+<polygon fill="none" stroke="#57d99e" stroke-width="2" points="71,-129 71,-241 335,-241 335,-129 71,-129"/>
+</g>
+<!-- 1&#45;&gt;0 -->
+<g id="edge1" class="edge">
+<title>1&#45;&gt;0</title>
+<path fill="none" stroke="grey" stroke-width="2" d="M203,-127.88C203,-119.48 203,-110.81 203,-102.42"/>
+<polygon fill="grey" stroke="grey" stroke-width="2" points="206.5,-102.36 203,-92.36 199.5,-102.36 206.5,-102.36"/>
+</g>
+<!-- 2 -->
+<g id="node3" class="node">
+<title>2</title>
+<text text-anchor="start" x="7" y="-367.6" font-family="sans" font-weight="bold" font-size="18.00">query_usernames_device_empatica_ids</text>
+<text text-anchor="start" x="7" y="-346" font-family="sans" font-size="10.00"> &#160;</text>
+<text text-anchor="start" x="321" y="-325.8" font-family="sans" font-size="10.00"> </text>
+<text text-anchor="start" x="325" y="-325.8" font-family="sans" font-weight="bold" font-size="14.00">output →</text>
+<text text-anchor="start" x="395" y="-325.8" font-family="sans" font-size="10.00"> </text>
+<text text-anchor="start" x="7" y="-306" font-family="monospace" font-size="10.00">data/external/example_usernames.csv</text>
+<text text-anchor="start" x="7" y="-288" font-family="monospace" font-size="10.00">data/external/timezone.csv</text>
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+</g>
+<!-- 2&#45;&gt;1 -->
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+<title>2&#45;&gt;1</title>
+<path fill="none" stroke="grey" stroke-width="2" d="M203,-277.63C203,-269.45 203,-260.93 203,-252.53"/>
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+</svg>

machine_learning/config/minimal_features.yaml

@@ -0,0 +1,5 @@
+grouping_variable: date_lj
+features:
+  proximity:
+    all
+participants_usernames: [nokia_0000003]

machine_learning/config/minimal_labels.yaml

@@ -0,0 +1,6 @@
+grouping_variable: date_lj
+labels:
+  PANAS:
+    - PA
+    - NA
+participants_usernames: [nokia_0000003]

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/features_sensor.py

@@ -0,0 +1,231 @@
+import datetime
+import warnings
+from pathlib import Path
+from typing import Collection
+
+import pandas as pd
+from pyprojroot import here
+
+import participants.query_db
+from features import communication, helper, proximity
+from machine_learning.helper import (
+    read_csv_with_settings,
+    safe_outer_merge_on_index,
+    to_csv_with_settings,
+)
+
+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:
+    """
+    A class to represent all sensor (AWARE) features.
+
+    Attributes
+    ----------
+    grouping_variable: str
+        The name of the variable by which to group (segment) data, e.g. date_lj.
+    features: dict
+        A dictionary of sensors (data types) and features to calculate.
+        See config/minimal_features.yaml for an example.
+    participants_usernames: Collection
+        A list of usernames for which to calculate features.
+        If None, use all participants.
+
+    Methods
+    -------
+    set_sensor_data():
+        Query the database for data types defined by self.features.
+    get_sensor_data(data_type): pd.DataFrame
+        Returns the dataframe of sensor data for specified data_type.
+    calculate_features():
+        Calls appropriate functions from features/ and joins them in a single dataframe, df_features_all.
+    get_features(data_type, feature_names): pd.DataFrame
+        Returns the dataframe of specified features for selected sensor.
+
+    construct_export_path():
+        Construct a path for exporting the features as csv files.
+    set_participants_label(label):
+        Sets a label for the usernames subset. This is used to distinguish feature exports.
+    """
+
+    def __init__(
+        self,
+        grouping_variable: str,
+        features: dict,
+        participants_usernames: Collection = None,
+    ) -> None:
+        """
+        Specifies the grouping variable and usernames for which to calculate features.
+        Sets other (implicit) attributes used in other methods.
+        If participants_usernames=None, this queries the usernames which belong to the main part of the study,
+            i.e. from 2020-08-01 on.
+
+        Parameters
+        ----------
+        grouping_variable: str
+            The name of the variable by which to group (segment) data, e.g. date_lj.
+        features: dict
+            A dictionary of sensors (data types) and features to calculate.
+            See config/minimal_features.yaml for an example.
+        participants_usernames: Collection
+            A list of usernames for which to calculate features.
+            If None, use all participants.
+
+        Returns
+        -------
+        None
+        """
+        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()
+
+        self.df_calls = pd.DataFrame()
+        self.df_sms = pd.DataFrame()
+        self.df_calls_sms = pd.DataFrame()
+
+        self.folder: Path = Path()
+        self.filename_prefix = ""
+        self.construct_export_path()
+        print("SensorFeatures initialized.")
+
+    def set_sensor_data(self) -> None:
+        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: str) -> pd.DataFrame:
+        if data_type == "proximity":
+            return self.df_proximity
+        elif data_type == "communication":
+            return self.df_calls_sms
+        else:
+            raise KeyError("This data type has not been implemented.")
+
+    def calculate_features(self, cached=True) -> None:
+        print("Calculating features ...")
+        if not self.participants_label:
+            raise ValueError(WARNING_PARTICIPANTS_LABEL)
+        self.df_features_all = pd.DataFrame()
+
+        if "proximity" in self.data_types:
+            try:
+                if not cached:  # Do not use the file, even if it exists.
+                    raise FileNotFoundError
+                self.df_proximity_counts = read_csv_with_settings(
+                    self.folder,
+                    self.filename_prefix,
+                    data_type="prox",
+                    grouping_variable=self.grouping_variable,
+                )
+                print("Read proximity features from the file.")
+            except FileNotFoundError:
+                # We need to recalculate the features in this case.
+                self.df_proximity_counts = proximity.count_proximity(
+                    self.df_proximity, self.grouping_variable
+                )
+                print("Calculated proximity features.")
+                to_csv_with_settings(
+                    self.df_proximity_counts,
+                    self.folder,
+                    self.filename_prefix,
+                    data_type="prox",
+                )
+            finally:
+                self.df_features_all = safe_outer_merge_on_index(
+                    self.df_features_all, self.df_proximity_counts
+                )
+
+        if "communication" in self.data_types:
+            try:
+                if not cached:  # Do not use the file, even if it exists.
+                    raise FileNotFoundError
+                self.df_calls_sms = read_csv_with_settings(
+                    self.folder,
+                    self.filename_prefix,
+                    data_type="comm",
+                    grouping_variable=self.grouping_variable,
+                )
+                print("Read communication features from the file.")
+            except FileNotFoundError:
+                # We need to recalculate the features in this case.
+                self.df_calls_sms = communication.calls_sms_features(
+                    df_calls=self.df_calls,
+                    df_sms=self.df_sms,
+                    group_by=self.grouping_variable,
+                )
+                print("Calculated communication features.")
+                to_csv_with_settings(
+                    self.df_calls_sms,
+                    self.folder,
+                    self.filename_prefix,
+                    data_type="comm",
+                )
+            finally:
+                self.df_features_all = safe_outer_merge_on_index(
+                    self.df_features_all, self.df_calls_sms
+                )
+
+        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":
+            if feature_names == "all":
+                feature_names = proximity.FEATURES_PROXIMITY
+            return self.df_proximity_counts[feature_names]
+        elif data_type == "communication":
+            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) -> None:
+        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) -> None:
+        self.participants_label = label
+        self.construct_export_path()

machine_learning/helper.py

@@ -0,0 +1,57 @@
+from pathlib import Path
+
+import pandas as pd
+
+
+def safe_outer_merge_on_index(left: pd.DataFrame, right: pd.DataFrame) -> pd.DataFrame:
+    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:
+    full_path = construct_full_path(folder, filename_prefix, data_type)
+    df.to_csv(
+        path_or_buf=full_path,
+        sep=",",
+        na_rep="NA",
+        header=True,
+        index=True,
+        encoding="utf-8",
+    )
+    print("Exported the dataframe to " + str(full_path))
+
+
+def read_csv_with_settings(
+    folder: Path, filename_prefix: str, data_type: str, grouping_variable: list
+) -> pd.DataFrame:
+    full_path = construct_full_path(folder, filename_prefix, data_type)
+    return pd.read_csv(
+        filepath_or_buffer=full_path,
+        sep=",",
+        header=0,
+        na_values="NA",
+        encoding="utf-8",
+        index_col=(["participant_id"] + grouping_variable),
+        parse_dates=True,
+        infer_datetime_format=True,
+        cache_dates=True,
+    )
+
+
+def construct_full_path(folder: Path, filename_prefix: str, data_type: str) -> Path:
+    export_filename = filename_prefix + "_" + data_type + ".csv"
+    full_path = folder / export_filename
+    return full_path

machine_learning/labels.py

@@ -0,0 +1,135 @@
+import datetime
+import warnings
+from pathlib import Path
+from typing import Collection
+
+import pandas as pd
+from pyprojroot import here
+
+import participants.query_db
+from features import esm
+from machine_learning import QUESTIONNAIRE_IDS, QUESTIONNAIRE_IDS_RENAME
+from machine_learning.helper import read_csv_with_settings, to_csv_with_settings
+
+WARNING_PARTICIPANTS_LABEL = (
+    "Before aggregating labels, 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 Labels:
+    def __init__(
+        self,
+        grouping_variable: str,
+        labels: dict,
+        participants_usernames: Collection = None,
+    ) -> None:
+        self.grouping_variable_name = grouping_variable
+        self.grouping_variable = [grouping_variable]
+
+        self.questionnaires = labels.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_esm = pd.DataFrame()
+        self.df_esm_preprocessed = pd.DataFrame()
+        self.df_esm_interest = pd.DataFrame()
+        self.df_esm_clean = pd.DataFrame()
+
+        self.df_esm_means = pd.DataFrame()
+
+        self.folder: Path = Path()
+        self.filename_prefix = ""
+        self.construct_export_path()
+        print("Labels initialized.")
+
+    def set_labels(self) -> None:
+        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[
+                (
+                    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)
+        print("ESM data cleaned.")
+
+    def get_labels(self, questionnaire: str) -> pd.DataFrame:
+        if questionnaire == "PANAS":
+            return self.df_esm_clean
+        else:
+            raise KeyError("This questionnaire has not been implemented as a label.")
+
+    def aggregate_labels(self, cached=True) -> None:
+        print("Aggregating labels ...")
+        if not self.participants_label:
+            raise ValueError(WARNING_PARTICIPANTS_LABEL)
+
+        try:
+            if not cached:  # Do not use the file, even if it exists.
+                raise FileNotFoundError
+            self.df_esm_means = read_csv_with_settings(
+                self.folder,
+                self.filename_prefix,
+                data_type="_".join(self.questionnaires),
+                grouping_variable=self.grouping_variable,
+            )
+            print("Read labels from the file.")
+        except FileNotFoundError:
+            # We need to recalculate the features in this case.
+            self.df_esm_means = (
+                self.df_esm_clean.groupby(
+                    ["participant_id", "questionnaire_id"] + self.grouping_variable
+                )
+                .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.")
+            to_csv_with_settings(
+                self.df_esm_means,
+                self.folder,
+                self.filename_prefix,
+                data_type="_".join(self.questionnaires),
+            )
+
+    def get_aggregated_labels(self) -> pd.DataFrame:
+        return self.df_esm_means
+
+    def construct_export_path(self) -> None:
+        if not self.participants_label:
+            warnings.warn(WARNING_PARTICIPANTS_LABEL, UserWarning)
+        self.folder = here("machine_learning/intermediate_results/labels", warn=True)
+        self.filename_prefix = (
+            self.participants_label + "_" + self.grouping_variable_name
+        )
+
+    def set_participants_label(self, label: str) -> None:
+        self.participants_label = label
+        self.construct_export_path()

machine_learning/model.py

@@ -0,0 +1,47 @@
+from sklearn.model_selection import LeaveOneGroupOut, cross_val_score
+
+
+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",
+        )

machine_learning/pipeline.py

@@ -1,125 +1,32 @@
-import datetime
+import numpy as np
+import yaml
+from sklearn import linear_model
 
-import pandas as pd
-from sklearn.model_selection import cross_val_score
+from machine_learning.features_sensor import SensorFeatures
+from machine_learning.labels import Labels
+from machine_learning.model import ModelValidation
 
-import participants.query_db
-from features import esm, helper, proximity
-from machine_learning import QUESTIONNAIRE_IDS, QUESTIONNAIRE_IDS_RENAME
+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()
 
-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")
-            )
-        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_proximity = 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 get_sensor_data(self):
-        if "proximity" in self.data_types:
-            self.df_proximity = proximity.get_proximity_data(
-                self.participants_usernames
-            )
-            self.df_proximity = helper.get_date_from_timestamp(self.df_proximity)
-            self.df_proximity = proximity.recode_proximity(self.df_proximity)
-
-    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,
+    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))

participants/prepare_usernames_file.py

@@ -0,0 +1,69 @@
+import datetime
+import os
+import sys
+
+nb_dir = os.path.split(os.getcwd())[0]
+if nb_dir not in sys.path:
+    sys.path.append(nb_dir)
+
+import pandas as pd
+from features.timezone import get_timezone_data
+from pyprojroot import here
+
+import participants.query_db
+
+participants_inactive_usernames = participants.query_db.get_usernames(
+    tester=False,  # True participants are wanted.
+    active=False,  # They have all finished their participation.
+    collection_start=datetime.date.fromisoformat(
+        "2020-08-01"
+    ),  # This is the timeframe of the main study.
+    last_upload=datetime.date.fromisoformat("2021-09-01"),
+)
+
+participants_overview_si = pd.read_csv(
+    snakemake.params["baseline_folder"] + "Participants_overview_Slovenia.csv", sep=";"
+)
+participants_overview_be = pd.read_csv(
+    snakemake.params["baseline_folder"]+ "Participants_overview_Belgium.csv", sep=";"
+)
+
+participants_true_si = participants_overview_si[
+    participants_overview_si["Wristband_SerialNo"] != "DECLINED"
+]
+participants_true_be = participants_overview_be[
+    participants_overview_be["SmartphoneBrand+Generation"].str.slice(0, 3) != "Not"
+]
+
+# Concatenate participants from both countries.
+participants_usernames_empatica = pd.concat(
+    [participants_true_be, participants_true_si]
+)
+# Filter only the participants from the main study (queried from the database).
+participants_usernames_empatica = participants_usernames_empatica[
+    participants_usernames_empatica["Username"].isin(participants_inactive_usernames)
+]
+# Rename and select columns.
+participants_usernames_empatica = participants_usernames_empatica.rename(
+    columns={"Username": "label", "Wristband_SerialNo": "empatica_id"}
+)[["label", "empatica_id"]]
+# Adapt for csv export.
+participants_usernames_empatica["empatica_id"] = participants_usernames_empatica[
+    "empatica_id"
+].str.replace(",", ";")
+
+participants_usernames_empatica.to_csv(
+    snakemake.output["usernames_file"],
+    header=True,
+    index=False,
+    line_terminator="\n",
+)
+
+timezone_df = get_timezone_data(participants_inactive_usernames)
+
+timezone_df.to_csv(
+    snakemake.output["timezone_file"],
+    header=True,
+    index=False,
+    line_terminator="\n",
+)

rapids

@@ -0,0 +1 @@
+Subproject commit f78aa3e7b3567423b44045766b230cd60d557cb0

statistical_analysis/adherence.py

@@ -6,7 +6,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.11.4
+#       jupytext_version: 1.13.0
 #   kernelspec:
 #     display_name: straw2analysis
 #     language: python
@@ -14,25 +14,7 @@
 # ---
 
 # %%
-# %matplotlib inline
-import datetime
-import os
-import sys
-
-import matplotlib.pyplot as plt
-import pandas as pd
-import seaborn as sns
-import statsmodels.api as sm
-import statsmodels.formula.api as smf
-
-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.esm import *
-
-# %%
-SAVE_FIGS = True
+SAVE_FIGS = False
 FIG_HEIGHT = 5
 FIG_ASPECT = 1.7
 FIG_COLOUR = "#28827C"
@@ -96,13 +78,41 @@ df_session_counts_time = classify_sessions_by_completion_time(df_esm_preprocesse
 # Sessions are now classified according to the type of a session (a true questionnaire or simple single questions) and users response.
 
 # %%
-df_session_counts_time
+df_session_counts_time["session_response_cat"] = df_session_counts_time[
+    "session_response"
+].astype("category")
+df_session_counts_time["session_response_cat"] = df_session_counts_time[
+    "session_response_cat"
+].cat.remove_categories(
+    ["during_work_first", "ema_unanswered", "evening_first", "morning", "morning_first"]
+)
+df_session_counts_time["session_response_cat"] = df_session_counts_time[
+    "session_response_cat"
+].cat.add_categories("interrupted")
+df_session_counts_time.loc[
+    df_session_counts_time["session_response_cat"].isna(), "session_response_cat"
+] = "interrupted"
+# df_session_counts_time["session_response_cat"] = df_session_counts_time["session_response_cat"].cat.rename_categories({
+#    "ema_unanswered": "interrupted",
+#    "morning_first": "interrupted",
+#    "evening_first": "interrupted",
+#    "morning": "interrupted",
+#    "during_work_first": "interrupted"})
+
+# %%
+df_session_counts_time.session_response_cat
 
 # %%
 tbl_session_outcomes = df_session_counts_time.reset_index()[
-    "session_response"
+    "session_response_cat"
 ].value_counts()
 
+# %%
+tbl_session_outcomes_relative = tbl_session_outcomes / len(df_session_counts_time)
+
+# %%
+print(tbl_session_outcomes_relative.to_latex(escape=True))
+
 # %%
 print("All sessions:", len(df_session_counts_time))
 print("-------------------------------------")

test/test_communication.py

@@ -88,6 +88,5 @@ class CallsFeatures(unittest.TestCase):
         self.features_call_sms = calls_sms_features(self.calls, self.sms)
         self.assertIsInstance(self.features_call_sms, pd.DataFrame)
         self.assertCountEqual(
-            self.features_call_sms.columns.to_list(),
-            FEATURES_CALLS + FEATURES_SMS + FEATURES_CONTACT,
+            self.features_call_sms.columns.to_list(), FEATURES_CALLS_SMS_ALL
         )

test/test_esm.py

@@ -1,6 +1,7 @@
 import unittest
 
 from pandas.testing import assert_series_equal
+from pyprojroot import here
 
 from features.esm import *
 from features.esm_JCQ import *
@@ -9,7 +10,7 @@ from features.esm_JCQ import *
 class EsmFeatures(unittest.TestCase):
     @classmethod
     def setUpClass(cls) -> None:
-        cls.esm = pd.read_csv("../data/example_esm.csv", sep=";")
+        cls.esm = pd.read_csv(here("data/example_esm.csv"), sep=";")
         cls.esm["esm_json"] = cls.esm["esm_json"].apply(eval)
         cls.esm_processed = preprocess_esm(cls.esm)
         cls.esm_clean = clean_up_esm(cls.esm_processed)

test/test_features_sensor.py

@@ -0,0 +1,27 @@
+import unittest
+
+import yaml
+from pyprojroot import here
+
+from machine_learning.features_sensor import *
+
+
+class SensorFeaturesTest(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls) -> None:
+        with open(here("machine_learning/config/minimal_features.yaml"), "r") as file:
+            cls.sensor_features_params = yaml.safe_load(file)
+
+    def test_yaml(self):
+        with open(here("machine_learning/config/minimal_features.yaml"), "r") as file:
+            sensor_features_params = yaml.safe_load(file)
+        self.assertIsInstance(sensor_features_params, dict)
+        self.assertIsInstance(sensor_features_params.get("grouping_variable"), str)
+        self.assertIsInstance(sensor_features_params.get("features"), dict)
+        self.assertIsInstance(
+            sensor_features_params.get("participants_usernames"), list
+        )
+
+    def test_participants_label(self):
+        sensor_features = SensorFeatures(**self.sensor_features_params)
+        self.assertRaises(ValueError, sensor_features.calculate_features)

test/test_proximity.py

@@ -1,5 +1,7 @@
 import unittest
 
+from pyprojroot import here
+
 from features.proximity import *
 
 
@@ -10,7 +12,7 @@ class ProximityFeatures(unittest.TestCase):
 
     @classmethod
     def setUpClass(cls) -> None:
-        cls.df_proximity = pd.read_csv("../data/example_proximity.csv")
+        cls.df_proximity = pd.read_csv(here("data/example_proximity.csv"))
         cls.df_proximity["participant_id"] = 99
 
     def test_recode_proximity(self):