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stress_at_work_analysis
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9
.flake8
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@ -1,9 +0,0 @@
[flake8]
max-line-length = 88
extend-ignore =
E203,
# E501 line too long for docstrings
D501
per-file-ignores =
exploration/*.py:E501
docstring-convention = numpy

19
.gitignore vendored
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@ -5,22 +5,3 @@ __pycache__/
/exploration/*.ipynb
/config/*.ipynb
/statistical_analysis/*.ipynb
/presentation/*.ipynb
/machine_learning/intermediate_results/
/data/features/
/data/baseline/
/data/*input*.csv
/data/daily*
/data/intradaily*
/data/raw
/data/stressfulness_event*
/data/30min*
/presentation/*scores.csv
/presentation/Results.ods
/presentation/results/
.Rproj.user
.Rhistory
/presentation/*.nb.html
presentation/event_stressful_detection_half_loso.csv
presentation/event_stressful_detection_loso.csv
/statistical_analysis/scale_reliability.nb.html

4
.gitmodules vendored
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[submodule "rapids"]
path = rapids
url = https://repo.ijs.si/junoslukan/rapids.git
branch = master

6
.idea/codeStyles/Project.xml
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<component name="ProjectCodeStyleConfiguration">
<code_scheme name="Project" version="173">
<option name="RIGHT_MARGIN" value="150" />
<option name="SOFT_MARGINS" value="88" />
</code_scheme>
</component>

5
.idea/codeStyles/codeStyleConfig.xml
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<component name="ProjectCodeStyleConfiguration">
<state>
<option name="USE_PER_PROJECT_SETTINGS" value="true" />
</state>
</component>

3
.idea/dictionaries/junos.xml
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<component name="ProjectDictionaryState">
<dictionary name="junos" />
</component>

26
.idea/misc.xml
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectRootManager" version="2" project-jdk-name="straw2analysis" project-jdk-type="Python SDK" />
<component name="PyCharmDSProjectLayout">
<option name="id" value="JupyterRightHiddenStructureLayout" />
</component>
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (straw2analysis)" project-jdk-type="Python SDK" />
<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>
<entry key="file://$PROJECT_DIR$/statistical_analysis/scale_reliability.rmd">
<value>
<RMarkdownRenderProfile>
<option name="lastOutput" value="$PROJECT_DIR$/statistical_analysis/scale_reliability.nb.html" />
<option name="outputDirectoryUrl" value="file://$PROJECT_DIR$/statistical_analysis" />
</RMarkdownRenderProfile>
</value>
</entry>
</map>
</option>
</component>
</project>

9
.idea/rGraphicsSettings.xml
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="RGraphicsSettings">
<option name="height" value="600" />
<option name="resolution" value="75" />
<option name="version" value="2" />
<option name="width" value="960" />
</component>
</project>

7
.idea/rMarkdownGraphicsSettings.xml
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@ -1,7 +0,0 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="RMarkdownGraphicsSettings">
<option name="globalResolution" value="75" />
<option name="version" value="2" />
</component>
</project>

6
.idea/rSettings.xml
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@ -1,6 +0,0 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="RSettings">
<option name="interpreterPath" value="C:\Program Files\R\R-4.3.1\bin\R.exe" />
</component>
</project>

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

2
.idea/straw2analysis.iml
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@ -5,7 +5,7 @@
<excludeFolder url="file://$MODULE_DIR$/config/.ipynb_checkpoints" />
<excludeFolder url="file://$MODULE_DIR$/exploration/.ipynb_checkpoints" />
</content>
<orderEntry type="jdk" jdkName="straw2analysis" jdkType="Python SDK" />
<orderEntry type="jdk" jdkName="Python 3.9 (straw2analysis)" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
<component name="PyDocumentationSettings">

1
.idea/vcs.xml
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@ -2,6 +2,5 @@
<project version="4">
<component name="VcsDirectoryMappings">
<mapping directory="$PROJECT_DIR$" vcs="Git" />
<mapping directory="$PROJECT_DIR$/rapids" vcs="Git" />
</component>
</project>

30
.pre-commit-config.yaml
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@ -1,30 +0,0 @@
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.4.0
hooks:
- id: check-yaml
- id: end-of-file-fixer
- id: trailing-whitespace
- repo: https://github.com/pycqa/isort
rev: 5.12.0
hooks:
- id: isort
name: isort (python)
- repo: https://github.com/psf/black
rev: 23.3.0
hooks:
- id: black
language_version: python3
- repo: https://github.com/pycqa/flake8
rev: 6.0.0
hooks:
- id: flake8
# - repo: https://github.com/mwouts/jupytext
# rev: v1.14.7
# hooks:
# - id: jupytext
# args: [ --from, "py:percent", --to, "ipynb" ]
# additional_dependencies:
# - isort==5.12.0 # Matches hook
# - black==23.3.0
# - flake8==6.0.0

128
README.md
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@ -27,135 +27,9 @@ To install:
ipython kernel install --user --name=straw2analysis
```
2. Provide a file called `.env` to be used by `python-dotenv` which should be placed in the top folder of the application
2. Provide an .env file 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"))'
```

12
config/environment.yml
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@ -1,28 +1,22 @@
name: straw2analysis
channels:
- defaults
- conda-forge
dependencies:
- python=3.11
- python=3.9
- black
- isort
- flake8
- flake8-docstrings
- imbalanced-learn=0.10.0
- jupyterlab
- jupytext
- lightgbm
- mypy
- nodejs
- pandas
- psycopg2 >= 2.9.1
- pre-commit
- psycopg2
- python-dotenv
- pytz
- pyprojroot
- pyyaml
- seaborn
- scikit-learn
- sqlalchemy
- statsmodels
- tabulate
- xgboost

75
config/models.py
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@ -166,43 +166,12 @@ 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.)
@ -288,19 +257,6 @@ 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)
@ -420,43 +376,12 @@ 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"

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data/app_categories.csv
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data/input_PANAS_NA.csv
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data/input_PANAS_negative_affect_mean.csv
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data/z_input_PANAS_NA.csv
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exploration/all_sensors_sequential_addition_scores.xlsx
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337
exploration/debug_heatmap.py
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@ -1,337 +0,0 @@
# ---
# 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 pandas as pd
from rapids.src.features.utils.utils import chunk_episodes
# %%
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#", "#", regex=True)
time_segments_labels["label"] = time_segments_labels["label"].str.replace(
r"_RR\d+SS$", "", regex=True
)
# %% tags=[]
def filter_data_by_segment(data, time_segment_current):
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 = (
r"[0-9]{4}[\-|\/][0-9]{2}[\-|\/][0-9]{2} [0-9]{2}:[0-9]{2}:[0-9]{2}"
)
timestamps_regex = r"[0-9]{13}"
segment_regex = r"\[({}#{},{};{},{})\]".format(
time_segment_current,
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 get_data_for_plot(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 = get_data_for_plot(phone_data_yield_per_segment)
# %%

1178
exploration/ex_ml_pipeline.py
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28
exploration/expl_app_categories.py
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@ -6,7 +6,7 @@
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.13.0
# jupytext_version: 1.11.4
# kernelspec:
# display_name: straw2analysis
# language: python
@ -74,29 +74,3 @@ 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)
# %%

299
exploration/expl_esm_labels.py
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@ -7,7 +7,7 @@
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.14.5
# jupytext_version: 1.11.2
# kernelspec:
# display_name: straw2analysis
# language: python
@ -15,33 +15,17 @@
# ---
# %%
import datetime
import os
import sys
import seaborn as sns
nb_dir = os.path.split(os.getcwd())[0]
if nb_dir not in sys.path:
sys.path.append(nb_dir)
import participants.query_db
from features.esm import (
QUESTIONNAIRE_IDS,
clean_up_esm,
get_esm_data,
increment_answers,
preprocess_esm,
reassign_question_ids,
)
from features.esm_COPE import DICT_COPE_QUESTION_IDS
from features.esm_JCQ import reverse_jcq_demand_control_scoring
from features.esm_SAM import DICT_SAM_QUESTION_IDS, extract_stressful_events
# import os
# import sys
# nb_dir = os.path.split(os.getcwd())[0]
# if nb_dir not in sys.path:
# sys.path.append(nb_dir)
# %%
save_figs = False
export_data = True
from features.esm import *
from features.esm_JCQ import *
# %%
participants_inactive_usernames = participants.query_db.get_usernames(
@ -57,14 +41,8 @@ df_esm_preprocessed = preprocess_esm(df_esm_inactive)
# %%
df_esm_PANAS = df_esm_preprocessed[
(
df_esm_preprocessed["questionnaire_id"]
== QUESTIONNAIRE_IDS["PANAS_positive_affect"]
)
| (
df_esm_preprocessed["questionnaire_id"]
== QUESTIONNAIRE_IDS["PANAS_negative_affect"]
)
(df_esm_preprocessed["questionnaire_id"] == 8)
| (df_esm_preprocessed["questionnaire_id"] == 9)
]
df_esm_PANAS_clean = clean_up_esm(df_esm_PANAS)
@ -85,47 +63,35 @@ df_esm_PANAS_daily_means = (
# %%
df_esm_PANAS_summary_participant = (
df_esm_PANAS_daily_means.groupby(["participant_id", "questionnaire_id"])
.esm_numeric_mean.agg(["mean", "median", "std"])
.agg(["mean", "median", "std"])
.reset_index(col_level=1)
)
df_esm_PANAS_summary_participant.columns = df_esm_PANAS_summary_participant.columns.get_level_values(
1
)
df_esm_PANAS_summary_participant[
"PANAS subscale"
"PANAS_subscale"
] = df_esm_PANAS_daily_means.questionnaire_id.astype("category").cat.rename_categories(
{8.0: "positive affect", 9.0: "negative affect"}
{8.0: "PA", 9.0: "NA"}
)
# %%
df_esm_PANAS_summary_participant.groupby("PANAS subscale").describe()["mean"]
# %%
df_esm_PANAS_summary_participant.groupby("PANAS subscale").describe()["std"]
# %%
df_esm_PANAS_summary_participant.query("std == 0")
# %%
fig1 = sns.displot(
data=df_esm_PANAS_summary_participant, x="mean", hue="PANAS subscale", binwidth=0.2
sns.displot(
data=df_esm_PANAS_summary_participant, x="mean", hue="PANAS_subscale", binwidth=0.2
)
fig1.set_axis_labels(x_var="participant mean", y_var="frequency")
if save_figs:
fig1.figure.savefig("PANAS_mean_participant.pdf", dpi=300)
# %%
sns.displot(
data=df_esm_PANAS_summary_participant,
x="median",
hue="PANAS subscale",
hue="PANAS_subscale",
binwidth=0.2,
)
# %%
fig2 = sns.displot(
data=df_esm_PANAS_summary_participant, x="std", hue="PANAS subscale", binwidth=0.05
sns.displot(
data=df_esm_PANAS_summary_participant, x="std", hue="PANAS_subscale", binwidth=0.05
)
fig2.set_axis_labels(x_var="participant standard deviation", y_var="frequency")
if save_figs:
fig2.figure.savefig("PANAS_std_participant.pdf", dpi=300)
# %%
df_esm_PANAS_summary_participant[df_esm_PANAS_summary_participant["std"] < 0.1]
@ -133,22 +99,10 @@ 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"]
>= QUESTIONNAIRE_IDS["appraisal_stressfulness_event"]
)
& (
df_esm_preprocessed["questionnaire_id"]
<= QUESTIONNAIRE_IDS["appraisal_stressfulness_period"]
)
(df_esm_preprocessed["questionnaire_id"] >= 87)
& (df_esm_preprocessed["questionnaire_id"] <= 93)
]
df_esm_SAM_clean = clean_up_esm(df_esm_SAM)
@ -156,10 +110,9 @@ df_esm_SAM_clean = clean_up_esm(df_esm_SAM)
# ## Stressful events
# %%
df_esm_SAM_event = df_esm_SAM_clean[
df_esm_SAM_clean["questionnaire_id"]
== QUESTIONNAIRE_IDS["appraisal_stressfulness_event"]
].assign(stressful_event=lambda x: (x.esm_user_answer_numeric > 0))
df_esm_SAM_event = df_esm_SAM_clean[df_esm_SAM_clean["questionnaire_id"] == 87].assign(
stressful_event=lambda x: (x.esm_user_answer_numeric > 0)
)
# %%
df_esm_SAM_daily_events = (
@ -170,22 +123,20 @@ df_esm_SAM_daily_events = (
)
# %% [markdown]
# Calculate the daily mean of YES (1) or NO (0) answers to the question about stressful events. This is then the daily ratio of EMA sessions that included a stressful event.
# Calculate the daily mean of YES (1) or NO (0) answers to the question about a stressful events. This is then the daily ratio of EMA sessions that included a stressful event.
# %%
df_esm_SAM_event_summary_participant = (
df_esm_SAM_daily_events.groupby(["participant_id"])
.SAM_event_ratio.agg(["mean", "median", "std"])
.agg(["mean", "median", "std"])
.reset_index(col_level=1)
)
df_esm_SAM_event_summary_participant.columns = df_esm_SAM_event_summary_participant.columns.get_level_values(
1
)
# %%
fig6 = sns.displot(data=df_esm_SAM_event_summary_participant, x="mean", binwidth=0.1)
fig6.set_axis_labels(
x_var="participant proportion of stressful events", y_var="frequency"
)
if save_figs:
fig6.figure.savefig("SAM_events_mean_participant.pdf", dpi=300)
sns.displot(data=df_esm_SAM_event_summary_participant, x="mean", binwidth=0.1)
# %%
sns.displot(data=df_esm_SAM_event_summary_participant, x="std", binwidth=0.05)
@ -196,12 +147,7 @@ sns.displot(data=df_esm_SAM_event_summary_participant, x="std", binwidth=0.05)
# %% [markdown]
# * Example of threat: "Did this event make you feel anxious?"
# * Example of challenge: "How eager are you to tackle this event?"
# * Possible answers:
# 0 - Not at all,
# 1 - Slightly,
# 2 - Moderately,
# 3 - Considerably,
# 4 - Extremely
# * Possible answers: 0 - Not at all, 1 - Slightly, 2 - Moderately, 3 - Considerably, 4 - Extremely
# %%
df_esm_SAM_daily = (
@ -213,45 +159,27 @@ df_esm_SAM_daily = (
# %%
df_esm_SAM_daily_threat_challenge = df_esm_SAM_daily[
(df_esm_SAM_daily["questionnaire_id"] == QUESTIONNAIRE_IDS["appraisal_threat"])
| (df_esm_SAM_daily["questionnaire_id"] == QUESTIONNAIRE_IDS["appraisal_challenge"])
(df_esm_SAM_daily["questionnaire_id"] == 88)
| (df_esm_SAM_daily["questionnaire_id"] == 89)
]
# %%
df_esm_SAM_summary_participant = (
df_esm_SAM_daily.groupby(["participant_id", "questionnaire_id"])
.esm_numeric_mean.agg(["mean", "median", "std"])
.agg(["mean", "median", "std"])
.reset_index(col_level=1)
)
# %%
df_esm_SAM_event_stressfulness_summary_participant = df_esm_SAM_summary_participant[
df_esm_SAM_summary_participant["questionnaire_id"]
== QUESTIONNAIRE_IDS["appraisal_stressfulness_event"]
]
df_esm_SAM_event_stressfulness_summary_participant.describe()["mean"]
# %%
df_esm_SAM_event_stressfulness_summary_participant.describe()["std"]
# %%
sns.displot(
data=df_esm_SAM_event_stressfulness_summary_participant, x="mean", binwidth=0.2
df_esm_SAM_summary_participant.columns = df_esm_SAM_summary_participant.columns.get_level_values(
1
)
# %%
df_esm_SAM_threat_challenge_summary_participant = df_esm_SAM_summary_participant[
(
df_esm_SAM_summary_participant["questionnaire_id"]
== QUESTIONNAIRE_IDS["appraisal_threat"]
)
| (
df_esm_SAM_summary_participant["questionnaire_id"]
== QUESTIONNAIRE_IDS["appraisal_challenge"]
)
(df_esm_SAM_summary_participant["questionnaire_id"] == 88)
| (df_esm_SAM_summary_participant["questionnaire_id"] == 89)
]
df_esm_SAM_threat_challenge_summary_participant[
"event subscale"
"event_subscale"
] = df_esm_SAM_threat_challenge_summary_participant.questionnaire_id.astype(
"category"
).cat.rename_categories(
@ -262,84 +190,26 @@ df_esm_SAM_threat_challenge_summary_participant[
sns.displot(
data=df_esm_SAM_threat_challenge_summary_participant,
x="mean",
hue="event subscale",
hue="event_subscale",
binwidth=0.2,
)
# %%
fig3 = sns.displot(
sns.displot(
data=df_esm_SAM_threat_challenge_summary_participant,
x="std",
hue="event subscale",
hue="event_subscale",
binwidth=0.1,
)
fig3.set_axis_labels(x_var="participant standard deviation", y_var="frequency")
if save_figs:
fig3.figure.savefig("SAM_std_participant.pdf", dpi=300)
# %%
df_esm_SAM_threat_challenge_summary_participant.groupby("event subscale").describe()[
"mean"
]
# %%
df_esm_SAM_threat_challenge_summary_participant.groupby("event subscale").describe()[
"std"
]
# %%
df_esm_SAM_clean.columns
# %%
df_esm_SAM_clean.esm_status.value_counts()
# %%
if export_data:
df_esm_SAM_fixed = reassign_question_ids(df_esm_SAM_clean, DICT_SAM_QUESTION_IDS)
df_esm_SAM_fixed = increment_answers(df_esm_SAM_fixed)
df_esm_SAM_for_export = df_esm_SAM_fixed[
[
"participant_id",
"username",
"device_id",
"_id",
"esm_trigger",
"esm_session",
"esm_notification_id",
"question_id",
"questionnaire_id",
"esm_instructions",
"double_esm_user_answer_timestamp",
"datetime_lj",
"date_lj",
"time",
"esm_user_answer",
"esm_user_answer_numeric",
]
]
df_esm_SAM_for_export.sort_values(
by=["participant_id", "device_id", "_id"], ignore_index=True, inplace=True
)
print(df_esm_SAM_for_export.head())
df_esm_SAM_for_export.to_csv(
"../data/raw/df_esm_SAM_threat_challenge.csv", index=False
)
# %% [markdown]
# ## Stressfulness of period
# %%
df_esm_SAM_period_summary_participant = df_esm_SAM_summary_participant[
df_esm_SAM_summary_participant["questionnaire_id"]
== QUESTIONNAIRE_IDS["appraisal_stressfulness_period"]
df_esm_SAM_summary_participant["questionnaire_id"] == 93
]
# %%
df_esm_SAM_period_summary_participant.describe()["mean"]
# %%
df_esm_SAM_period_summary_participant.describe()["std"]
# %%
sns.displot(data=df_esm_SAM_period_summary_participant, x="mean", binwidth=0.2)
@ -351,8 +221,8 @@ sns.displot(data=df_esm_SAM_period_summary_participant, x="std", binwidth=0.1)
# %%
df_esm_JCQ_demand_control = df_esm_preprocessed[
(df_esm_preprocessed["questionnaire_id"] >= QUESTIONNAIRE_IDS["JCQ_job_demand"])
& (df_esm_preprocessed["questionnaire_id"] <= QUESTIONNAIRE_IDS["JCQ_job_control"])
(df_esm_preprocessed["questionnaire_id"] >= 10)
& (df_esm_preprocessed["questionnaire_id"] <= 11)
]
df_esm_JCQ_demand_control_clean = clean_up_esm(df_esm_JCQ_demand_control)
@ -372,11 +242,14 @@ df_esm_JCQ_daily = (
)
df_esm_JCQ_summary_participant = (
df_esm_JCQ_daily.groupby(["participant_id", "questionnaire_id"])
.esm_score_mean.agg(["mean", "median", "std"])
.agg(["mean", "median", "std"])
.reset_index(col_level=1)
)
df_esm_JCQ_summary_participant.columns = df_esm_JCQ_summary_participant.columns.get_level_values(
1
)
df_esm_JCQ_summary_participant[
"JCQ subscale"
"JCQ_subscale"
] = df_esm_JCQ_summary_participant.questionnaire_id.astype(
"category"
).cat.rename_categories(
@ -384,71 +257,11 @@ df_esm_JCQ_summary_participant[
)
# %%
df_esm_JCQ_summary_participant.groupby("JCQ subscale").describe()["mean"]
# %%
df_esm_JCQ_summary_participant.groupby("JCQ subscale").describe()["std"]
# %%
fig4 = sns.displot(
data=df_esm_JCQ_summary_participant,
x="mean",
hue="JCQ subscale",
binwidth=0.1,
sns.displot(
data=df_esm_JCQ_summary_participant, x="mean", hue="JCQ_subscale", binwidth=0.1,
)
fig4.set_axis_labels(x_var="participant mean", y_var="frequency")
if save_figs:
fig4.figure.savefig("JCQ_mean_participant.pdf", dpi=300)
# %%
fig5 = sns.displot(
data=df_esm_JCQ_summary_participant,
x="std",
hue="JCQ subscale",
binwidth=0.05,
sns.displot(
data=df_esm_JCQ_summary_participant, x="std", hue="JCQ_subscale", binwidth=0.05,
)
fig6.set_axis_labels(x_var="participant standard deviation", y_var="frequency")
if save_figs:
fig5.figure.savefig("JCQ_std_participant.pdf", dpi=300)
# %% [markdown]
# # COPE Inventory
# %%
df_esm_COPE = df_esm_preprocessed[
(df_esm_preprocessed["questionnaire_id"] >= QUESTIONNAIRE_IDS["COPE_active"])
& (df_esm_preprocessed["questionnaire_id"] <= QUESTIONNAIRE_IDS["COPE_emotions"])
]
# %%
df_esm_COPE_clean = clean_up_esm(df_esm_COPE)
df_esm_COPE_clean = increment_answers(df_esm_COPE_clean)
df_esm_COPE_fixed = reassign_question_ids(df_esm_COPE_clean, DICT_COPE_QUESTION_IDS)
# %%
if export_data:
df_esm_COPE_for_export = df_esm_COPE_fixed[
[
"participant_id",
"username",
"device_id",
"_id",
"esm_trigger",
"esm_session",
"esm_notification_id",
"question_id",
"questionnaire_id",
"esm_instructions",
"double_esm_user_answer_timestamp",
"datetime_lj",
"date_lj",
"time",
"esm_user_answer",
"esm_user_answer_numeric",
]
]
df_esm_COPE_for_export.sort_values(
by=["participant_id", "device_id", "_id"], ignore_index=True, inplace=True
)
print(df_esm_COPE_for_export.head())
df_esm_COPE_for_export.to_csv("../data/raw/df_esm_COPE.csv", index=False)

318
exploration/expl_features_analysis.py
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@ -1,318 +0,0 @@
# ---
# 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": false, "outputs_hidden": false}
# %matplotlib inline
import os, sys, math
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree
from sklearn.impute import SimpleImputer
from sklearn.model_selection import train_test_split
# %% jupyter={"source_hidden": false, "outputs_hidden": false} nteract={"transient": {"deleting": false}}
def calc_entropy(column):
"""
Calculate entropy given a pandas series, list, or numpy array.
"""
# Compute the counts of each unique value in the column
counts = np.bincount(column)
# Divide by the total column length to get a probability
probabilities = counts / len(column)
# Initialize the entropy to 0
entropy = 0
# Loop through the probabilities, and add each one to the total entropy
for prob in probabilities:
if prob > 0:
# use log from math and set base to 2
entropy += prob * math.log(prob, 2)
return -entropy
def calc_information_gain(data, split_name, target_name):
"""
Calculate information gain given a data set, column to split on, and target
"""
# Calculate the original entropy
original_entropy = calc_entropy(data[target_name])
#Find the unique values in the column
values = data[split_name].unique()
# Make two subsets of the data, based on the unique values
left_split = data[data[split_name] == values[0]]
right_split = data[data[split_name] == values[1]]
# Loop through the splits and calculate the subset entropies
to_subtract = 0
for subset in [left_split, right_split]:
prob = (subset.shape[0] / data.shape[0])
to_subtract += prob * calc_entropy(subset[target_name])
# Return information gain
return original_entropy - to_subtract
def get_information_gains(data, target_name):
#Intialize an empty dictionary for information gains
information_gains = {}
#Iterate through each column name in our list
for col in list(data.columns):
#Find the information gain for the column
information_gain = calc_information_gain(data, col, target_name)
#Add the information gain to our dictionary using the column name as the ekey
information_gains[col] = information_gain
#Return the key with the highest value
#return max(information_gains, key=information_gains.get)
return information_gains
def n_features_with_highest_info_gain(info_gain_dict, n=None):
"""
Get n-features that have highest information gain
"""
if n is None:
n = len(info_gain_dict)
import heapq
n_largest = heapq.nlargest(n, info_gain_dict.items(), key=lambda i: i[1])
return {feature[0]: feature[1] for feature in n_largest}
# %% jupyter={"source_hidden": false, "outputs_hidden": false} nteract={"transient": {"deleting": false}}
index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
model_input = pd.read_csv("../data/stressfulness_event_with_target_0_ver2/input_appraisal_stressfulness_event_mean.csv").set_index(index_columns)
categorical_feature_colnames = ["gender", "startlanguage"]
additional_categorical_features = [col for col in model_input.columns if "mostcommonactivity" in col or "homelabel" in col]
categorical_feature_colnames += additional_categorical_features
categorical_features = model_input[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 = model_input.drop(categorical_feature_colnames, axis=1)
model_input = pd.concat([numerical_features, categorical_features], axis=1)
# Binarizacija targeta
bins = [-1, 0, 4] # bins for stressfulness (0-4) target
model_input['target'], edges = pd.cut(model_input.target, bins=bins, labels=[0, 1], retbins=True, right=True)
print(model_input['target'].value_counts(), edges)
# %%
info_gains = get_information_gains(model_input, 'target')
# %% [markdown]
# Present the feature importance results
# %%
print("Total columns:", len(info_gains))
print(pd.Series(info_gains).value_counts())
n_features_with_highest_info_gain(info_gains, n=189)
# %%
def compute_impurity(feature, impurity_criterion):
"""
This function calculates impurity of a feature.
Supported impurity criteria: 'entropy', 'gini'
input: feature (this needs to be a Pandas series)
output: feature impurity
"""
probs = feature.value_counts(normalize=True)
if impurity_criterion == 'entropy':
impurity = -1 * np.sum(np.log2(probs) * probs)
elif impurity_criterion == 'gini':
impurity = 1 - np.sum(np.square(probs))
else:
raise ValueError('Unknown impurity criterion')
return impurity
def comp_feature_information_gain(df, target, descriptive_feature, split_criterion, print_flag=False):
"""
This function calculates information gain for splitting on
a particular descriptive feature for a given dataset
and a given impurity criteria.
Supported split criterion: 'entropy', 'gini'
"""
if print_flag:
print('target feature:', target)
print('descriptive_feature:', descriptive_feature)
print('split criterion:', split_criterion)
target_entropy = compute_impurity(df[target], split_criterion)
# we define two lists below:
# entropy_list to store the entropy of each partition
# weight_list to store the relative number of observations in each partition
entropy_list = list()
weight_list = list()
# loop over each level of the descriptive feature
# to partition the dataset with respect to that level
# and compute the entropy and the weight of the level's partition
for level in df[descriptive_feature].unique():
df_feature_level = df[df[descriptive_feature] == level]
entropy_level = compute_impurity(df_feature_level[target], split_criterion)
entropy_list.append(round(entropy_level, 3))
weight_level = len(df_feature_level) / len(df)
weight_list.append(round(weight_level, 3))
# print('impurity of partitions:', entropy_list)
# print('weights of partitions:', weight_list)
feature_remaining_impurity = np.sum(np.array(entropy_list) * np.array(weight_list))
information_gain = target_entropy - feature_remaining_impurity
if print_flag:
print('impurity of partitions:', entropy_list)
print('weights of partitions:', weight_list)
print('remaining impurity:', feature_remaining_impurity)
print('information gain:', information_gain)
print('====================')
return information_gain
def calc_information_gain_2(data, split_name, target_name, split_criterion):
"""
Calculate information gain given a data set, column to split on, and target
"""
# Calculate the original impurity
original_impurity = compute_impurity(data[target_name], split_criterion)
#Find the unique values in the column
values = data[split_name].unique()
# Make two subsets of the data, based on the unique values
left_split = data[data[split_name] == values[0]]
right_split = data[data[split_name] == values[1]]
# Loop through the splits and calculate the subset impurities
to_subtract = 0
for subset in [left_split, right_split]:
prob = (subset.shape[0] / data.shape[0])
to_subtract += prob * compute_impurity(subset[target_name], split_criterion)
# Return information gain
return original_impurity - to_subtract
def get_information_gains_2(data, target_name, split_criterion):
#Intialize an empty dictionary for information gains
information_gains = {}
#Iterate through each column name in our list
for feature in list(data.columns):
#Find the information gain for the column
information_gain = calc_information_gain_2(model_input, target_name, feature, split_criterion)
#Add the information gain to our dictionary using the column name as the ekey
information_gains[feature] = information_gain
#Return the key with the highest value
#return max(information_gains, key=information_gains.get)
return information_gains
# %% [markdown]
# Present the feature importance results from other methods
# %%
split_criterion = 'entropy'
print("Target impurity:", compute_impurity(model_input['target'], split_criterion))
information_gains = get_information_gains_2(model_input, 'target', split_criterion)
print(pd.Series(information_gains).value_counts().sort_index(ascending=False))
n_features_with_highest_info_gain(information_gains)
# %%
# Present the feature importance using a tree (that uses gini imputity measure)
split_criterion = 'entropy'
print("Target impurity:", compute_impurity(model_input['target'], split_criterion))
X, y = model_input.drop(columns=['target', 'pid']), model_input['target']
imputer = SimpleImputer(missing_values=np.nan, strategy='median')
X = imputer.fit_transform(X)
X, _, y, _ = train_test_split(X, y, random_state=19, test_size=0.25)
clf = DecisionTreeClassifier(criterion=split_criterion)
clf.fit(X, y)
feat_importance = clf.tree_.compute_feature_importances(normalize=False)
print("feat importance = ", feat_importance)
print("shape", feat_importance.shape)
tree_feat_imp = dict(zip(model_input.drop(columns=['target', 'pid']).columns, feat_importance.tolist()))
info_gains_dict = pd.Series(n_features_with_highest_info_gain(tree_feat_imp))
info_gains_dict[info_gains_dict > 0]
# %%
# Binarizacija vrednosti tree Information Gain-a
bins = [-0.1, 0, 0.1] # bins for target's correlations with features
cut_info_gains = pd.cut(info_gains_dict, bins=bins, labels=['IG=0', 'IG>0'], right=True)
plt.title(f"Tree information gains by value ({split_criterion})")
cut_info_gains.value_counts().plot(kind='bar', color='purple')
plt.xticks(rotation=45, ha='right')
print(cut_info_gains.value_counts())
pd.Series(n_features_with_highest_info_gain(tree_feat_imp, 20))
# %%
# Plot feature importance tree graph
plt.figure(figsize=(12,12))
tree.plot_tree(clf,
feature_names = list(model_input.drop(columns=['target', 'pid']).columns),
class_names=True,
filled = True, fontsize=5, max_depth=3)
plt.savefig('tree_high_dpi', dpi=800)
# %% [markdown]
# Present the feature importance by correlation with target
corrs = abs(model_input.drop(columns=["target", 'pid'], axis=1).apply(lambda x: x.corr(model_input.target.astype(int))))
# corrs.sort_values(ascending=False)
# Binarizacija vrednosti korelacij
bins = [0, 0.1, 0.2, 0.3] # bins for target's correlations with features
cut_corrs = pd.cut(corrs, bins=bins, labels=['very week (0-0.1)', 'weak (0.1-0.2)', 'medium (0.2-0.3)'], right=True)
plt.title("Target's correlations with features")
cut_corrs.value_counts().plot(kind='bar')
plt.xticks(rotation=45, ha='right')
print(cut_corrs.value_counts())
print(corrs[corrs > 0.1]) # or corrs < -0.1])
# %%
# %%

328
exploration/expl_features_groups_analysis.py
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@ -1,328 +0,0 @@
# ---
# 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": false, "outputs_hidden": false}
# %matplotlib inline
import os, sys, math
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.impute import SimpleImputer
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split, cross_validate, StratifiedKFold
from sklearn import metrics
# %% jupyter={"source_hidden": false, "outputs_hidden": false} nteract={"transient": {"deleting": false}}
index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
model_input = pd.read_csv("../data/stressfulness_event_with_target_0_ver2/input_appraisal_stressfulness_event_mean.csv").set_index(index_columns)
categorical_feature_colnames = ["gender", "startlanguage"]
additional_categorical_features = [col for col in model_input.columns if "mostcommonactivity" in col or "homelabel" in col]
categorical_feature_colnames += additional_categorical_features
categorical_features = model_input[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 = model_input.drop(categorical_feature_colnames, axis=1)
model_input = pd.concat([numerical_features, categorical_features], axis=1)
# Binarizacija targeta
bins = [-1, 0, 4] # bins for stressfulness (0-4) target
model_input['target'], edges = pd.cut(model_input.target, bins=bins, labels=[0, 1], retbins=True, right=True)
print("Non-numeric cols (or target):", list(model_input.columns.difference(model_input.select_dtypes(include=np.number).columns)))
print("Shapes of numeric df:", model_input.shape, model_input.select_dtypes(include=np.number).shape)
# %%
# Add prefix to demographical features
demo_features = ['age', 'limesurvey_demand', 'limesurvey_control', 'limesurvey_demand_control_ratio', 'limesurvey_demand_control_ratio_quartile',
'gender_F', 'gender_M', 'startlanguage_nl', 'startlanguage_sl']
new_names = [(col, "demo_"+col) for col in demo_features]
model_input.rename(columns=dict(new_names), inplace=True)
demo_features = ['demo_age', 'demo_limesurvey_demand', 'demo_limesurvey_control', 'demo_limesurvey_demand_control_ratio',
'demo_limesurvey_demand_control_ratio_quartile', 'target', 'demo_gender_F', 'demo_gender_M',
'demo_startlanguage_nl', 'demo_startlanguage_sl']
# %%
# Get phone and non-phone columns
import warnings
def make_predictions_with_sensor_groups(df, groups_substrings, include_group=True, with_cols=[], print_flag=False):
"""
This function makes predictions with sensor groups.
It takes in a dataframe (df), a list of group substrings (groups_substrings)
and an optional parameter include_group (default is True).
It creates a list of columns in the dataframe that contain the group substrings,
while excluding the 'pid' and 'target' columns. It then splits the data into training
and test sets, using a test size of 0.25 for the first split and 0.2 for the second split.
A SimpleImputer is used to fill in missing values with median values.
A LogisticRegression is then used to fit the training set and make predictions
on the test set. Finally, accuracy, precision, recall and F1 scores are printed
for each substring group depending on whether or not include_group
is set to True or False.
"""
best_sensor = None
best_recall_score, best_f1_score = None, None
for fgroup_substr in groups_substrings:
if fgroup_substr is None:
feature_group_cols = list(df.columns)
feature_group_cols.remove("pid")
feature_group_cols.remove("target")
else:
if include_group:
feature_group_cols = [col for col in df.columns if fgroup_substr in col and col not in ['pid', 'target']]
else:
feature_group_cols = [col for col in df.columns if fgroup_substr not in col and col not in ['pid', 'target']]
X, y = df.drop(columns=['target', 'pid'])[feature_group_cols+with_cols], df['target']
X, _, y, _ = train_test_split(X, y, stratify=y, random_state=19, test_size=0.2)
imputer = SimpleImputer(missing_values=np.nan, strategy='median')
nb = GaussianNB()
model_cv = cross_validate(
nb,
X=imputer.fit_transform(X),
y=y,
cv=StratifiedKFold(n_splits=5, shuffle=True),
n_jobs=-1,
scoring=('accuracy', 'precision', 'recall', 'f1')
)
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=2, test_size=0.2)
if print_flag:
if include_group:
print("\nPrediction with", fgroup_substr)
else:
print("\nPrediction without", fgroup_substr)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", message="Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.")
acc = np.mean(model_cv['test_accuracy'])
acc_std = np.std(model_cv['test_accuracy'])
prec = np.mean(model_cv['test_precision'])
prec_std = np.std(model_cv['test_precision'])
rec = np.mean(model_cv['test_recall'])
rec_std = np.std(model_cv['test_recall'])
f1 = np.mean(model_cv['test_f1'])
f1_std = np.std(model_cv['test_f1'])
if print_flag:
print("************************************************")
print(f"Accuracy: {acc} (sd={acc_std})")
print(f"Precison: {prec} (sd={prec_std})")
print(f"Recall: {rec} (sd={rec_std})")
print(f"F1: {f1} (sd={f1_std})\n")
if (not best_recall_score and not best_f1_score) or (rec > best_recall_score):
best_sensor = fgroup_substr
best_recall_score, best_f1_score = rec, f1
best_recall_score_std, best_f1_score_std = rec_std, f1_std
return best_sensor, best_recall_score, best_f1_score, best_recall_score_std, best_f1_score_std
# %% [markdown]
# ### sensor big feature groups (phone, empatica, demographical)
big_groups_substr = ["phone_", "empatica_", "demo_"]
make_predictions_with_sensor_groups(model_input.copy(), groups_substrings=big_groups_substr, include_group=False)
# %% [markdown]
# ### Empatica sezor groups
# make_predictions_with_sensor_groups(model_input.copy(), groups_substrings="_", include_group=True)
# e4_sensors = ["empatica_inter_beat_", "empatica_accelerometer_", "empatica_temperature_", "empatica_electrodermal_"]
# make_predictions_with_sensor_groups(model_input.copy(), groups_substrings=e4_sensors, include_group=False)
# %% [markdown]
# ### Phone sensor groups
# make_predictions_with_sensor_groups(model_input.copy(), groups_substrings="_", include_group=True)
# phone_sensors = ["phone_activity_", "phone_applications_", "phone_bluetooth_", "phone_battery", "phone_calls_",
# "phone_light_", "phone_location_", "phone_messages", "phone_screen_", "phone_speech_"]
# make_predictions_with_sensor_groups(model_input.copy(), groups_substrings=phone_sensors, include_group=False)
# %%
# Write all the sensors (phone, empatica), seperate other (demographical) cols also
sensors_features_groups = ["empatica_inter_beat_", "empatica_accelerometer_", "empatica_temperature_", "empatica_electrodermal_",
"phone_activity_", "phone_applications_", "phone_bluetooth_", "phone_battery_", "phone_calls_", "phone_light_",
"phone_locations_", "phone_messages", "phone_screen_"] # , "phone_speech_"]
# %%
def find_sensor_group_features_importance(model_input, sensor_groups_strings):
"""
This function finds the importance of sensor groups for a given model input. It takes two parameters:
model_input and sensor_groups_strings. It creates an empty list called sensor_importance_scores,
which will be populated with tuples containing the best sensor, its recall score, and its F1 score.
It then makes a copy of the model input and the sensor groups strings. It then loops through each group
in the list of strings, creating a list of important columns from the sensor importance scores list.
It then calls make_predictions_with_sensor_groups to determine the best sensor, its recall score,
and its F1 score. These values are added to the sensor importance scores list as a tuple. The function
then removes that best sensor from the list of strings before looping again until all groups have been evaluated.
Finally, it returns the populated list of tuples containing all sensors' scores.
"""
sensor_importance_scores = []
model_input = model_input.copy()
sensor_groups_strings = sensor_groups_strings.copy()
groups_len = len(sensor_groups_strings)
for i in range(groups_len):
important_cols = [col[0] for col in sensor_importance_scores]
with_cols = [col for col in model_input.columns if any(col.startswith(y) for y in important_cols)]
best_sensor, best_recall_score, best_f1_sore, best_recall_score_std, best_f1_score_std = \
make_predictions_with_sensor_groups(model_input,
groups_substrings=sensor_groups_strings, include_group=True,
with_cols=with_cols)
sensor_importance_scores.append((best_sensor, best_recall_score, best_f1_sore, best_recall_score_std, best_f1_score_std ))
print(f"\nAdded sensor: {best_sensor}\n")
sensor_groups_strings.remove(best_sensor)
return sensor_importance_scores
# %%
# Method for sorting list of tuples into 3 lists
def sort_tuples_to_lists(list_of_tuples):
"""
sort_tuples_to_lists(list_of_tuples) is a method that takes in a list of tuples as an argument
and sorts them into three separate lists. The first list, xs, contains the first element
of each tuple. The second list, yrecall, contains the second element of each tuple rounded
to 4 decimal places. The third list, y_fscore, contains the third element of each tuple
rounded to 4 decimal places. The method returns all three lists.
"""
xs, y_recall, y_fscore, recall_std, fscore_std = [], [], [], [], []
for a_tuple in list_of_tuples:
xs.append(a_tuple[0])
y_recall.append(round(a_tuple[1], 4))
y_fscore.append(round(a_tuple[2], 4))
recall_std.append(round(a_tuple[3], 4))
fscore_std.append(round(a_tuple[4], 4))
return xs, y_recall, y_fscore, recall_std, fscore_std
def plot_sequential_progress_of_feature_addition_scores(xs, y_recall, y_fscore, recall_std, fscore_std,
title="Sequential addition of features and its F1, and recall scores"):
"""
This function plots the sequential progress of feature addition scores using two subplots.
The first subplot is for recall scores and the second subplot is for F1-scores.
The parameters xs, yrecall, and yfscore are used to plot the data on the respective axes.
The title of the plot can be specified by the user using the parameter title.
The maximum recall index and maximum F1-score index are also plotted using a black dot.
The figure size is set to 18.5 inches in width and 10.5 inches in height,
and the x-axis labels are rotated by 90 degrees. Finally, the plot is displayed
using plt.show().
"""
fig, ax = plt.subplots(nrows=2, sharex=True)
ax[0].plot(xs, np.array(y_recall)+np.array(recall_std), linestyle=":", color='m') # Upper SD
ax[0].plot(xs, y_recall, color='red')
ax[0].plot(xs, np.array(y_recall)-np.array(recall_std), linestyle=":", color='m') # Lower SD
mrec_indx = np.argmax(y_recall)
ax[0].plot(xs[mrec_indx], y_recall[mrec_indx], "-o", color='black')
ax[0].legend(["Upper std", "Mean Recall", "Lower std"])
ax[1].plot(xs, np.array(y_fscore)+np.array(fscore_std), linestyle=":", color='c') # Upper SD
ax[1].plot(xs, y_fscore)
ax[1].plot(xs, np.array(y_fscore)-np.array(fscore_std), linestyle=":", color='c') # Lower SD
mfscore_indx = np.argmax(y_fscore)
ax[1].plot(xs[mfscore_indx], y_fscore[mfscore_indx], "-o", color='black')
ax[1].legend(["Upper std", "Mean F1-score", "Lower std"])
fig.set_size_inches(18.5, 10.5)
ax[0].title.set_text('Recall scores')
ax[1].title.set_text('F1-scores')
plt.suptitle(title, fontsize=14)
plt.xticks(rotation=90)
plt.show()
# %%
sensors_features_groups = ["empatica_inter_beat_", "empatica_accelerometer_", "empatica_temperature_", "empatica_electrodermal_",
"phone_activity_", "phone_applications_", "phone_bluetooth_", "phone_battery_", "phone_calls_", "phone_light_",
"phone_locations_", "phone_messages", "phone_screen_"] # , "phone_speech_"]
# sensors_features_groups = ["phone_", "empatica_", "demo_"]
# %%
# sensor_importance_scores = find_sensor_group_features_importance(model_input, big_groups_substr)
sensor_groups_importance_scores = find_sensor_group_features_importance(model_input, sensors_features_groups)
xs, y_recall, y_fscore, recall_std, fscore_std = sort_tuples_to_lists(sensor_groups_importance_scores)
# %% [markdown]
# ### Visualize sensors groups F1 and recall scores
print(sensor_groups_importance_scores)
plot_sequential_progress_of_feature_addition_scores(xs, y_recall, y_fscore, recall_std, fscore_std,
title="Sequential addition of sensors and its F1, and recall scores")
# %%
# Take the most important feature group and investigate it feature-by-feature
best_sensor_group = sensor_groups_importance_scores[0][0] # take the highest rated sensor group
best_sensor_features = [col for col in model_input if col.startswith(best_sensor_group)]
# best_sensor_features_scores = find_sensor_group_features_importance(model_input, best_sensor_features)
# xs, y_recall, y_fscore, recall_std, fscore_std = sort_tuples_to_lists(best_sensor_features_scores)
# %% [markdown]
# ### Visualize best sensor's F1 and recall scores
# print(best_sensor_features_scores)
# plot_sequential_progress_of_feature_addition_scores(xs, y_recall, y_fscore, recall_std, fscore_std,
# title="Best sensor addition it's features with F1 and recall scores")
# %%
# This section iterates over all sensor groups and investigates sequential feature importance feature-by-feature
# It also saves the sequence of scores for all sensors' features in excel file
seq_columns = ["sensor_name", "feature_sequence", "recall", "f1_score"]
feature_sequence = pd.DataFrame(columns=seq_columns)
for i, sensor_group in enumerate(sensor_groups_importance_scores):
current_sensor_features = [col for col in model_input if col.startswith(sensor_group[0])]
current_sensor_features_scores = find_sensor_group_features_importance(model_input, current_sensor_features)
xs, y_recall, y_fscore, recall_std, fscore_std = sort_tuples_to_lists(current_sensor_features_scores)
feature_sequence = pd.concat([feature_sequence, pd.DataFrame({"sensor_name":sensor_group[0], "feature_sequence": [xs], "recall": [y_recall],
"f1_score": [y_fscore], "recall_std": [recall_std], "f1_std": [fscore_std]})])
plot_sequential_progress_of_feature_addition_scores(xs, y_recall, y_fscore, recall_std, fscore_std,
title=f"Sequential addition of features for {sensor_group[0]} and its F1, and recall scores")
feature_sequence.to_excel("all_sensors_sequential_addition_scores.xlsx", index=False)
# %%
# TODO: method that reads data from the excel file, specified above, and then the method,
# that selects only features that are max a thresh[%] below the max value (best for recall
# possibly for f1). This method should additionally take threshold parameter.
# %%

84
exploration/expl_ambient.py → exploration/expl_light.py
View File

@ -6,7 +6,7 @@
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.13.0
# jupytext_version: 1.11.4
# kernelspec:
# display_name: straw2analysis
# language: python
@ -21,6 +21,7 @@ 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:
@ -31,18 +32,18 @@ import participants.query_db
TZ_LJ = timezone("Europe/Ljubljana")
# %%
from features.ambient import *
from features.light import *
# %% [markdown]
# # Light
# # Basic characteristics
# %%
df_light_nokia = get_ambient_data(["nokia_0000003"], "light")
df_light_nokia = get_light_data(["nokia_0000003"])
print(df_light_nokia)
# %%
participants_inactive_usernames = participants.query_db.get_usernames()
df_light_inactive = get_ambient_data(participants_inactive_usernames, "light")
df_light_inactive = get_light_data(participants_inactive_usernames)
# %%
df_light_inactive.accuracy.value_counts()
@ -102,7 +103,7 @@ 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 = (
@ -165,74 +166,3 @@ 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.

20
exploration/expl_proximity.py
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@ -16,7 +16,6 @@
# %%
# %matplotlib inline
import datetime
import importlib
import os
import sys
@ -33,16 +32,13 @@ import participants.query_db
TZ_LJ = timezone("Europe/Ljubljana")
# %%
from features import helper, proximity
# %%
importlib.reload(proximity)
from features.proximity import *
# %% [markdown]
# # Basic characteristics
# %%
df_proximity_nokia = proximity.get_proximity_data(["nokia_0000003"])
df_proximity_nokia = get_proximity_data(["nokia_0000003"])
print(df_proximity_nokia)
# %%
@ -57,7 +53,7 @@ df_proximity_nokia.double_proximity.value_counts()
# %%
participants_inactive_usernames = participants.query_db.get_usernames()
df_proximity_inactive = proximity.get_proximity_data(participants_inactive_usernames)
df_proximity_inactive = get_proximity_data(participants_inactive_usernames)
# %%
df_proximity_inactive.double_proximity.describe()
@ -114,13 +110,3 @@ 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)

166
exploration/expl_stress_event.py
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# -*- coding: utf-8 -*-
# ---
# jupyter:
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# ---
# %%
import os
import sys
import datetime
import math
import seaborn as sns
nb_dir = os.path.split(os.getcwd())[0]
if nb_dir not in sys.path:
sys.path.append(nb_dir)
import participants.query_db
from features.esm import *
from features.esm_JCQ import *
from features.esm_SAM import *
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
# %%
participants_inactive_usernames = participants.query_db.get_usernames(
collection_start=datetime.date.fromisoformat("2020-08-01")
)
df_esm_inactive = get_esm_data(participants_inactive_usernames)
# %%
df_esm_preprocessed = preprocess_esm(df_esm_inactive)
# %% [markdown]
# Investigate stressfulness events
# %%
extracted_ers = df_esm_preprocessed.groupby(["device_id", "esm_session"])['timestamp'].apply(lambda x: math.ceil((x.max() - x.min()) / 1000)).reset_index().rename(columns={'timestamp': 'session_length'}) # questionnaire length
extracted_ers = extracted_ers[extracted_ers["session_length"] <= 15 * 60].reset_index(drop=True) # ensure that the longest duration of the questionnaire answering is 15 min
session_start_timestamp = df_esm_preprocessed.groupby(['device_id', 'esm_session'])['timestamp'].min().to_frame().rename(columns={'timestamp': 'session_start_timestamp'}) # questionnaire start timestamp
session_end_timestamp = df_esm_preprocessed.groupby(['device_id', 'esm_session'])['timestamp'].max().to_frame().rename(columns={'timestamp': 'session_end_timestamp'}) # questionnaire end timestamp
se_time = df_esm_preprocessed[df_esm_preprocessed.questionnaire_id == 90.].set_index(['device_id', 'esm_session'])['esm_user_answer'].to_frame().rename(columns={'esm_user_answer': 'se_time'})
se_duration = df_esm_preprocessed[df_esm_preprocessed.questionnaire_id == 91.].set_index(['device_id', 'esm_session'])['esm_user_answer'].to_frame().rename(columns={'esm_user_answer': 'se_duration'})
# Make se_durations to the appropriate lengths
# Extracted 3 targets that will be transfered in the csv file to the cleaning script.
df_esm_preprocessed[df_esm_preprocessed.questionnaire_id == 87.].columns
se_stressfulness_event_tg = df_esm_preprocessed[df_esm_preprocessed.questionnaire_id == 87.].set_index(['device_id', 'esm_session'])['esm_user_answer'].to_frame().rename(columns={'esm_user_answer': 'appraisal_stressfulness_event'})
# All relevant features are joined by inner join to remove standalone columns (e.g., stressfulness event target has larger count)
extracted_ers = extracted_ers.join(session_start_timestamp, on=['device_id', 'esm_session'], how='inner') \
.join(session_end_timestamp, on=['device_id', 'esm_session'], how='inner') \
.join(se_stressfulness_event_tg, on=['device_id', 'esm_session'], how='inner') \
.join(se_time, on=['device_id', 'esm_session'], how='left') \
.join(se_duration, on=['device_id', 'esm_session'], how='left') \
# Filter-out the sessions that are not useful. Because of the ambiguity this excludes:
# (1) straw event times that are marked as "0 - I don't remember"
# (2) straw event durations that are marked as "0 - I don't remember"
extracted_ers = extracted_ers[(~extracted_ers.se_time.astype(str).str.startswith("0 - ")) & (~extracted_ers.se_duration.astype(str).str.startswith("0 - ")) & (~extracted_ers.se_duration.astype(str).str.startswith("Removed "))]
extracted_ers.reset_index(drop=True, inplace=True)
# Add default duration in case if participant answered that no stressful event occured
# Prepare data to fit the data structure in the CSV file ...
# Add the event time as the start of the questionnaire if no stress event occured
extracted_ers['se_time'] = extracted_ers['se_time'].fillna(extracted_ers['session_start_timestamp'])
# Type could be an int (timestamp [ms]) which stays the same, and datetime str which is converted to timestamp in miliseconds
extracted_ers['event_timestamp'] = extracted_ers['se_time'].apply(lambda x: x if isinstance(x, int) else pd.to_datetime(x).timestamp() * 1000).astype('int64')
extracted_ers['shift_direction'] = -1
""">>>>> begin section (could be optimized) <<<<<"""
# Checks whether the duration is marked with "1 - It's still ongoing" which means that the end of the current questionnaire
# is taken as end time of the segment. Else the user input duration is taken.
extracted_ers['temp_duration'] = extracted_ers['se_duration']
extracted_ers['se_duration'] = \
np.where(
extracted_ers['se_duration'].astype(str).str.startswith("1 - "),
extracted_ers['session_end_timestamp'] - extracted_ers['event_timestamp'],
extracted_ers['se_duration']
)
# This converts the rows of timestamps in miliseconds and the rows with datetime... to timestamp in seconds.
extracted_ers['se_duration'] = \
extracted_ers['se_duration'].apply(lambda x: math.ceil(x / 1000) if isinstance(x, int) else abs(pd.to_datetime(x).hour * 60 + pd.to_datetime(x).minute) * 60)
# Check whether min se_duration is at least the same duration as the ioi. Filter-out the rest.
""">>>>> end section <<<<<"""
# %% [markdown]
# Count negative values of duration
print("Count all:", extracted_ers[['se_duration', 'temp_duration', 'session_end_timestamp', 'event_timestamp']].shape[0])
print("Count stressed:", extracted_ers[(~extracted_ers['se_duration'].isna())][['se_duration', 'temp_duration', 'session_end_timestamp', 'event_timestamp']].shape[0])
print("Count negative durations (invalid se_time user input):", extracted_ers[extracted_ers['se_duration'] < 0][['se_duration', 'temp_duration', 'session_end_timestamp', 'event_timestamp']].shape[0])
print("Count 0 durations:", extracted_ers[extracted_ers['se_duration'] == 0][['se_duration', 'temp_duration', 'session_end_timestamp', 'event_timestamp']].shape[0])
extracted_ers[extracted_ers['se_duration'] <= 0][['se_duration', 'temp_duration', 'session_end_timestamp', 'event_timestamp']].shape[0]
extracted_ers[(~extracted_ers['se_duration'].isna()) & (extracted_ers['se_duration'] <= 0)][['se_duration', 'temp_duration', 'session_end_timestamp', 'event_timestamp']]
ax = extracted_ers.hist(column='se_duration', bins='auto', grid=False, figsize=(12,8), color='#86bf91', zorder=2, rwidth=0.9)
hist, bin_edges = np.histogram(extracted_ers['se_duration'].dropna())
hist
bin_edges
extracted_ers = extracted_ers[extracted_ers['se_duration'] >= 0]
# %%
# bins = [-100000000, 0, 0.0000001, 1200, 7200, 100000000] #'neg', 'zero', '<20min', '2h', 'high_pos' ..... right=False
bins = [-100000000, -0.0000001, 0, 300, 600, 1200, 3600, 7200, 14400, 1000000000] # 'neg', 'zero', '5min', '10min', '20min', '1h', '2h', '4h', 'more'
extracted_ers['bins'], edges = pd.cut(extracted_ers.se_duration, bins=bins, labels=['neg', 'zero', '5min', '10min', '20min', '1h', '2h', '4h', 'more'], retbins=True, right=True) #['low', 'medium', 'high']
sns.displot(
data=extracted_ers.dropna(),
x="bins",
binwidth=0.1,
)
# %% [markdown]
extracted_ers[extracted_ers['session_end_timestamp'] - extracted_ers['event_timestamp'] >= 0]
extracted_ers['se_time'].value_counts()
pd.set_option('display.max_rows', 100)
# Tukaj nas zanima, koliko so oddaljeni časi stresnega dogodka od konca vprašalnika.
extracted_ers = extracted_ers[~extracted_ers['se_duration'].isna()] # Remove no stress events
extracted_ers['diff_se_time_session_end'] = (extracted_ers['session_end_timestamp'] - extracted_ers['event_timestamp'])
print("Count all:", extracted_ers[['se_duration', 'temp_duration', 'session_start_timestamp', 'event_timestamp']].shape[0])
print("Count negative durations:", extracted_ers[extracted_ers['diff_se_time_session_end'] < 0][['se_duration', 'temp_duration', 'session_start_timestamp', 'event_timestamp']])
print("Count 0 durations:", extracted_ers[extracted_ers['diff_se_time_session_end'] == 0][['se_duration', 'temp_duration', 'session_start_timestamp', 'event_timestamp']].shape[0])
extracted_ers[extracted_ers['diff_se_time_session_end'] < 0]['diff_se_time_session_end']
# extracted_ers = extracted_ers[(extracted_ers['diff_se_time_session_end'] > 0)]
bins2 = [-100000, 0, 300, 600, 1200, 3600, 7200, 14400, 1000000000] # 'zero', '5min', '10min', '20min', '1h', '2h', '4h', 'more'
extracted_ers['bins2'], edges = pd.cut(extracted_ers.diff_se_time_session_end, bins=bins2, labels=['neg_zero', '5min', '10min', '20min', '1h', '2h', '4h', 'more'], retbins=True, right=True) #['low', 'medium', 'high']
extracted_ers['bins2']
sns.displot(
data=extracted_ers.dropna(),
x="bins2",
binwidth=0.1,
)
extracted_ers.shape
extracted_ers.dropna().shape
print()
# %%
extracted_ers['appraisal_stressfulness_event_num'] = extracted_ers['appraisal_stressfulness_event'].str[0].astype(int)
print("duration-target (corr):", extracted_ers['se_duration'].corr(extracted_ers['appraisal_stressfulness_event_num']))
# %%
# Explore groupby participants?

93
exploration/ml_pipeline.py
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# ---
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# ---
# %%
import sys, os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import recall_score, f1_score
nb_dir = os.path.split(os.getcwd())[0]
if nb_dir not in sys.path:
sys.path.append(nb_dir)
from machine_learning.cross_validation import CrossValidation
from machine_learning.preprocessing import Preprocessing
from machine_learning.feature_selection import FeatureSelection
# %%
df = pd.read_csv("../data/stressfulness_event_with_speech/input_appraisal_stressfulness_event_mean.csv")
index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
df.set_index(index_columns, inplace=True)
# Create binary target
bins = [-1, 0, 4] # bins for stressfulness (0-4) target
df['target'], edges = pd.cut(df.target, bins=bins, labels=[0, 1], retbins=True, right=True) #['low', 'medium', 'high']
nan_cols = df.columns[df.isna().any()].tolist()
df[nan_cols] = df[nan_cols].fillna(round(df[nan_cols].median(), 0))
cv = CrossValidation(data=df, cv_method="logo")
categorical_columns = ["gender", "startlanguage", "mostcommonactivity", "homelabel"]
interval_feature_list, other_feature_list = [], []
# %%
for split in cv.get_splits():
train_X, train_y, test_X, test_y = cv.get_train_test_sets(split)
pre = Preprocessing(train_X, train_y, test_X, test_y)
pre.one_hot_encode_train_and_test_sets(categorical_columns)
train_X, train_y, test_X, test_y = pre.get_train_test_sets()
print(train_X.shape, test_X.shape)
# Predict before feature selection
rfc = RandomForestClassifier(n_estimators=10)
rfc.fit(train_X, train_y)
predictions = rfc.predict(test_X)
print("Recall:", recall_score(test_y, predictions))
print("F1:", f1_score(test_y, predictions))
# Feature selection on train set
train_groups, test_groups = cv.get_groups_sets(split)
fs = FeatureSelection(train_X, train_y, train_groups)
selected_features = fs.select_features(n_min=20, n_max=29, k=40,
ml_type="classification_bin",
metric="recall", n_tolerance=20)
train_X = train_X[selected_features]
test_X = test_X[selected_features]
print(selected_features)
print(len(selected_features))
# Predict after feature selection
rfc = RandomForestClassifier(n_estimators=500)
rfc.fit(train_X, train_y)
predictions = rfc.predict(test_X)
print("Recall:", recall_score(test_y, predictions))
print("F1:", f1_score(test_y, predictions))
break
# %%

134
exploration/ml_pipeline_classification.py
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# ---
# %% jupyter={"outputs_hidden": false, "source_hidden": false}
# from IPython.core.interactiveshell import InteractiveShell
from pathlib import Path
# matplotlib inline
# import os
# import sys
import pandas as pd
from machine_learning.helper import (
impute_encode_categorical_features,
prepare_cross_validator,
prepare_sklearn_data_format,
run_all_classification_models,
)
# InteractiveShell.ast_node_interactivity = "all"
#
# nb_dir = os.path.split(os.getcwd())[0]
# if nb_dir not in sys.path:
# sys.path.append(nb_dir)
# %%
CV_METHOD = "logo" # logo, half_logo, 5kfold
# Cross-validation method (could be regarded as a hyperparameter)
print("CV_METHOD: " + CV_METHOD)
N_SL = 3 # Number of largest/smallest accuracies (of particular CV) outputs
UNDERSAMPLING = False
# (bool) If True this will train and test data on balanced dataset
# (using undersampling method)
# %% jupyter={"outputs_hidden": false, "source_hidden": false}
PATH_BASE = Path("E:/STRAWresults/20230415")
SEGMENT_TYPE = "period"
print("SEGMENT_TYPE: " + SEGMENT_TYPE)
SEGMENT_LENGTH = "30_minutes_before"
print("SEGMENT_LENGTH: " + SEGMENT_LENGTH)
TARGET_VARIABLE = "JCQ_job_control"
print("TARGET_VARIABLE: " + TARGET_VARIABLE)
if ("appraisal" in TARGET_VARIABLE) and ("stressfulness" in TARGET_VARIABLE):
TARGET_VARIABLE += "_"
TARGET_VARIABLE += SEGMENT_TYPE
PATH_FULL = PATH_BASE / SEGMENT_LENGTH / ("input_" + TARGET_VARIABLE + "_mean.csv")
model_input = pd.read_csv(PATH_FULL)
if SEGMENT_LENGTH == "daily":
DAY_LENGTH = "daily" # or "working"
print(DAY_LENGTH)
model_input = model_input[model_input["local_segment"].str.contains(DAY_LENGTH)]
# %% jupyter={"outputs_hidden": false, "source_hidden": false}
model_input["target"].value_counts()
# %% jupyter={"outputs_hidden": false, "source_hidden": false}
# bins = [-10, 0, 10] # bins for z-scored targets
BINS = [-1, 0, 4] # bins for stressfulness (0-4) target
print("BINS: ", BINS)
model_input["target"], edges = pd.cut(
model_input.target, bins=BINS, labels=["low", "high"], retbins=True, right=True
) # ['low', 'medium', 'high']
print(model_input["target"].value_counts())
REMOVE_MEDIUM = True
if ("medium" in model_input["target"]) and REMOVE_MEDIUM:
model_input = model_input[model_input["target"] != "medium"]
model_input["target"] = (
model_input["target"].astype(str).apply(lambda x: 0 if x == "low" else 1)
)
else:
model_input["target"] = model_input["target"].map(
{"low": 0, "medium": 1, "high": 2}
)
print(model_input["target"].value_counts())
# %% jupyter={"outputs_hidden": false, "source_hidden": false}
# UnderSampling
if UNDERSAMPLING:
no_stress = model_input[model_input["target"] == 0]
stress = model_input[model_input["target"] == 1]
no_stress = no_stress.sample(n=len(stress))
model_input = pd.concat([stress, no_stress], axis=0)
# %% jupyter={"outputs_hidden": false, "source_hidden": false}
model_input_encoded = impute_encode_categorical_features(model_input)
# %%
data_x, data_y, data_groups = prepare_sklearn_data_format(
model_input_encoded, CV_METHOD
)
cross_validator = prepare_cross_validator(data_x, data_y, data_groups, CV_METHOD)
# %%
data_y.head()
# %%
data_y.tail()
# %%
data_y.shape
# %%
scores = run_all_classification_models(data_x, data_y, data_groups, cross_validator)
# %%
PATH_OUTPUT = Path("..") / Path("presentation/results")
path_output_full = PATH_OUTPUT / (
TARGET_VARIABLE
+ "_"
+ SEGMENT_LENGTH
+ "_classification"
+ str(BINS)
+ "_"
+ CV_METHOD
+ ".csv"
)
scores.to_csv(path_output_full, index=False)

177
exploration/ml_pipeline_classification_composite.py
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# ---
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# ---
# %% jupyter={"outputs_hidden": false, "source_hidden": false}
from pathlib import Path
import pandas as pd
import seaborn as sns
from sklearn.decomposition import PCA
from machine_learning.helper import (
impute_encode_categorical_features,
prepare_cross_validator,
prepare_sklearn_data_format,
run_all_classification_models,
)
# %%
CV_METHOD = "logo" # logo, half_logo, 5kfold
# Cross-validation method (could be regarded as a hyperparameter)
print("CV_METHOD: " + CV_METHOD)
N_SL = 3 # Number of largest/smallest accuracies (of particular CV) outputs
UNDERSAMPLING = False
# (bool) If True this will train and test data on balanced dataset
# (using undersampling method)
# %% jupyter={"outputs_hidden": false, "source_hidden": false}
PATH_BASE = Path("E:/STRAWresults/20230415")
SEGMENT_TYPE = "period"
print("SEGMENT_TYPE: " + SEGMENT_TYPE)
SEGMENT_LENGTH = "30_minutes_before"
print("SEGMENT_LENGTH: " + SEGMENT_LENGTH)
PATH_FULL = PATH_BASE / SEGMENT_LENGTH / "features" / "all_sensor_features.csv"
all_features_with_baseline = pd.read_csv(PATH_FULL)
# %%
TARGETS = [
"PANAS_negative_affect_mean",
"PANAS_positive_affect_mean",
"JCQ_job_demand_mean",
"JCQ_job_control_mean",
"appraisal_stressfulness_period_mean",
]
# %%
all_features_cleaned = pd.DataFrame()
for target in TARGETS:
PATH_FULL = (
PATH_BASE
/ SEGMENT_LENGTH
/ "features"
/ ("all_sensor_features_cleaned_straw_py_(" + target + ").csv")
)
current_features = pd.read_csv(PATH_FULL, index_col="local_segment")
if all_features_cleaned.empty:
all_features_cleaned = current_features
else:
all_features_cleaned = all_features_cleaned.join(
current_features[("phone_esm_straw_" + target)],
how="inner",
rsuffix="_" + target,
)
print(all_features_cleaned.shape)
# %%
pca = PCA(n_components=1)
TARGETS_PREFIXED = ["phone_esm_straw_" + target for target in TARGETS]
pca.fit(all_features_cleaned[TARGETS_PREFIXED])
print(pca.explained_variance_ratio_)
# %%
model_input = all_features_cleaned.drop(columns=TARGETS_PREFIXED)
model_input["target"] = pca.fit_transform(all_features_cleaned[TARGETS_PREFIXED])
# %%
sns.histplot(data=model_input, x="target")
# %%
model_input.target.quantile(0.6)
# %% jupyter={"outputs_hidden": false, "source_hidden": false}
# bins = [-10, 0, 10] # bins for z-scored targets
BINS = [-10, 0, 10] # bins for stressfulness (0-4) target
print("BINS: ", BINS)
model_input["target"], edges = pd.cut(
model_input.target, bins=BINS, labels=["low", "high"], retbins=True, right=True
) # ['low', 'medium', 'high']
print(model_input["target"].value_counts())
REMOVE_MEDIUM = True
if REMOVE_MEDIUM:
if "medium" in model_input["target"]:
model_input = model_input[model_input["target"] != "medium"]
model_input["target"] = (
model_input["target"].astype(str).apply(lambda x: 0 if x == "low" else 1)
)
else:
model_input["target"] = model_input["target"].map(
{"low": 0, "medium": 1, "high": 2}
)
print(model_input["target"].value_counts())
# %% jupyter={"outputs_hidden": false, "source_hidden": false}
# UnderSampling
if UNDERSAMPLING:
no_stress = model_input[model_input["target"] == 0]
stress = model_input[model_input["target"] == 1]
no_stress = no_stress.sample(n=len(stress))
model_input = pd.concat([stress, no_stress], axis=0)
# %%
TARGET_VARIABLE = "PANAS_negative_affect"
print("TARGET_VARIABLE: " + TARGET_VARIABLE)
PATH_FULL_HELP = PATH_BASE / SEGMENT_LENGTH / ("input_" + TARGET_VARIABLE + "_mean.csv")
model_input_with_baseline = pd.read_csv(PATH_FULL_HELP, index_col="local_segment")
# %%
baseline_col_names = [
col for col in model_input_with_baseline.columns if col not in model_input.columns
]
print(baseline_col_names)
# %%
model_input = model_input.join(
model_input_with_baseline[baseline_col_names], how="left"
)
model_input.reset_index(inplace=True)
# %%
model_input_encoded = impute_encode_categorical_features(model_input)
# %%
data_x, data_y, data_groups = prepare_sklearn_data_format(
model_input_encoded, CV_METHOD
)
cross_validator = prepare_cross_validator(data_x, data_y, data_groups, CV_METHOD)
# %%
data_y.head()
# %%
data_y.tail()
# %%
data_y.shape
# %%
scores = run_all_classification_models(data_x, data_y, data_groups, cross_validator)
# %%
PATH_OUTPUT = Path("..") / Path("presentation/results")
path_output_full = PATH_OUTPUT / (
"composite_"
+ SEGMENT_LENGTH
+ "_classification"
+ str(BINS)
+ "_"
+ CV_METHOD
+ ".csv"
)
scores.to_csv(path_output_full, index=False)

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exploration/ml_pipeline_classification_with_clustering.py
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# ---
# %% jupyter={"source_hidden": true}
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
from sklearn.impute import SimpleImputer
from sklearn.model_selection import LeaveOneGroupOut, StratifiedKFold, cross_validate
from machine_learning.classification_models import ClassificationModels
# %%
# ## Set script's parameters
N_CLUSTERS = 4 # Number of clusters (could be regarded as a hyperparameter)
CV_METHOD = "logo" # logo, halflogo, 5kfold
# Cross-validation method (could be regarded as a hyperparameter)
N_SL = 1 # Number of largest/smallest accuracies (of particular CV) outputs
# %%
PATH_BASE = Path("E:/STRAWresults/20230415")
SEGMENT_TYPE = "period"
print("SEGMENT_TYPE: " + SEGMENT_TYPE)
SEGMENT_LENGTH = "30_minutes_before"
print("SEGMENT_LENGTH: " + SEGMENT_LENGTH)
TARGET_VARIABLE = "appraisal_stressfulness"
print("TARGET_VARIABLE: " + TARGET_VARIABLE)
if ("appraisal" in TARGET_VARIABLE) and ("stressfulness" in TARGET_VARIABLE):
TARGET_VARIABLE += "_"
TARGET_VARIABLE += SEGMENT_TYPE
PATH_FULL = PATH_BASE / SEGMENT_LENGTH / ("input_" + TARGET_VARIABLE + "_mean.csv")
model_input = pd.read_csv(PATH_FULL)
if SEGMENT_LENGTH == "daily":
DAY_LENGTH = "daily" # or "working"
print(DAY_LENGTH)
model_input = model_input[model_input["local_segment"].str.contains(DAY_LENGTH)]
# %% jupyter={"source_hidden": true}
index_columns = [
"local_segment",
"local_segment_label",
"local_segment_start_datetime",
"local_segment_end_datetime",
]
CLUST_COL = "limesurvey_demand_control_ratio_quartile"
print("CLUST_COL: " + CLUST_COL)
BINS = [-1, 0, 4]
print("BINS: " + str(BINS))
model_input[CLUST_COL].describe()
# %%
model_input["target"].value_counts()
# %% jupyter={"source_hidden": true}
# Filter-out outlier rows by clust_col
# model_input = model_input[(np.abs(stats.zscore(model_input[clust_col])) < 3)]
uniq = model_input[[CLUST_COL, "pid"]].drop_duplicates().reset_index(drop=True)
uniq = uniq.dropna()
plt.bar(uniq["pid"], uniq[CLUST_COL])
# %% jupyter={"source_hidden": true}
# Get clusters by cluster col & and merge the clusters to main df
km = KMeans(n_clusters=N_CLUSTERS).fit_predict(uniq.set_index("pid"))
np.unique(km, return_counts=True)
uniq["cluster"] = km
model_input = model_input.merge(uniq[["pid", "cluster"]])
# %%
model_input[["cluster", "target"]].value_counts().sort_index()
# %% jupyter={"source_hidden": true}
model_input.set_index(index_columns, inplace=True)
# %% jupyter={"source_hidden": true}
# Create dict with classification ml models
cm = ClassificationModels()
cmodels = cm.get_cmodels()
# %% jupyter={"source_hidden": true}
for k in range(N_CLUSTERS):
model_input_subset = model_input[model_input["cluster"] == k].copy()
model_input_subset.loc[:, "target"] = pd.cut(
model_input_subset.loc[:, "target"],
bins=BINS,
labels=["low", "high"],
right=True,
) # ['low', 'medium', 'high']
model_input_subset["target"].value_counts()
# model_input_subset = model_input_subset[model_input_subset["target"] != "medium"]
model_input_subset["target"] = (
model_input_subset["target"].astype(str).apply(lambda x: 0 if x == "low" else 1)
)
print(model_input_subset["target"].value_counts())
if CV_METHOD == "half_logo":
model_input_subset["pid_index"] = model_input_subset.groupby("pid").cumcount()
model_input_subset["pid_count"] = model_input_subset.groupby("pid")[
"pid"
].transform("count")
model_input_subset["pid_index"] = (
model_input_subset["pid_index"] / model_input_subset["pid_count"] + 1
).round()
model_input_subset["pid_half"] = (
model_input_subset["pid"]
+ "_"
+ model_input_subset["pid_index"].astype(int).astype(str)
)
data_x, data_y, data_groups = (
model_input_subset.drop(["target", "pid", "pid_index", "pid_half"], axis=1),
model_input_subset["target"],
model_input_subset["pid_half"],
)
else:
data_x, data_y, data_groups = (
model_input_subset.drop(["target", "pid"], axis=1),
model_input_subset["target"],
model_input_subset["pid"],
)
# Treat categorical features
categorical_feature_colnames = ["gender", "startlanguage"]
additional_categorical_features = [
col
for col in data_x.columns
if "mostcommonactivity" in col or "homelabel" in col
]
categorical_feature_colnames += additional_categorical_features
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)
# Establish cv method
cv_method = StratifiedKFold(
n_splits=5, shuffle=True
) # Defaults to 5 k-folds in cross_validate method
if CV_METHOD == "logo" or CV_METHOD == "half_logo":
cv_method = LeaveOneGroupOut()
cv_method.get_n_splits(
train_x,
data_y,
groups=data_groups,
)
imputer = SimpleImputer(missing_values=np.nan, strategy="median")
for model_title, model in cmodels.items():
classifier = cross_validate(
model["model"],
X=imputer.fit_transform(train_x),
y=data_y,
groups=data_groups,
cv=cv_method,
n_jobs=-1,
error_score="raise",
scoring=("accuracy", "precision", "recall", "f1"),
)
print("\n-------------------------------------\n")
print("Current cluster:", k, end="\n")
print("Current model:", model_title, end="\n")
print("Acc", np.mean(classifier["test_accuracy"]))
print("Precision", np.mean(classifier["test_precision"]))
print("Recall", np.mean(classifier["test_recall"]))
print("F1", np.mean(classifier["test_f1"]))
print(
f"Largest {N_SL} ACC:",
np.sort(-np.partition(-classifier["test_accuracy"], N_SL)[:N_SL])[::-1],
)
print(
f"Smallest {N_SL} ACC:",
np.sort(np.partition(classifier["test_accuracy"], N_SL)[:N_SL]),
)
cmodels[model_title]["metrics"][0] += np.mean(classifier["test_accuracy"])
cmodels[model_title]["metrics"][1] += np.mean(classifier["test_precision"])
cmodels[model_title]["metrics"][2] += np.mean(classifier["test_recall"])
cmodels[model_title]["metrics"][3] += np.mean(classifier["test_f1"])
# %% jupyter={"source_hidden": true}
# Get overall results
scores = cm.get_total_models_scores(n_clusters=N_CLUSTERS)
# %%
PATH_OUTPUT = Path("..") / Path("presentation/results")
path_output_full = PATH_OUTPUT / (
TARGET_VARIABLE
+ "_"
+ SEGMENT_LENGTH
+ "_classification_"
+ CV_METHOD
+ str(BINS)
+ "_clust_"
+ CLUST_COL
+ str(N_CLUSTERS)
+ ".csv"
)
scores.to_csv(path_output_full, index=False)

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exploration/ml_pipeline_classification_with_clustering_2_class.py
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# ---
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# ---
# %% jupyter={"source_hidden": true}
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy import stats
from sklearn.cluster import KMeans
from sklearn.impute import SimpleImputer
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
from sklearn.model_selection import train_test_split
from machine_learning.classification_models import ClassificationModels
from machine_learning.helper import impute_encode_categorical_features
# %% [markdown]
# ## Set script's parameters
#
# %%
n_clusters = 3 # Number of clusters (could be regarded as a hyperparameter)
n_sl = 3 # Number of largest/smallest accuracies (of particular CV) outputs
# %%
PATH_BASE = Path("E:/STRAWresults/20230415")
SEGMENT_TYPE = "period"
print("SEGMENT_TYPE: " + SEGMENT_TYPE)
SEGMENT_LENGTH = "30_minutes_before"
print("SEGMENT_LENGTH: " + SEGMENT_LENGTH)
TARGET_VARIABLE = "appraisal_stressfulness"
print("TARGET_VARIABLE: " + TARGET_VARIABLE)
if ("appraisal" in TARGET_VARIABLE) and ("stressfulness" in TARGET_VARIABLE):
TARGET_VARIABLE += "_"
TARGET_VARIABLE += SEGMENT_TYPE
PATH_FULL = PATH_BASE / SEGMENT_LENGTH / ("input_" + TARGET_VARIABLE + "_mean.csv")
model_input = pd.read_csv(PATH_FULL)
if SEGMENT_LENGTH == "daily":
DAY_LENGTH = "daily" # or "working"
print(DAY_LENGTH)
model_input = model_input[model_input["local_segment"].str.contains(DAY_LENGTH)]
# %% jupyter={"source_hidden": true}
CLUST_COL = "limesurvey_demand_control_ratio"
print("CLUST_COL: " + CLUST_COL)
BINS = [-1, 0, 4]
print("BINS: " + str(BINS))
index_columns = [
"local_segment",
"local_segment_label",
"local_segment_start_datetime",
"local_segment_end_datetime",
]
model_input[CLUST_COL].describe()
# %% jupyter={"source_hidden": true}
# Filter-out outlier rows by clust_col
model_input = model_input[(np.abs(stats.zscore(model_input[CLUST_COL])) < 3)]
uniq = model_input[[CLUST_COL, "pid"]].drop_duplicates().reset_index(drop=True)
plt.bar(uniq["pid"], uniq[CLUST_COL])
# %% jupyter={"source_hidden": true}
# Get clusters by cluster col & and merge the clusters to main df
km = KMeans(n_clusters=n_clusters).fit_predict(uniq.set_index("pid"))
np.unique(km, return_counts=True)
uniq["cluster"] = km
print(uniq)
model_input = model_input.merge(uniq[["pid", "cluster"]])
# %% jupyter={"source_hidden": true}
model_input.set_index(index_columns, inplace=True)
# %% jupyter={"source_hidden": true}
# Create dict with classification ml models
cm = ClassificationModels()
cmodels = cm.get_cmodels()
# %%
model_input["target"].value_counts()
# %% jupyter={"source_hidden": true}
for k in range(n_clusters):
model_input_subset = model_input[model_input["cluster"] == k].copy()
# Takes 10th percentile and above 90th percentile as the test set -> the rest for the training set. Only two classes, seperated by z-score of 0.
# model_input_subset['numerical_target'] = model_input_subset['target']
model_input_subset.loc[:, "target"] = pd.cut(
model_input_subset.loc[:, "target"], bins=BINS, labels=[0, 1], right=True
)
# p15 = np.percentile(model_input_subset['numerical_target'], 15)
# p85 = np.percentile(model_input_subset['numerical_target'], 85)
# Treat categorical features
model_input_subset = impute_encode_categorical_features(model_input_subset)
# Split to train, validate, and test subsets
# train_set = model_input_subset[(model_input_subset['numerical_target'] > p15) & (model_input_subset['numerical_target'] < p85)].drop(['numerical_target'], axis=1)
# test_set = model_input_subset[(model_input_subset['numerical_target'] <= p15) | (model_input_subset['numerical_target'] >= p85)].drop(['numerical_target'], axis=1)
train_set, test_set = train_test_split(
model_input_subset,
test_size=0.3,
stratify=model_input_subset["pid"],
random_state=42,
)
print(train_set["target"].value_counts())
print(test_set["target"].value_counts())
train_x, train_y = train_set.drop(["target", "pid"], axis=1), train_set["target"]
validate_x, test_x, validate_y, test_y = train_test_split(
test_set.drop(["target", "pid"], axis=1),
test_set["target"],
test_size=0.50,
random_state=42,
)
# Impute missing values
imputer = SimpleImputer(missing_values=np.nan, strategy="median")
train_x = imputer.fit_transform(train_x)
validate_x = imputer.fit_transform(validate_x)
test_x = imputer.fit_transform(test_x)
for model_title, model in cmodels.items():
model["model"].fit(train_x, train_y)
y_pred = model["model"].predict(validate_x)
acc = accuracy_score(validate_y, y_pred)
prec = precision_score(validate_y, y_pred)
rec = recall_score(validate_y, y_pred)
f1 = f1_score(validate_y, y_pred)
print("\n-------------------------------------\n")
print("Current cluster:", k, end="\n")
print("Current model:", model_title, end="\n")
print("Acc", acc)
print("Precision", prec)
print("Recall", rec)
print("F1", f1)
cmodels[model_title]["metrics"][0] += acc
cmodels[model_title]["metrics"][1] += prec
cmodels[model_title]["metrics"][2] += rec
cmodels[model_title]["metrics"][3] += f1
# %% jupyter={"source_hidden": true}
# Get overall results
scores = cm.get_total_models_scores(n_clusters=n_clusters)
# %%
print(scores)
# %%
PATH_OUTPUT = Path("..") / Path("presentation/results")
path_output_full = PATH_OUTPUT / (
TARGET_VARIABLE
+ "_"
+ SEGMENT_LENGTH
+ "_classification"
+ str(BINS)
+ "_CLUST_"
+ CLUST_COL
+ +str(n_clusters)
+ ".csv"
)
scores.to_csv(path_output_full, index=False)

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# ---
# %%
import os
import sys
import pandas as pd
from machine_learning.helper import (
impute_encode_categorical_features,
prepare_cross_validator,
prepare_sklearn_data_format,
run_all_regression_models,
)
nb_dir = os.path.split(os.getcwd())[0]
if nb_dir not in sys.path:
sys.path.append(nb_dir)
# %%
model_input = pd.read_csv(
"../data/intradaily_30_min_all_targets/input_JCQ_job_demand_mean.csv"
)
# %%
model_input = model_input[model_input["local_segment"].str.contains("daily")]
# %%
CV_METHOD = "logo" # logo, half_logo, 5kfold
model_input_encoded = impute_encode_categorical_features(model_input)
# %%
data_x, data_y, data_groups = prepare_sklearn_data_format(
model_input_encoded, CV_METHOD
)
cross_validator = prepare_cross_validator(data_x, data_y, data_groups, CV_METHOD)
# %%
data_y.head()
# %%
data_y.tail()
# %%
data_y.shape
# %%
scores = run_all_regression_models(data_x, data_y, data_groups, cross_validator)
# %%
scores.to_csv(
"../presentation/JCQ_supervisor_support_regression_" + CV_METHOD + ".csv",
index=False,
)

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exploration/ml_pipeline_stress_event_cleaned.py
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# ---
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# ---
# %% 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
from sklearn.model_selection import LeaveOneGroupOut, LeavePGroupsOut, cross_val_score, cross_validate
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.impute import SimpleImputer
from sklearn.dummy import DummyRegressor
import xgboost as xg
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
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/stressfulness_event/input_appraisal_stressfulness_event_mean.csv")
# %% jupyter={"source_hidden": true}
index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
model_input.set_index(index_columns, inplace=True)
cv_method = 'half_logo'
if cv_method == 'logo':
data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"]
else:
model_input[(model_input['pid'] == "p037") | (model_input['pid'] == "p064") | (model_input['pid'] == "p092")]
model_input['pid_index'] = model_input.groupby('pid').cumcount()
model_input['pid_count'] = model_input.groupby('pid')['pid'].transform('count')
model_input["pid_index"] = (model_input['pid_index'] / model_input['pid_count'] + 1).round()
model_input["pid_half"] = model_input["pid"] + "_" + model_input["pid_index"].astype(int).astype(str)
data_x, data_y, data_groups = model_input.drop(["target", "pid", "pid_index", "pid_half"], axis=1), model_input["target"], model_input["pid_half"]
# %% jupyter={"source_hidden": true}
categorical_feature_colnames = ["gender", "startlanguage"]
additional_categorical_features = [col for col in data_x.columns if "mostcommonactivity" in col or "homelabel" in col]
categorical_feature_colnames += additional_categorical_features
# %% 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,
)
# Defaults to 5 k folds in cross_validate method
if cv_method != 'logo' and cv_method != 'half_logo':
logo = None
# %% jupyter={"source_hidden": true}
sum(data_y.isna())
# %% [markdown]
# ### Baseline: Dummy Regression (mean)
# %%
dummy_regr = DummyRegressor(strategy="mean")
# %% jupyter={"source_hidden": true}
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
# %% jupyter={"source_hidden": true}
lin_reg_scores = cross_validate(
dummy_regr,
X=imputer.fit_transform(train_x),
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
)
print("Negative Mean Squared Error", np.nanmedian(lin_reg_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.nanmedian(lin_reg_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.nanmedian(lin_reg_scores['test_neg_root_mean_squared_error']))
print("R2", np.nanmedian(lin_reg_scores['test_r2']))
# %% [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_validate(
lin_reg_rapids,
X=imputer.fit_transform(train_x),
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
)
print("Negative Mean Squared Error", np.nanmedian(lin_reg_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.nanmedian(lin_reg_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.nanmedian(lin_reg_scores['test_neg_root_mean_squared_error']))
print("R2", np.nanmedian(lin_reg_scores['test_r2']))
# %% [markdown]
# ### XGBRegressor Linear Regression
# %% jupyter={"source_hidden": true}
xgb_r = xg.XGBRegressor(objective ='reg:squarederror', n_estimators = 10)
# %% jupyter={"source_hidden": true}
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
# %% jupyter={"source_hidden": true}
xgb_reg_scores = cross_validate(
xgb_r,
X=imputer.fit_transform(train_x),
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
)
print("Negative Mean Squared Error", np.nanmedian(xgb_reg_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.nanmedian(xgb_reg_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.nanmedian(xgb_reg_scores['test_neg_root_mean_squared_error']))
print("R2", np.nanmedian(xgb_reg_scores['test_r2']))
# %% [markdown]
# ### XGBRegressor Pseudo Huber Error Regression
# %% jupyter={"source_hidden": true}
xgb_psuedo_huber_r = xg.XGBRegressor(objective ='reg:pseudohubererror', n_estimators = 10)
# %% jupyter={"source_hidden": true}
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
# %% jupyter={"source_hidden": true}
xgb_psuedo_huber_reg_scores = cross_validate(
xgb_psuedo_huber_r,
X=imputer.fit_transform(train_x),
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
)
print("Negative Mean Squared Error", np.nanmedian(xgb_psuedo_huber_reg_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.nanmedian(xgb_psuedo_huber_reg_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.nanmedian(xgb_psuedo_huber_reg_scores['test_neg_root_mean_squared_error']))
print("R2", np.nanmedian(xgb_psuedo_huber_reg_scores['test_r2']))
# %% [markdown]
# ### Ridge regression
# %% jupyter={"source_hidden": true}
ridge_reg = linear_model.Ridge(alpha=.5)
# %% tags=[] jupyter={"source_hidden": true}
ridge_reg_scores = cross_validate(
ridge_reg,
X=imputer.fit_transform(train_x),
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
)
print("Negative Mean Squared Error", np.nanmedian(ridge_reg_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.nanmedian(ridge_reg_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.nanmedian(ridge_reg_scores['test_neg_root_mean_squared_error']))
print("R2", np.nanmedian(ridge_reg_scores['test_r2']))
# %% [markdown]
# ### Lasso
# %% jupyter={"source_hidden": true}
lasso_reg = linear_model.Lasso(alpha=0.1)
# %% jupyter={"source_hidden": true}
lasso_reg_score = cross_validate(
lasso_reg,
X=imputer.fit_transform(train_x),
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
)
print("Negative Mean Squared Error", np.nanmedian(lasso_reg_score['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.nanmedian(lasso_reg_score['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.nanmedian(lasso_reg_score['test_neg_root_mean_squared_error']))
print("R2", np.nanmedian(lasso_reg_score['test_r2']))
# %% [markdown]
# ### Bayesian Ridge
# %% jupyter={"source_hidden": true}
bayesian_ridge_reg = linear_model.BayesianRidge()
# %% jupyter={"source_hidden": true}
bayesian_ridge_reg_score = cross_validate(
bayesian_ridge_reg,
X=imputer.fit_transform(train_x),
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
)
print("Negative Mean Squared Error", np.nanmedian(bayesian_ridge_reg_score['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.nanmedian(bayesian_ridge_reg_score['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.nanmedian(bayesian_ridge_reg_score['test_neg_root_mean_squared_error']))
print("R2", np.nanmedian(bayesian_ridge_reg_score['test_r2']))
# %% [markdown]
# ### RANSAC (outlier robust regression)
# %% jupyter={"source_hidden": true}
ransac_reg = linear_model.RANSACRegressor()
# %% jupyter={"source_hidden": true}
ransac_reg_scores = cross_validate(
ransac_reg,
X=imputer.fit_transform(train_x),
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
)
print("Negative Mean Squared Error", np.nanmedian(ransac_reg_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.nanmedian(ransac_reg_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.nanmedian(ransac_reg_scores['test_neg_root_mean_squared_error']))
print("R2", np.nanmedian(ransac_reg_scores['test_r2']))
# %% [markdown]
# ### Support vector regression
# %% jupyter={"source_hidden": true}
svr = svm.SVR()
# %% jupyter={"source_hidden": true}
svr_scores = cross_validate(
svr,
X=imputer.fit_transform(train_x),
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
)
print("Negative Mean Squared Error", np.nanmedian(svr_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.nanmedian(svr_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.nanmedian(svr_scores['test_neg_root_mean_squared_error']))
print("R2", np.nanmedian(svr_scores['test_r2']))
# %% [markdown]
# ### Kernel Ridge regression
# %% jupyter={"source_hidden": true}
kridge = kernel_ridge.KernelRidge()
# %% jupyter={"source_hidden": true}
kridge_scores = cross_validate(
kridge,
X=imputer.fit_transform(train_x),
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
)
print("Negative Mean Squared Error", np.nanmedian(kridge_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.nanmedian(kridge_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.nanmedian(kridge_scores['test_neg_root_mean_squared_error']))
print("R2", np.nanmedian(kridge_scores['test_r2']))
# %% [markdown]
# ### Gaussian Process Regression
# %% jupyter={"source_hidden": true}
gpr = gaussian_process.GaussianProcessRegressor()
# %% jupyter={"source_hidden": true}
gpr_scores = cross_validate(
gpr,
X=imputer.fit_transform(train_x),
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
)
print("Negative Mean Squared Error", np.nanmedian(gpr_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.nanmedian(gpr_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.nanmedian(gpr_scores['test_neg_root_mean_squared_error']))
print("R2", np.nanmedian(gpr_scores['test_r2']))
# %%

217
exploration/test_JCQ_reversal.py
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@ -1,217 +0,0 @@
# ---
# jupyter:
# jupytext:
# text_representation:
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.14.5
# kernelspec:
# display_name: straw2analysis
# language: python
# name: straw2analysis
# ---
# %%
import pandas as pd
from features.esm_JCQ import DICT_JCQ_DEMAND_CONTROL_REVERSE
# %%
limesurvey_questions = pd.read_csv(
"E:/STRAWbaseline/survey637813+question_text.csv", header=None
).T
# %%
limesurvey_questions
# %%
limesurvey_questions[["code", "text"]] = limesurvey_questions[0].str.split(
r"\.\s", expand=True, n=1
)
# %%
limesurvey_questions
# %%
demand_reverse_lime_rows = (
limesurvey_questions["text"].str.startswith(" [Od mene se ne zahteva,")
| limesurvey_questions["text"].str.startswith(" [Imam dovolj časa, da končam")
| limesurvey_questions["text"].str.startswith(
" [Pri svojem delu se ne srečujem s konfliktnimi"
)
)
control_reverse_lime_rows = limesurvey_questions["text"].str.startswith(
" [Moje delo vključuje veliko ponavljajočega"
) | limesurvey_questions["text"].str.startswith(
" [Pri svojem delu imam zelo malo svobode"
)
# %%
demand_reverse_lime = limesurvey_questions[demand_reverse_lime_rows]
demand_reverse_lime.loc[:, "qid"] = demand_reverse_lime["code"].str.extract(
r"\[(\d+)\]"
)
control_reverse_lime = limesurvey_questions[control_reverse_lime_rows]
control_reverse_lime.loc[:, "qid"] = control_reverse_lime["code"].str.extract(
r"\[(\d+)\]"
)
# %%
limesurvey_questions.loc[89, "text"]
# %%
limesurvey_questions[limesurvey_questions["code"].str.startswith("JobEisen")]
# %%
demand_reverse_lime
# %%
control_reverse_lime
# %%
participant_info = pd.read_csv(
"C:/Users/junos/Documents/FWO-ARRS/Analysis/straw2analysis/rapids/data/raw/p031/participant_baseline_raw.csv",
parse_dates=["date_of_birth"],
)
# %%
participant_info_t = participant_info.T
# %%
rows_baseline = participant_info_t.index
# %%
rows_demand = rows_baseline.str.startswith("JobEisen") & ~rows_baseline.str.endswith(
"Time"
)
# %%
rows_baseline[rows_demand]
# %%
limesurvey_control = (
participant_info_t[rows_demand]
.reset_index()
.rename(columns={"index": "question", 0: "score_original"})
)
# %%
limesurvey_control
# %%
limesurvey_control["qid"] = (
limesurvey_control["question"].str.extract(r"\[(\d+)\]").astype(int)
)
# %%
limesurvey_control["question"].str.extract(r"\[(\d+)\]").astype(int)
# %%
limesurvey_control["score"] = limesurvey_control["score_original"]
# %%
limesurvey_control["qid"][0]
# %%
rows_demand_reverse = limesurvey_control["qid"].isin(
DICT_JCQ_DEMAND_CONTROL_REVERSE.keys()
)
limesurvey_control.loc[rows_demand_reverse, "score"] = (
4 + 1 - limesurvey_control.loc[rows_demand_reverse, "score_original"]
)
# %%
JCQ_DEMAND = "JobEisen"
JCQ_CONTROL = "JobControle"
dict_JCQ_demand_control_reverse = {
JCQ_DEMAND: {
3: " [Od mene se ne zahteva,",
4: " [Imam dovolj časa, da končam",
5: " [Pri svojem delu se ne srečujem s konfliktnimi",
},
JCQ_CONTROL: {
2: " |Moje delo vključuje veliko ponavljajočega",
6: " [Pri svojem delu imam zelo malo svobode",
},
}
# %%
limesurvey_control
# %%
test = pd.DataFrame(
data={"question": "one", "score_original": 3, "score": 3, "qid": 10}, index=[0]
)
# %%
pd.concat([test, limesurvey_control]).reset_index()
# %%
limesurvey_control["score"].sum()
# %%
rows_demand_reverse
# %%
dict_JCQ_demand_control_reverse[JCQ_DEMAND].keys()
# %%
limesurvey_control
# %%
DEMAND_CONTROL_RATIO_MIN = 5 / (9 * 4)
DEMAND_CONTROL_RATIO_MAX = (4 * 5) / 9
JCQ_NORMS = {
"F": {
0: DEMAND_CONTROL_RATIO_MIN,
1: 0.45,
2: 0.52,
3: 0.62,
4: DEMAND_CONTROL_RATIO_MAX,
},
"M": {
0: DEMAND_CONTROL_RATIO_MIN,
1: 0.41,
2: 0.48,
3: 0.56,
4: DEMAND_CONTROL_RATIO_MAX,
},
}
# %%
JCQ_NORMS[participant_info.loc[0, "gender"]][0]
# %%
participant_info_t.index.str.startswith("JobControle")
# %%
columns_baseline = participant_info.columns
# %%
columns_demand = columns_baseline.str.startswith(
"JobControle"
) & ~columns_baseline.str.endswith("Time")
# %%
columns_baseline[columns_demand]
# %%
participant_control = participant_info.loc[:, columns_demand]
# %%
participant_control["id"] = participant_control.index
# %%
participant_control
# %%
pd.wide_to_long(
participant_control,
stubnames="JobControle",
i="id",
j="qid",
sep="[",
suffix="(\\d+)]",
)

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91
features/ambient.py
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@ -1,91 +0,0 @@
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

55
features/communication.py
View File

@ -8,21 +8,14 @@ from setup import db_engine, session
call_types = {1: "incoming", 2: "outgoing", 3: "missed"}
sms_types = {1: "received", 2: "sent"}
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"]
+ ["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"]
)
# FEATURES_CALLS =
# ["no_calls_all",
@ -30,26 +23,21 @@ FEATURES_CALLS = list(FILL_NA_CALLS.keys())
# "duration_total_incoming", "duration_total_outgoing",
# "duration_max_incoming", "duration_max_outgoing",
# "no_incoming_ratio", "no_outgoing_ratio",
# "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())
# "no_contacts"]
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_sms"]
# "no_contacts"]
FEATURES_CALLS_SMS_PROP = [
FEATURES_CONTACT = [
"proportion_calls_all",
"proportion_calls_incoming",
"proportion_calls_outgoing",
@ -57,15 +45,6 @@ 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:
"""

193
features/esm.py
View File

@ -20,47 +20,11 @@ ANSWER_DAY_OFF = "DayOff3421"
ANSWER_SET_EVENING = "DayFinishedSetEvening"
MAX_MORNING_LENGTH = 3
# When the participant was not yet at work at the time of the first (morning) EMA,
# When the participants was not yet at work at the time of the first (morning) EMA,
# only three items were answered.
# Two sleep related items and one indicating NOT starting work yet.
# Daytime EMAs are all longer, in fact they always consist of at least 6 items.
QUESTIONNAIRE_IDS = {
"sleep_quality": 1,
"PANAS_positive_affect": 8,
"PANAS_negative_affect": 9,
"JCQ_job_demand": 10,
"JCQ_job_control": 11,
"JCQ_supervisor_support": 12,
"JCQ_coworker_support": 13,
"PFITS_supervisor": 14,
"PFITS_coworkers": 15,
"UWES_vigor": 16,
"UWES_dedication": 17,
"UWES_absorption": 18,
"COPE_active": 19,
"COPE_support": 20,
"COPE_emotions": 21,
"balance_life_work": 22,
"balance_work_life": 23,
"recovery_experience_detachment": 24,
"recovery_experience_relaxation": 25,
"symptoms": 26,
"appraisal_stressfulness_event": 87,
"appraisal_threat": 88,
"appraisal_challenge": 89,
"appraisal_event_time": 90,
"appraisal_event_duration": 91,
"appraisal_event_work_related": 92,
"appraisal_stressfulness_period": 93,
"late_work": 94,
"work_hours": 95,
"left_work": 96,
"activities": 97,
"coffee_breaks": 98,
"at_work_yet": 99,
}
def get_esm_data(usernames: Collection) -> pd.DataFrame:
"""
@ -88,8 +52,6 @@ def get_esm_data(usernames: Collection) -> pd.DataFrame:
def preprocess_esm(df_esm: pd.DataFrame) -> pd.DataFrame:
"""
Convert timestamps and expand JSON column.
Convert timestamps into human-readable datetimes and dates
and expand the JSON column into several Pandas DF columns.
@ -101,8 +63,7 @@ def preprocess_esm(df_esm: pd.DataFrame) -> pd.DataFrame:
Returns
-------
df_esm_preprocessed: pd.DataFrame
A dataframe with added columns: datetime in Ljubljana timezone
and all fields from ESM_JSON column.
A dataframe with added columns: datetime in Ljubljana timezone and all fields from ESM_JSON column.
"""
df_esm = helper.get_date_from_timestamp(df_esm)
@ -115,39 +76,31 @@ def preprocess_esm(df_esm: pd.DataFrame) -> pd.DataFrame:
def classify_sessions_by_completion(df_esm_preprocessed: pd.DataFrame) -> pd.DataFrame:
"""
For each distinct EMA session, determine how the participant responded to it.
Possible outcomes are: SESSION_STATUS_UNANSWERED, SESSION_STATUS_DAY_FINISHED,
and SESSION_STATUS_COMPLETE
Possible outcomes are: SESSION_STATUS_UNANSWERED, SESSION_STATUS_DAY_FINISHED, and SESSION_STATUS_COMPLETE
This is done in three steps.
First, the esm_status is considered.
If any of the ESMs in a session has a status *other than* "answered",
then this session is taken as unfinished.
If any of the ESMs in a session has a status *other than* "answered", then this session is taken as unfinished.
Second, the sessions which do not represent full questionnaires are identified.
These are sessions where participants only marked they are finished with the day
or have not yet started working.
These are sessions where participants only marked they are finished with the day or have not yet started working.
Third, the sessions with only one item are marked with their trigger.
We never offered questionnaires with single items,
so we can be sure these are unfinished.
We never offered questionnaires with single items, so we can be sure these are unfinished.
Finally, all sessions that remain are marked as completed.
By going through different possibilities in expl_esm_adherence.ipynb,
this turned out to be a reasonable option.
By going through different possibilities in expl_esm_adherence.ipynb, this turned out to be a reasonable option.
Parameters
----------
df_esm_preprocessed: pd.DataFrame
A preprocessed dataframe of esm data,
which must include the session ID (esm_session).
A preprocessed dataframe of esm data, which must include the session ID (esm_session).
Returns
-------
df_session_counts: pd.Dataframe
A dataframe of all sessions (grouped by GROUP_SESSIONS_BY)
with their statuses and the number of items.
A dataframe of all sessions (grouped by GROUP_SESSIONS_BY) with their statuses and the number of items.
"""
sessions_grouped = df_esm_preprocessed.groupby(GROUP_SESSIONS_BY)
@ -202,22 +155,17 @@ def classify_sessions_by_completion(df_esm_preprocessed: pd.DataFrame) -> pd.Dat
def classify_sessions_by_time(df_esm_preprocessed: pd.DataFrame) -> pd.DataFrame:
"""
Classify EMA sessions into morning, workday, or evening.
For each EMA session, determine the time of the first user answer
and its time type (morning, workday, or evening).
For each EMA session, determine the time of the first user answer and its time type (morning, workday, or evening.)
Parameters
----------
df_esm_preprocessed: pd.DataFrame
A preprocessed dataframe of esm data,
which must include the session ID (esm_session).
A preprocessed dataframe of esm data, which must include the session ID (esm_session).
Returns
-------
df_session_time: pd.DataFrame
A dataframe of all sessions (grouped by GROUP_SESSIONS_BY)
with their time type and timestamp of first answer.
A dataframe of all sessions (grouped by GROUP_SESSIONS_BY) with their time type and timestamp of first answer.
"""
df_session_time = (
df_esm_preprocessed.sort_values(["participant_id", "datetime_lj"])
@ -231,17 +179,13 @@ def classify_sessions_by_completion_time(
df_esm_preprocessed: pd.DataFrame,
) -> pd.DataFrame:
"""
Classify sessions and correct the time type.
The point of this function is to not only classify sessions
by using the previously defined functions.
The point of this function is to not only classify sessions by using the previously defined functions.
It also serves to "correct" the time type of some EMA sessions.
A morning questionnaire could seamlessly transition into a daytime questionnaire,
if the participant was already at work.
In this case, the "time" label changed mid-session.
Because of the way classify_sessions_by_time works,
this questionnaire was classified as "morning".
Because of the way classify_sessions_by_time works, this questionnaire was classified as "morning".
But for all intents and purposes, it can be treated as a "daytime" EMA.
The way this scenario is differentiated from a true "morning" questionnaire,
@ -250,16 +194,13 @@ def classify_sessions_by_completion_time(
Parameters
----------
df_esm_preprocessed: pd.DataFrame
A preprocessed dataframe of esm data,
which must include the session ID (esm_session).
A preprocessed dataframe of esm data, which must include the session ID (esm_session).
Returns
-------
df_session_counts_time: pd.DataFrame
A dataframe of all sessions (grouped by GROUP_SESSIONS_BY) with statuses,
the number of items,
their time type (with some morning EMAs reclassified)
and timestamp of first answer.
A dataframe of all sessions (grouped by GROUP_SESSIONS_BY) with statuses, the number of items,
their time type (with some morning EMAs reclassified) and timestamp of first answer.
"""
df_session_counts = classify_sessions_by_completion(df_esm_preprocessed)
@ -278,8 +219,7 @@ def classify_sessions_by_completion_time(
def clean_up_esm(df_esm_preprocessed: pd.DataFrame) -> pd.DataFrame:
"""
Eliminate invalid ESM responses.
This function eliminates invalid ESM responses.
It removes unanswered ESMs and those that indicate end of work and similar.
It also extracts a numeric answer from strings such as "4 - I strongly agree".
@ -316,100 +256,3 @@ def clean_up_esm(df_esm_preprocessed: pd.DataFrame) -> pd.DataFrame:
)
)
return df_esm_clean
def increment_answers(df_esm_clean: pd.DataFrame, increment_by=1):
"""
Increment answers to keep in line with original scoring.
We always used 0 for the lowest value of user answer.
Some scales originally used other scoring, such as starting from 1.
This restores original scoring so that the values are comparable to references.
Parameters
----------
df_esm_clean: pd.DataFrame
A cleaned ESM dataframe, which must also include esm_user_answer_numeric.
increment_by:
A number to add to the user answer.
Returns
-------
df_esm_clean: pd.DataFrame
The same df with addition of a column 'esm_user_answer_numeric'.
"""
try:
df_esm_clean = df_esm_clean.assign(
esm_user_score=lambda x: x.esm_user_answer_numeric + increment_by
)
except AttributeError as e:
print("Please, clean the dataframe first using features.esm.clean_up_esm.")
print(e)
return df_esm_clean
def reassign_question_ids(
df_esm_cleaned: pd.DataFrame, question_ids_content: dict
) -> pd.DataFrame:
"""
Fix question IDs to match their actual content.
Unfortunately, when altering the protocol to adapt to COVID pandemic,
we did not retain original question IDs.
This means that for participants before 2021, they are different
from for the rest of them.
This function searches for question IDs by matching their strings.
Parameters
----------
df_esm_cleaned: pd.DataFrame
A cleaned up dataframe, which must also include esm_user_answer_numeric.
question_ids_content: dict
A dictionary, linking question IDs with their content ("instructions").
Returns
-------
df_esm_fixed: pd.DataFrame
The same dataframe but with fixed question IDs.
"""
df_esm_unique_questions = (
df_esm_cleaned.groupby("question_id")
.esm_instructions.value_counts()
.rename()
.reset_index()
)
# Tabulate all possible answers to each question (group by question ID).
# First, check that we anticipated all esm instructions.
for q_id in question_ids_content.keys():
# Look for all questions ("instructions") occurring in the dataframe.
actual_questions = df_esm_unique_questions.loc[
df_esm_unique_questions["question_id"] == q_id,
"esm_instructions",
]
# These are all answers to a given question (by q_id).
questions_matches = actual_questions.str.startswith(
question_ids_content.get(q_id)
)
# See if they are expected, i.e. included in the dictionary.
if ~actual_questions.all():
print("One of the questions that occur in the data was undefined.")
print("This were the questions found in the data: ")
raise KeyError(actual_questions[~questions_matches])
# In case there is an unexpected answer, raise an exception.
# Next, replace question IDs.
df_esm_fixed = df_esm_cleaned.copy()
df_esm_fixed["question_id"] = df_esm_cleaned["esm_instructions"].apply(
lambda x: next(
(
key
for key, values in question_ids_content.items()
if x.startswith(values)
),
None,
)
)
return df_esm_fixed

125
features/esm_COPE.py
View File

@ -1,125 +0,0 @@
COPE_ORIGINAL_MAX = 4
COPE_ORIGINAL_MIN = 1
DICT_COPE_QUESTION_IDS = {
164: (
"I took additional action to try to get rid of the problem",
"Ik deed extra mijn best om er iets aan te doen",
"Vložila sem dodaten napor, da bi rešila problem",
"Vložil sem dodaten napor, da bi rešil problem",
),
165: (
"I concentrated my efforts on doing something about it",
"Ik probeerde de situatie te verbeteren",
"Svoje sile sem usmerila v reševanje nastale situacije",
"Svoje sile sem usmeril v reševanje nastale situacije",
),
166: (
"I did what had to be done, one step at a time",
"Ik deed stap voor stap wat nodig was",
"Naredila sem, kar je bilo potrebno – korak za korakom",
"Naredil sem, kar je bilo potrebno – korak za korakom",
),
167: (
"I took direct action to get around the problem",
"Ik handelde vlug om het probleem te verhelpen",
"Nekaj sem naredila, da sem zaobšla problem",
"Nekaj sem naredil, da sem zaobšel problem",
),
168: (
"I tried to come up with a strategy about what to do",
"Ik probeerde te verzinnen wat ik er aan kon doen",
"Skušala sem najti ustrezen način za rešitev situacije",
"Skušal sem najti ustrezen način za rešitev situacije",
),
169: (
"I made a plan of action",
"Ik maakte een plan",
"Naredila sem načrt za delovanje",
"Naredil sem načrt za delovanje",
),
170: (
"I thought hard about what steps to take",
"Ik dacht hard na over wat ik moest doen",
"Dobro sem premislila, katere korake moram narediti, da rešim problem",
"Dobro sem premislil, katere korake moram narediti, da rešim problem",
),
171: (
"I thought about how I might best handle the problem",
"lk dacht na over hoe ik het probleem het best kon aanpakken",
"Razmišljala sem, kaj bi bilo najbolje narediti s problemom",
"Razmišljal sem, kaj bi bilo najbolje narediti s problemom",
),
172: (
"I asked people who have had similar experiences what they did",
"Ik vroeg aan mensen met dergelijke ervaringen hoe zij reageerden",
"Vprašala sem posameznike s podobnimi izkušnjami, kaj so storili",
"Vprašal sem posameznike s podobnimi izkušnjami, kaj so storili",
),
173: (
"I tried to get advice from someone about what to do",
"lk vroeg advies aan iemand",
"Pri drugih sem poskušala dobiti nasvet, kaj naj storim",
"Pri drugih sem poskušal dobiti nasvet, kaj naj storim",
),
174: (
"I talked to someone to find out more about the situation",
"Ik sprak met iemand om meer te weten te komen over de situatie",
"Z nekom sem se pogovorila, da bi izvedela še kaj o svojem problemu",
"Z nekom sem se pogovoril, da bi izvedel še kaj o svojem problemu",
),
175: (
"I talked to someone who could do something concrete about the problem",
"Ik sprak met iemand die iets aan het probleem kon doen",
"Pogovorila sem se s kom, ki bi lahko naredil kaj konkretnega",
"Pogovoril sem se s kom, ki bi lahko naredil kaj konkretnega",
),
176: (
"I talked to someone about how I felt",
"Ik sprak met iemand over hoe ik mij voelde",
"Z nekom sem se pogovorila o tem, kako sem se počutila",
"Z nekom sem se pogovoril o tem, kako sem se počutil",
),
177: (
"I tried to get emotional support from friends or relatives",
"Ik zocht steun bij vrienden of familie",
"Skušala sem dobiti čustveno podporo prijateljev ali sorodnikov",
"Skušal sem dobiti čustveno podporo prijateljev ali sorodnikov",
),
178: (
"I discussed my feelings with someone",
"lk besprak mijn gevoelens met iemand",
"O svojih občutkih sem se z nekom pogovorila",
"O svojih občutkih sem se z nekom pogovoril",
),
179: (
"I got sympathy and understanding from someone",
"Ik vroeg medeleven en begrip van iemand",
"Poiskala sem naklonjenost in razumevanje drugih",
"Poiskal sem naklonjenost in razumevanje drugih",
),
180: (
"I got upset and let my emotions out",
"Ik raakte van streek",
"Razburila sem se in to tudi pokazala",
"Razburil sem se in to tudi pokazal",
),
181: (
"I let my feelings out",
"Ik toonde mijn gevoelens",
"Svojim čustvom sem dala prosto pot",
"Svojim čustvom sem dal prosto pot",
),
182: (
"I felt a lot of emotional distress and I found myself expressing",
"lk liet duidelijk blijken hoe ellendig ik mij voelde",
"Doživljala sem veliko stresa in opažala, da sem čustva",
"Doživljal sem veliko stresa in opažal, da sem čustva",
),
183: (
"I got upset, and I was really aware of it",
"Ik merkte dat ik erg van streek was",
"Razburila sem se in razmišljala samo o tem",
"Razburil sem se in razmišljal samo o tem",
),
}

35
features/esm_JCQ.py
View File

@ -1,11 +1,9 @@
import pandas as pd
from features.esm import increment_answers
JCQ_ORIGINAL_MAX = 4
JCQ_ORIGINAL_MIN = 1
DICT_JCQ_DEMAND_CONTROL_REVERSE = {
dict_JCQ_demand_control_reverse = {
75: (
"I was NOT asked",
"Men legde mij geen overdreven",
@ -42,14 +40,10 @@ def reverse_jcq_demand_control_scoring(
df_esm_jcq_demand_control: pd.DataFrame,
) -> pd.DataFrame:
"""
Reverse JCQ demand and control answers.
This function recodes answers in Job content questionnaire
by first incrementing them by 1, to be in line with original (1-4) scoring.
Then, some answers are reversed (i.e. 1 becomes 4 etc.),
because the questions are negatively phrased.
These answers are listed in DICT_JCQ_DEMAND_CONTROL_REVERSE
and identified by their question ID.
This function recodes answers in Job content questionnaire by first incrementing them by 1,
to be in line with original (1-4) scoring.
Then, some answers are reversed (i.e. 1 becomes 4 etc.), because the questions are negatively phrased.
These answers are listed in dict_JCQ_demand_control_reverse and identified by their question ID.
However, the existing data is checked against literal phrasing of these questions
to protect against wrong numbering of questions (differing question IDs).
@ -61,8 +55,7 @@ def reverse_jcq_demand_control_scoring(
Returns
-------
df_esm_jcq_demand_control: pd.DataFrame
The same dataframe with a column esm_user_score
containing answers recoded and reversed.
The same dataframe with a column esm_user_score containing answers recoded and reversed.
"""
df_esm_jcq_demand_control_unique_answers = (
df_esm_jcq_demand_control.groupby("question_id")
@ -71,7 +64,7 @@ def reverse_jcq_demand_control_scoring(
.reset_index()
)
# Tabulate all possible answers to each question (group by question ID).
for q_id in DICT_JCQ_DEMAND_CONTROL_REVERSE.keys():
for q_id in dict_JCQ_demand_control_reverse.keys():
# Look through all answers that need to be reversed.
possible_answers = df_esm_jcq_demand_control_unique_answers.loc[
df_esm_jcq_demand_control_unique_answers["question_id"] == q_id,
@ -79,7 +72,7 @@ def reverse_jcq_demand_control_scoring(
]
# These are all answers to a given question (by q_id).
answers_matches = possible_answers.str.startswith(
DICT_JCQ_DEMAND_CONTROL_REVERSE.get(q_id)
dict_JCQ_demand_control_reverse.get(q_id)
)
# See if they are expected, i.e. included in the dictionary.
if ~answers_matches.all():
@ -89,16 +82,18 @@ def reverse_jcq_demand_control_scoring(
# In case there is an unexpected answer, raise an exception.
try:
df_esm_jcq_demand_control = increment_answers(df_esm_jcq_demand_control)
# Increment the original answer by 1 to keep in line
# with traditional scoring (from JCQ_ORIGINAL_MIN to JCQ_ORIGINAL_MAX).
df_esm_jcq_demand_control = df_esm_jcq_demand_control.assign(
esm_user_score=lambda x: x.esm_user_answer_numeric + 1
)
# Increment the original answer by 1
# to keep in line with traditional scoring (JCQ_ORIGINAL_MIN - JCQ_ORIGINAL_MAX).
df_esm_jcq_demand_control[
df_esm_jcq_demand_control["question_id"].isin(
DICT_JCQ_DEMAND_CONTROL_REVERSE.keys()
dict_JCQ_demand_control_reverse.keys()
)
] = df_esm_jcq_demand_control[
df_esm_jcq_demand_control["question_id"].isin(
DICT_JCQ_DEMAND_CONTROL_REVERSE.keys()
dict_JCQ_demand_control_reverse.keys()
)
].assign(
esm_user_score=lambda x: JCQ_ORIGINAL_MAX

138
features/esm_SAM.py
View File

@ -3,9 +3,6 @@ import pandas as pd
import features.esm
SAM_ORIGINAL_MAX = 5
SAM_ORIGINAL_MIN = 1
QUESTIONNAIRE_ID_SAM = {
"event_stress": 87,
"event_threat": 88,
@ -23,107 +20,10 @@ GROUP_QUESTIONNAIRES_BY = [
"device_id",
"esm_session",
]
# Each questionnaire occurs only once within each esm_session on the same device
# within the same participant.
DICT_SAM_QUESTION_IDS = {
87: (
"Was there a particular event that created tension in you?",
"Was er een bepaalde gebeurtenis die spanning veroorzaakte?",
"Je prišlo do kakega dogodka, ki je v vas ustvaril napetost?",
),
88: (
"Did this event make you feel anxious?",
"Voelde je je angstig door deze gebeurtenis?",
"Ste se zaradi tega dogodka počutili tesnob",
),
89: (
"Will the outcome of this event be negative?",
"Zal de uitkomst van deze gebeurtenis negatief zijn?",
"Bo izid tega dogodka negativen?",
),
90: (
"How threatening was this event?",
"Hoe bedreigend was deze gebeurtenis?",
"Kako grozeč je bil ta dogodek?",
),
91: (
"Is this going to have a negative impact on you?",
"Zal dit een negatieve impact op je hebben?",
"Ali bo to negativno vplivalo na vas?",
),
92: (
"Is this going to have a positive impact on you?",
"Zal dit een positief effect op je hebben?",
"Ali bo to pozitivno vplivalo na vas?",
),
93: (
"How eager are you to tackle this event?",
"Hoe graag wil je deze gebeurtenis aanpakken?",
"Kako zagnani ste bili",
),
94: (
"To what extent can you become a stronger person because of this event?",
"In welke mate kan je een sterkere persoon worden door deze gebeurtenis?",
"V kolikšni meri lahko zaradi tega dogodka postanete močnejša oseba?",
),
95: (
"To what extent are you excited thinking about the outcome of this event?",
"In welke mate ben je enthousiast bij de gedachte",
"V kolikšni meri vas misel na izid tega dogodka navdušuje?",
),
96: (
"At what time did this event occur?",
"Hoe laat vond deze gebeurtenis plaats?",
"Kdaj se je ta dogodek zgodil?",
),
97: (
"How long did this event last?",
"Hoe lang duurde deze gebeurtenis?",
"Kako dolgo je trajal ta dogodek?",
),
98: (
"Was/is this event work-related?",
"Was/is deze gebeurtenis werkgerelateerd?",
"Je (bil) ta dogodek povezan s službo?",
"Je bil ali je ta dogodek povezan s službo?",
),
99: (
"Did this overall period create tension in you?",
"Heeft deze globale periode spanning veroorzaakt?",
"Je to obdobje kot celota v vas ustvarilo napetost?",
"Je to celo obdobje v vas ustvarilo napetost?",
),
100: (
"To what extent do you perceive this overall period as stressful?",
"In welke mate ervaar je deze globale periode als stressvol?",
"V kolikšni meri ste to obdobje dojemali kot stresno?",
"V kolikšni meri ste celo to obdobje dojemali kot stresno?",
),
}
# Each questionnaire occurs only once within each esm_session on the same device within the same participant.
def extract_stressful_events(df_esm: pd.DataFrame) -> pd.DataFrame:
"""
Extract information about stressful events.
Participants were asked: "Was there a particular event that created tension in you?"
Then a subset of questions related to this event followed.
This function goes through the follow-up questions one by one
and preprocesses them, so that it adds new columns to the dataframe.
Parameters
----------
df_esm: pd.DataFrame
A raw dataframe of all ESM data.
Returns
-------
df_esm_events: pd.DataFrame
A cleaned up df of Stress Appraisal Measure items with additional columns.
"""
# 0. Select only questions from Stress Appraisal Measure.
df_esm_preprocessed = features.esm.preprocess_esm(df_esm)
df_esm_sam = df_esm_preprocessed[
@ -178,8 +78,7 @@ def extract_stressful_events(df_esm: pd.DataFrame) -> pd.DataFrame:
def calculate_threat_challenge_means(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
"""
This function calculates challenge and threat
(two Stress Appraisal Measure subscales) means,
This function calculates challenge and threat (two Stress Appraisal Measure subscales) means,
for each ESM session (within participants and devices).
It creates a grouped dataframe with means in two columns.
@ -191,8 +90,7 @@ def calculate_threat_challenge_means(df_esm_sam_clean: pd.DataFrame) -> pd.DataF
Returns
-------
df_esm_event_threat_challenge_mean_wide: pd.DataFrame
A dataframe of unique ESM sessions (by participants and devices)
with threat and challenge means.
A dataframe of unique ESM sessions (by participants and devices) with threat and challenge means.
"""
# Select only threat and challenge assessments for events
df_esm_event_threat_challenge = df_esm_sam_clean[
@ -214,8 +112,8 @@ def calculate_threat_challenge_means(df_esm_sam_clean: pd.DataFrame) -> pd.DataF
aggfunc="mean",
)
# Drop unnecessary column values.
df_esm_event_threat_challenge_mean_wide.columns = (
df_esm_event_threat_challenge_mean_wide.columns.get_level_values(1)
df_esm_event_threat_challenge_mean_wide.columns = df_esm_event_threat_challenge_mean_wide.columns.get_level_values(
1
)
df_esm_event_threat_challenge_mean_wide.columns.name = None
df_esm_event_threat_challenge_mean_wide.rename(
@ -291,12 +189,10 @@ def detect_event_work_related(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
def convert_event_time(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
"""
This function only serves to convert the string datetime answer
into a real datetime type.
Errors during this conversion are coerced, meaning that non-datetime answers
are assigned Not a Time (NaT).
NOTE: Since the only available non-datetime answer to this question was
"0 - I do not remember", the NaTs can be interpreted to mean this.
This function only serves to convert the string datetime answer into a real datetime type.
Errors during this conversion are coerced, meaning that non-datetime answers are assigned Not a Time (NaT).
NOTE: Since the only available non-datetime answer to this question was "0 - I do not remember",
the NaTs can be interpreted to mean this.
Parameters
----------
@ -312,13 +208,9 @@ def convert_event_time(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
df_esm_sam_clean["questionnaire_id"] == QUESTIONNAIRE_ID_SAM.get("event_time")
].assign(
event_time=lambda x: pd.to_datetime(
x.esm_user_answer,
errors="coerce",
format="%Y-%m-%d %H:%M:%S %z",
exact=True,
x.esm_user_answer, errors="coerce", infer_datetime_format=True, exact=True
)
)
# Example answer: 2020-09-29 00:05:00 +0200
return df_esm_event_time
@ -349,12 +241,9 @@ def extract_event_duration(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
== QUESTIONNAIRE_ID_SAM.get("event_duration")
].assign(
event_duration=lambda x: pd.to_datetime(
x.esm_user_answer.str.slice(start=0, stop=-6),
errors="coerce",
format="%Y-%m-%d %H:%M:%S",
x.esm_user_answer.str.slice(start=0, stop=-6), errors="coerce"
).dt.time
)
# Example answer: 2020-09-29 00:05:00 +0200
# TODO Explore the values recorded in event_duration and possibly fix mistakes.
# For example, participants reported setting 23:50:00 instead of 00:50:00.
@ -362,7 +251,7 @@ def extract_event_duration(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
# we can determine whether:
# - this event is still going on ("1 - It is still going on")
# - the participant couldn't remember it's duration ("0 - I do not remember")
# Generally, these answers were converted to esm_user_answer_numeric in clean_up_esm
# Generally, these answers were converted to esm_user_answer_numeric in clean_up_esm,
# but only the numeric types of questions and answers.
# Since this was of "datetime" type, convert these specific answers here again.
df_esm_event_duration["event_duration_info"] = np.nan
@ -375,5 +264,4 @@ def extract_event_duration(df_esm_sam_clean: pd.DataFrame) -> pd.DataFrame:
return df_esm_event_duration
# TODO: How many questions about the stressfulness of the period were asked
# and how does this relate to events?
# TODO: How many questions about the stressfulness of the period were asked and how does this relate to events?

30
features/light.py 100644
View File

@ -0,0 +1,30 @@
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

11
features/proximity.py
View File

@ -5,12 +5,7 @@ import pandas as pd
from config.models import Participant, Proximity
from setup import db_engine, session
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())
FEATURES_PROXIMITY = ["freq_prox_near", "prop_prox_near"]
def get_proximity_data(usernames: Collection) -> pd.DataFrame:
@ -83,11 +78,11 @@ def count_proximity(
A dataframe with the count of "near" proximity values and their relative count.
"""
if group_by is None:
group_by = []
group_by = ["participant_id"]
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(["participant_id"] + group_by).sum()[
df_proximity_features = df_proximity.groupby(group_by).sum()[
["bool_prox_near", "bool_prox_far"]
]
df_proximity_features = df_proximity_features.assign(

30
features/timezone.py
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@ -1,30 +0,0 @@
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

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123
machine_learning/classification_models.py
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@ -1,123 +0,0 @@
import pandas as pd
import xgboost as xg
from lightgbm import LGBMClassifier
from sklearn import ensemble, linear_model, naive_bayes, neighbors, svm, tree
from sklearn.dummy import DummyClassifier
class ClassificationModels:
def __init__(self):
self.cmodels = self.init_classification_models()
def get_cmodels(self):
return self.cmodels
def init_classification_models(self):
cmodels = {
"dummy_classifier": {
"model": DummyClassifier(strategy="most_frequent"),
"metrics": [0, 0, 0, 0],
},
"logistic_regression": {
"model": linear_model.LogisticRegression(max_iter=1000),
"metrics": [0, 0, 0, 0],
},
"support_vector_machine": {"model": svm.SVC(), "metrics": [0, 0, 0, 0]},
"gaussian_naive_bayes": {
"model": naive_bayes.GaussianNB(),
"metrics": [0, 0, 0, 0],
},
"stochastic_gradient_descent_classifier": {
"model": linear_model.SGDClassifier(),
"metrics": [0, 0, 0, 0],
},
"knn": {"model": neighbors.KNeighborsClassifier(), "metrics": [0, 0, 0, 0]},
"decision_tree": {
"model": tree.DecisionTreeClassifier(),
"metrics": [0, 0, 0, 0],
},
"random_forest_classifier": {
"model": ensemble.RandomForestClassifier(),
"metrics": [0, 0, 0, 0],
},
"gradient_boosting_classifier": {
"model": ensemble.GradientBoostingClassifier(),
"metrics": [0, 0, 0, 0],
},
"lgbm_classifier": {"model": LGBMClassifier(), "metrics": [0, 0, 0, 0]},
"XGBoost_classifier": {
"model": xg.sklearn.XGBClassifier(),
"metrics": [0, 0, 0, 0],
},
}
return cmodels
def get_total_models_scores(self, n_clusters=1):
scores = pd.DataFrame(columns=["method", "metric", "mean"])
for model_title, model in self.cmodels.items():
scores_df = pd.DataFrame(columns=["method", "metric", "mean"])
print("\n************************************\n")
print("Current model:", model_title, end="\n")
print("Acc:", model["metrics"][0] / n_clusters)
scores_df = pd.concat(
[
scores_df,
pd.DataFrame(
{
"method": model_title,
"metric": "test_accuracy",
"mean": model["metrics"][0] / n_clusters,
},
index=[0],
),
],
ignore_index=True,
)
print("Precision:", model["metrics"][1] / n_clusters)
scores_df = pd.concat(
[
scores_df,
pd.DataFrame(
{
"method": model_title,
"metric": "test_precision",
"mean": model["metrics"][1] / n_clusters,
},
index=[0],
),
],
ignore_index=True,
)
print("Recall:", model["metrics"][2] / n_clusters)
scores_df = pd.concat(
[
scores_df,
pd.DataFrame(
{
"method": model_title,
"metric": "test_recall",
"mean": model["metrics"][2] / n_clusters,
},
index=[0],
),
],
ignore_index=True,
)
print("F1:", model["metrics"][3] / n_clusters)
scores_df = pd.concat(
[
scores_df,
pd.DataFrame(
{
"method": model_title,
"metric": "test_f1",
"mean": model["metrics"][3] / n_clusters,
},
index=[0],
),
],
ignore_index=True,
)
scores = pd.concat([scores, scores_df])
return scores

5
machine_learning/config/minimal_features.yaml
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@ -1,5 +0,0 @@
grouping_variable: date_lj
features:
proximity:
all
participants_usernames: [nokia_0000003]

6
machine_learning/config/minimal_labels.yaml
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@ -1,6 +0,0 @@
grouping_variable: date_lj
labels:
PANAS:
- PA
- NA
participants_usernames: [nokia_0000003]

6
machine_learning/config/prox_comm_PANAS_features.yaml
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@ -1,6 +0,0 @@
grouping_variable: date_lj
features:
proximity:
all
communication:
all

5
machine_learning/config/prox_comm_PANAS_labels.yaml
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@ -1,5 +0,0 @@
grouping_variable: date_lj
labels:
PANAS:
- PA
- NA

128
machine_learning/cross_validation.py
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import os
import sys
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import LeaveOneGroupOut, StratifiedKFold
class CrossValidation():
"""This code implements a CrossValidation class for creating cross validation splits.
"""
def __init__(self, data=None, cv_method='logo'):
"""This method initializes the cv_method argument and optionally prepares the data if supplied.
Args:
cv_method (str, optional): String of cross validation method; options are 'logo', 'half_logo' and '5kfold'.
Defaults to 'logo'.
data (DataFrame, optional): Pandas DataFrame with target, pid columns and other features as columns.
Defaults to None.
"""
self.initialize_cv_method(cv_method)
if data is not None:
self.prepare_data(data)
def prepare_data(self, data):
"""Prepares the data ready to be passed to the cross-validation algorithm, depending on the cv_method type.
For example, if cv_method is set to 'half_logo' new columns 'pid_index', 'pid_count', 'pid_half'
are added and used in the process.
Args:
data (_type_): Pandas DataFrame with target, pid columns and other features as columns.
"""
self.data = data
if self.cv_method == "logo":
data_X, data_y, data_groups = data.drop(["target", "pid"], axis=1), data["target"], data["pid"]
elif self.cv_method == "half_logo":
data['pid_index'] = data.groupby('pid').cumcount()
data['pid_count'] = data.groupby('pid')['pid'].transform('count')
data["pid_index"] = (data['pid_index'] / data['pid_count'] + 1).round()
data["pid_half"] = data["pid"] + "_" + data["pid_index"].astype(int).astype(str)
data_X, data_y, data_groups = data.drop(["target", "pid", "pid_index", "pid_half"], axis=1), data["target"], data["pid_half"]
elif self.cv_method == "Stratified5kfold":
data_X, data_y, data_groups = data.drop(["target", "pid"], axis=1), data["target"], None
self.X, self.y, self.groups = data_X, data_y, data_groups
def initialize_cv_method(self, cv_method):
"""Initializes the given cv_method type. Depending on the type, the appropriate splitting technique is used.
Args:
cv_method (str): The type of cross-validation method to use; options are 'logo', 'half_logo' and '5kfold'.
Raises:
ValueError: If cv_method is not in the list of available methods, it raises an ValueError.
"""
self.cv_method = cv_method
if self.cv_method not in ["logo", "half_logo", "5kfold"]:
raise ValueError("Invalid cv_method input. Correct values are: 'logo', 'half_logo', '5kfold'")
if self.cv_method in ["logo", "half_logo"]:
self.cv = LeaveOneGroupOut()
elif self.cv_method == "Stratified5kfold":
self.cv = StratifiedKFold(n_splits=5, shuffle=True)
def get_splits(self):
"""Returns a generator object containing the cross-validation splits.
Raises:
ValueError: Raises ValueError if no data has been set.
"""
if not self.data.empty:
return self.cv.split(self.X, self.y, self.groups)
else:
raise ValueError("No data has been set. Use 'prepare_data(data)' method to set the data.")
def get_data(self):
"""data getter
Returns:
Pandas DataFrame: Returns the data from the class instance.
"""
return self.data
def get_x_y_groups(self):
"""X, y, and groups data getter
Returns:
Pandas DataFrame: Returns the data from the class instance.
"""
return self.X, self.y, self.groups
def get_train_test_sets(self, split):
"""Gets train and test sets, dependent on the split parameter. This method can be used in a specific splitting context,
where by index we can get train and test sets.
Args:
split (tuple of indices): It represents one iteration of the split generator (see get_splits method).
Returns:
tuple of Pandas DataFrames: This method returns train_X, train_y, test_X, test_y, with correctly indexed rows by split param.
"""
return self.X.iloc[split[0]], self.y.iloc[split[0]], self.X.iloc[split[1]], self.y.iloc[split[1]]
def get_groups_sets(self, split):
if self.groups is None:
return None, None
else:
return self.groups.iloc[split[0]], self.groups.iloc[split[1]]

245
machine_learning/feature_selection.py
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import os
import sys
import warnings
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.feature_selection import SelectKBest, f_classif, mutual_info_classif, f_regression
from sklearn.model_selection import cross_validate, StratifiedKFold, GroupKFold
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import Lasso
""" Feature selection pipeline: a methods that can be used in the wrapper metod alongside other wrapper contents (hyperparameter tuning etc.).
(1) Establish methods for each of the steps in feature selection protocol.
(2) Ensure that above methods are given only a part of data and use appropriate random seeds - to later simulate use case in production.
(3) Implement a method which gives graphical exploration of (1) (a) and (b) steps of the feature selection.
(4) Prepare a core method that can be fit into a wrapper (see sklearn wrapper methods) and integrates methods from (1)
"""
class FeatureSelection:
def __init__(self, X, y, groups):
self.X = X
self.y = y
self.groups = groups
def select_best_feature(self, features, method="remove", ml_category="classification", ml_subcategory="bin", metric="recall", stored_features=[]):
"""The method selects the best feature by testing the prediction on the feature set with or without the current feature.
The "remove" method removes a particular feature and predicts on the test set without it. The "add" method adds a particular
feature to the previously established feature set (stored_features). The best feature is selected dependent on the metric
specified as a parameter.
Args:
df (DataFrame): Input data on which the predictions will be made.
features (list): List of features to select the best/worst from
method (str, optional): remove or add features. Defaults to "remove".
ml_category (str, optional): Either classification or regression ml problem controls the ML algorithm and metric.
Defaults to "classification".
ml_subcategory (str, optional): In case of classification '_bin' for binary classification
and 'multi' for multiclass classification. For regression an empty string '' is sufficient.
Defaults to "bin".
metric (str, optional): Selected metric with which the best/worst feature will be determined. Defaults to "recall".
stored_features (list, optional): In case if method is 'add', stored features refer to the features that had been previously added. Defaults to [].
Raises:
ValueError: Raises if classification or regression metrics are not recognised if a specific ml_type is selected.
ValueError: If unknown ml_type is chosen.
Returns:
tuple: name of the best feature, best feature score, best feature score standard deviation.
"""
best_feature = None
# Validacije tipov ML in specificiranimi metrikami
if ml_category == "classification":
if ml_subcategory == "bin" and metric not in ['accuracy', 'precision', 'recall', 'f1']:
raise ValueError("Classification metric not recognized. Please choose 'accuracy', 'precision', 'recall' and/or 'f1'")
elif ml_subcategory == "multi":
ml_subcategory_error = False
if metric != "accuracy" and "_" in metric:
metric_s, metric_t = metric.split("_")
if metric_s not in ['accuracy', 'precision', 'recall', 'f1'] or metric_t not in ['micro', 'macro', 'weighted']:
ml_subcategory_error = True
else:
ml_subcategory_error = True
if ml_subcategory_error:
raise ValueError(""""Classification metric for multi-class classification must be specified precisely.
Available metric are: 'accuracy', 'precision', 'recall' and 'f1'.
Only accuracy must be specified as 'accuracy'.
For others please add appropriate suffixes: '_macro', '_micro', or '_weighted', e.g., 'f1_macro'""")
elif ml_category == "regression" and metric not in ['r2']:
raise ValueError("Regression metric not recognized. Please choose 'r2'")
for feat in features:
if method == "remove":
pred_features = [col for col in self.X.columns if feat != col] # All but feat
elif method == "add":
pred_features = [feat] + stored_features # Feat with stored features
X = self.X[pred_features].copy()
if self.groups is not None:
cv = GroupKFold(n_splits=5)
else:
cv = StratifiedKFold(n_splits=5, shuffle=True)
# See link about scoring for multiclassfication
# http://iamirmasoud.com/2022/06/19/understanding-micro-macro-and-weighted-averages-for-scikit-learn-metrics-in-multi-class-classification-with-example/
if ml_category == "classification":
nb = GaussianNB()
model_cv = cross_validate(
nb,
X=X,
y=self.y,
cv=cv,
groups=self.groups,
n_jobs=-1,
scoring=(metric)
)
elif ml_category == "regression":
lass = Lasso()
model_cv = cross_validate(
lass,
X=X,
y=y,
cv=cv,
groups=self.groups,
n_jobs=-1,
scoring=('r2')
)
else:
raise ValueError("ML type not yet implemented!")
# Section of metrics' scores comparison.
with warnings.catch_warnings():
warnings.filterwarnings("ignore", message="Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.")
metric_score = np.nanmean(model_cv["test_score"])
metric_score_std = np.nanstd(model_cv["test_score"])
if not best_feature or (metric_score > best_metric_score):
best_feature = feat
best_metric_score = metric_score
best_metric_score_std = metric_score_std
return best_feature, best_metric_score, best_metric_score_std
def select_features(self, n_min=20, n_max=50, k=100, method="remove", ml_type="classification_bin", metric="recall", n_tolerance=10):
"""This method selects a set of features and returns them as a list. It returns number of features
determined in the interval of [n_min, n_max].
The method consists of two steps:
(1) The method uses sklearn kBest method which selects k best features dependent on the ml_type parameter.
(2) The sequential features removal procedure is executed. Using the remaing features from (1).
The best score is detected using a removal procedure. The procedure sequentially removes the features
that attribute the least to the choosen evaluation metric. If in this sequence the score ML score is
improved the next feature is remove otherwise there is a tolerance criteria (n_tolerance)
with which the next n removed features are inspected whether currently best score is improved.
Args:
n_min (int, optional): Minimal amount of features returned.
n_max (int, optional): Maximal amount of features returned.
k (int, optional): Determines the k in the k-best features method.
If None, SelectKBest feature selection does not execute.
ml_type(str, optional): Type of ML problem. Currently implemented options:
'classification_bin', 'classification_multi', and 'regression_'
method (str, optional): "remove" or "add" features. Defaults to "remove".
n_tolerance (int, optional): If the best score is not improved in n that is specified by this parameter
the method returns index of feature with current best score as a tipping point feature.
Returns:
list: list of selected features
"""
if k is not None and k <= n_max:
raise ValueError("The k parameter needs to be greater than the n_max parameter.")
# Select k-best feature dependent on the type of ML task
ml_category, ml_subcategory = ml_type.split("_")
if k is not None:
if ml_category == "classification":
if ml_subcategory== "bin":
selector = SelectKBest(mutual_info_classif, k=k)
elif ml_subcategory== "multi":
selector = SelectKBest(f_classif, k=k)
else:
raise ValueError("Unknown ML type: cannot recognize ML classification subtype.")
elif ml_category == "regression":
selector = SelectKBest(f_regression, k=k)
else:
raise ValueError("Unknown ML type: cannot recognize ML type. Must be either classification or regression.")
selector.fit(self.X, self.y)
cols_idxs = selector.get_support(indices=True)
self.X = self.X.iloc[:,cols_idxs]
print("All columns (after SelectKBest method):")
print(self.X.columns)
# Sequential feature addition / removal
n_features = self.X.shape[1]
if n_max >= n_features:
n_max = n_features-1 # The algorithm removes at least one feature
if n_min > n_features:
raise ValueError("The number of remaining features in the dataframe must be at least as n_min+1 parameter.")
if n_max < n_min:
raise ValueError("n_max parameter needs to be greater than or equal to n_min parameter.")
features = self.X.columns.tolist()
feature_importance = []
if method == "remove":
best_score = 0
best_feature_indx = None
i_worse = 0
for i in reversed(range(n_features)):
if i+1 == n_min:
break
best_feature, best_metric_score, best_metric_score_std = \
self.select_best_feature(features, method=method, ml_category=ml_category, ml_subcategory=ml_subcategory, metric=metric)
feature_importance.append((i+1, best_feature, best_metric_score, best_metric_score_std))
features.remove(best_feature)
print("Features left:", i)
if i <= n_max:
if best_metric_score >= best_score:
best_score = best_metric_score
best_feature_indx = i+1
i_worse = 0
else:
i_worse += 1
if i_worse == n_tolerance:
break
feature_importance_df = pd.DataFrame(feature_importance, columns=['i', 'name', 'metric', 'metric_sd'])
print(feature_importance_df)
print("best_feature_indx", best_feature_indx)
print("best_score", best_score)
features_to_remove = feature_importance_df[feature_importance_df["i"] >= best_feature_indx]["name"].values.tolist()
selected_features = [feat for feat in self.X.columns.tolist() if feat not in features_to_remove]
return selected_features
else:
raise ValueError("Method type not recognized: only the 'remove' method is currently implemented.")

231
machine_learning/features_sensor.py
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@ -1,231 +0,0 @@
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()

730
machine_learning/helper.py
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@ -1,730 +0,0 @@
from pathlib import Path
import numpy as np
import pandas as pd
from sklearn import (
ensemble,
gaussian_process,
kernel_ridge,
linear_model,
naive_bayes,
svm,
)
from sklearn.dummy import DummyClassifier, DummyRegressor
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import (
BaseCrossValidator,
LeaveOneGroupOut,
StratifiedKFold,
cross_validate,
)
from xgboost import XGBClassifier, XGBRegressor
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
def insert_row(df, row):
return pd.concat([df, pd.DataFrame([row], columns=df.columns)], ignore_index=True)
def impute_encode_categorical_features(model_input: pd.DataFrame) -> pd.DataFrame:
categorical_feature_col_names = [
"gender",
"startlanguage",
"limesurvey_demand_control_ratio_quartile",
]
additional_categorical_features = [
col
for col in model_input.columns
if "mostcommonactivity" in col or "homelabel" in col
]
categorical_feature_col_names += additional_categorical_features
categorical_features = model_input[categorical_feature_col_names].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 = model_input.drop(categorical_feature_col_names, axis=1)
model_input = pd.concat([numerical_features, categorical_features], axis=1)
return model_input
def prepare_sklearn_data_format(
model_input: pd.DataFrame, cv_method: str = "logo"
) -> tuple:
index_columns = [
"local_segment",
"local_segment_label",
"local_segment_start_datetime",
"local_segment_end_datetime",
]
model_input.set_index(index_columns, inplace=True)
if cv_method == "half_logo":
model_input["pid_index"] = model_input.groupby("pid").cumcount()
model_input["pid_count"] = model_input.groupby("pid")["pid"].transform("count")
model_input["pid_index"] = (
model_input["pid_index"] / model_input["pid_count"] + 1
).round()
model_input["pid_half"] = (
model_input["pid"] + "_" + model_input["pid_index"].astype(int).astype(str)
)
data_x, data_y, data_groups = (
model_input.drop(["target", "pid", "pid_index", "pid_half"], axis=1),
model_input["target"],
model_input["pid_half"],
)
else:
data_x, data_y, data_groups = (
model_input.drop(["target", "pid"], axis=1),
model_input["target"],
model_input["pid"],
)
return data_x, data_y, data_groups
def prepare_cross_validator(
data_x: pd.DataFrame,
data_y: pd.DataFrame,
data_groups: pd.DataFrame,
cv_method: str = "logo",
) -> BaseCrossValidator:
if cv_method == "logo" or cv_method == "half_logo":
cv = LeaveOneGroupOut()
cv.get_n_splits(
data_x,
data_y,
groups=data_groups,
)
else:
cv = StratifiedKFold(n_splits=5, shuffle=True)
return cv
def aggregate_and_transpose(df: pd.DataFrame, statistics=None) -> pd.DataFrame:
if statistics is None:
statistics = ["max", "mean"]
return (
df.agg(statistics)
.transpose()
.reset_index()
.rename(columns={"index": "test_metric"})
)
def run_all_regression_models(
data_x: pd.DataFrame,
data_y: pd.DataFrame,
data_groups: pd.DataFrame,
cross_validator: BaseCrossValidator,
) -> pd.DataFrame:
metrics = ["r2", "neg_mean_absolute_error", "neg_root_mean_squared_error"]
test_metrics = ["test_" + metric for metric in metrics]
scores = pd.DataFrame(columns=["method", "test_metric", "max", "nanmedian"])
# Validate models
dummy_regr = DummyRegressor(strategy="mean")
dummy_regr_scores = cross_validate(
dummy_regr,
X=data_x,
y=data_y,
groups=data_groups,
cv=cross_validator,
n_jobs=-1,
scoring=metrics,
)
print("Dummy model:")
print("R^2: ", np.nanmedian(dummy_regr_scores["test_r2"]))
scores_df = pd.DataFrame(dummy_regr_scores)[test_metrics]
scores_df = aggregate_and_transpose(scores_df, statistics=["max", np.nanmedian])
scores_df["method"] = "dummy"
scores = pd.concat([scores, scores_df])
del dummy_regr
del dummy_regr_scores
lin_reg = linear_model.LinearRegression()
lin_reg_scores = cross_validate(
lin_reg,
X=data_x,
y=data_y,
groups=data_groups,
cv=cross_validator,
n_jobs=-1,
scoring=metrics,
)
print("Linear regression:")
print("R^2: ", np.nanmedian(lin_reg_scores["test_r2"]))
scores_df = pd.DataFrame(lin_reg_scores)[test_metrics]
scores_df = aggregate_and_transpose(scores_df, statistics=["max", np.nanmedian])
scores_df["method"] = "linear_reg"
scores = pd.concat([scores, scores_df])
del lin_reg
del lin_reg_scores
ridge_reg = linear_model.Ridge(alpha=0.5)
ridge_reg_scores = cross_validate(
ridge_reg,
X=data_x,
y=data_y,
groups=data_groups,
cv=cross_validator,
n_jobs=-1,
scoring=metrics,
)
print("Ridge regression")
scores_df = pd.DataFrame(ridge_reg_scores)[test_metrics]
scores_df = aggregate_and_transpose(scores_df, statistics=["max", np.nanmedian])
scores_df["method"] = "ridge_reg"
scores = pd.concat([scores, scores_df])
del ridge_reg
del ridge_reg_scores
lasso_reg = linear_model.Lasso(alpha=0.1)
lasso_reg_score = cross_validate(
lasso_reg,
X=data_x,
y=data_y,
groups=data_groups,
cv=cross_validator,
n_jobs=-1,
scoring=metrics,
)
print("Lasso regression")
scores_df = pd.DataFrame(lasso_reg_score)[test_metrics]
scores_df = aggregate_and_transpose(scores_df, statistics=["max", np.nanmedian])
scores_df["method"] = "lasso_reg"
scores = pd.concat([scores, scores_df])
del lasso_reg
del lasso_reg_score
bayesian_ridge_reg = linear_model.BayesianRidge()
bayesian_ridge_reg_score = cross_validate(
bayesian_ridge_reg,
X=data_x,
y=data_y,
groups=data_groups,
cv=cross_validator,
n_jobs=-1,
scoring=metrics,
)
print("Bayesian Ridge")
scores_df = pd.DataFrame(bayesian_ridge_reg_score)[test_metrics]
scores_df = aggregate_and_transpose(scores_df, statistics=["max", np.nanmedian])
scores_df["method"] = "bayesian_ridge"
scores = pd.concat([scores, scores_df])
del bayesian_ridge_reg
del bayesian_ridge_reg_score
ransac_reg = linear_model.RANSACRegressor()
ransac_reg_score = cross_validate(
ransac_reg,
X=data_x,
y=data_y,
groups=data_groups,
cv=cross_validator,
n_jobs=-1,
scoring=metrics,
)
print("RANSAC (outlier robust regression)")
scores_df = pd.DataFrame(ransac_reg_score)[test_metrics]
scores_df = aggregate_and_transpose(scores_df, statistics=["max", np.nanmedian])
scores_df["method"] = "RANSAC"
scores = pd.concat([scores, scores_df])
del ransac_reg
del ransac_reg_score
svr = svm.SVR()
svr_score = cross_validate(
svr,
X=data_x,
y=data_y,
groups=data_groups,
cv=cross_validator,
n_jobs=-1,
scoring=metrics,
)
print("Support vector regression")
scores_df = pd.DataFrame(svr_score)[test_metrics]
scores_df = aggregate_and_transpose(scores_df, statistics=["max", np.nanmedian])
scores_df["method"] = "SVR"
scores = pd.concat([scores, scores_df])
del svr
del svr_score
kridge = kernel_ridge.KernelRidge()
kridge_score = cross_validate(
kridge,
X=data_x,
y=data_y,
groups=data_groups,
cv=cross_validator,
n_jobs=-1,
scoring=metrics,
)
print("Kernel Ridge regression")
scores_df = pd.DataFrame(kridge_score)[test_metrics]
scores_df = aggregate_and_transpose(scores_df, statistics=["max", np.nanmedian])
scores_df["method"] = "kernel_ridge"
scores = pd.concat([scores, scores_df])
del kridge
del kridge_score
gpr = gaussian_process.GaussianProcessRegressor()
gpr_score = cross_validate(
gpr,
X=data_x,
y=data_y,
groups=data_groups,
cv=cross_validator,
n_jobs=-1,
scoring=metrics,
)
print("Gaussian Process Regression")
scores_df = pd.DataFrame(gpr_score)[test_metrics]
scores_df = aggregate_and_transpose(scores_df, statistics=["max", np.nanmedian])
scores_df["method"] = "gaussian_proc"
scores = pd.concat([scores, scores_df])
del gpr
del gpr_score
rfr = ensemble.RandomForestRegressor(max_features=0.3, n_jobs=-1)
rfr_score = cross_validate(
rfr,
X=data_x,
y=data_y,
groups=data_groups,
cv=cross_validator,
n_jobs=-1,
scoring=metrics,
)
print("Random Forest Regression")
scores_df = pd.DataFrame(rfr_score)[test_metrics]
scores_df = aggregate_and_transpose(scores_df, statistics=["max", np.nanmedian])
scores_df["method"] = "random_forest"
scores = pd.concat([scores, scores_df])
del rfr
del rfr_score
xgb = XGBRegressor()
xgb_score = cross_validate(
xgb,
X=data_x,
y=data_y,
groups=data_groups,
cv=cross_validator,
n_jobs=-1,
scoring=metrics,
)
print("XGBoost Regressor")
scores_df = pd.DataFrame(xgb_score)[test_metrics]
scores_df = aggregate_and_transpose(scores_df, statistics=["max", np.nanmedian])
scores_df["method"] = "XGBoost"
scores = pd.concat([scores, scores_df])
del xgb
del xgb_score
ada = ensemble.AdaBoostRegressor()
ada_score = cross_validate(
ada,
X=data_x,
y=data_y,
groups=data_groups,
cv=cross_validator,
n_jobs=-1,
scoring=metrics,
)
print("ADA Boost Regressor")
scores_df = pd.DataFrame(ada_score)[test_metrics]
scores_df = aggregate_and_transpose(scores_df, statistics=["max", np.nanmedian])
scores_df["method"] = "ADA_boost"
scores = pd.concat([scores, scores_df])
del ada
del ada_score
return scores
def confusion_matrix_scorer(clf, X, y):
y_pred = clf.predict(X)
cm = confusion_matrix(y, y_pred)
return {"tn": cm[0, 0], "fp": cm[0, 1], "fn": cm[1, 0], "tp": cm[1, 1]}
def aggregate_confusion_matrix(scores_dict: dict) -> pd.DataFrame:
scores_aggregated = aggregate_and_transpose(
pd.DataFrame(scores_dict), statistics=["sum"]
)
return scores_aggregated[
~scores_aggregated.test_metric.isin(["fit_time", "score_time"])
]
def run_all_classification_models(
data_x: pd.DataFrame,
data_y: pd.DataFrame,
data_groups: pd.DataFrame,
cross_validator: BaseCrossValidator,
):
data_y_value_counts = data_y.value_counts()
if len(data_y_value_counts) == 1:
raise (ValueError("There is only one unique value in data_y."))
if len(data_y_value_counts) == 2:
metrics = ["accuracy", "average_precision", "recall", "f1"]
else:
metrics = ["accuracy", "precision_micro", "recall_micro", "f1_micro"]
test_metrics = ["test_" + metric for metric in metrics]
scores = pd.DataFrame(columns=["method", "test_metric", "max", "mean"])
dummy_class = DummyClassifier(strategy="most_frequent")
dummy_score = cross_validate(
dummy_class,
X=data_x,
y=data_y,
groups=data_groups,
cv=cross_validator,
n_jobs=-1,
error_score="raise",
scoring=metrics,
)
dummy_confusion_matrix = cross_validate(
dummy_class,
X=data_x,
y=data_y,
groups=data_groups,
cv=cross_validator,
n_jobs=-1,
error_score="raise",
scoring=confusion_matrix_scorer,
)
print("Dummy")
scores_df = pd.DataFrame(dummy_score)[test_metrics]
scores_df = aggregate_and_transpose(scores_df, statistics=["max", "mean"])
scores_df = pd.concat(
[
scores_df,
aggregate_confusion_matrix(dummy_confusion_matrix).rename(
columns={"sum": "mean"}
# Note: the column is misleadingly renamed to get concise output.
),
]
)
scores_df["method"] = "dummy_classifier"
scores = pd.concat([scores, scores_df])
del dummy_class
del dummy_score
del dummy_confusion_matrix
logistic_regression = linear_model.LogisticRegression()
log_reg_scores = cross_validate(
logistic_regression,
X=data_x,
y=data_y,
groups=data_groups,
cv=cross_validator,
n_jobs=-1,
scoring=metrics,
)
log_reg_confusion_matrix = cross_validate(
logistic_regression,
X=data_x,
y=data_y,
groups=data_groups,
cv=cross_validator,
n_jobs=-1,
scoring=confusion_matrix_scorer,
)
print("Logistic regression")
scores_df = pd.DataFrame(log_reg_scores)[test_metrics]
scores_df = aggregate_and_transpose(scores_df, statistics=["max", "mean"])
scores_df = pd.concat(
[
scores_df,
aggregate_confusion_matrix(log_reg_confusion_matrix).rename(
columns={"sum": "mean"}
# Note: the column is misleadingly renamed to get concise output.
),
]
)
scores_df["method"] = "logistic_regression"
scores = pd.concat([scores, scores_df])
del logistic_regression
del log_reg_scores
del log_reg_confusion_matrix
svc = svm.SVC()
svc_scores = cross_validate(
svc,
X=data_x,
y=data_y,
groups=data_groups,
cv=cross_validator,
n_jobs=-1,
scoring=metrics,
)
svc_confusion_matrix = cross_validate(
svc,
X=data_x,
y=data_y,
groups=data_groups,
cv=cross_validator,
n_jobs=-1,
scoring=confusion_matrix_scorer,
)
print("Support Vector Machine")
scores_df = pd.DataFrame(svc_scores)[test_metrics]
scores_df = aggregate_and_transpose(scores_df, statistics=["max", "mean"])
scores_df = pd.concat(
[
scores_df,
aggregate_confusion_matrix(svc_confusion_matrix).rename(
columns={"sum": "mean"}
# Note: the column is misleadingly renamed to get concise output.
),
]
)
scores_df["method"] = "SVC"
scores = pd.concat([scores, scores_df])
del svc
del svc_scores
del svc_confusion_matrix
gaussian_nb = naive_bayes.GaussianNB()
gaussian_nb_scores = cross_validate(
gaussian_nb,
X=data_x,
y=data_y,
groups=data_groups,
cv=cross_validator,
n_jobs=-1,
scoring=metrics,
)
gaussian_nb_confusion_matrix = cross_validate(
gaussian_nb,
X=data_x,
y=data_y,
groups=data_groups,
cv=cross_validator,
n_jobs=-1,
scoring=confusion_matrix_scorer,
)
print("Gaussian Naive Bayes")
scores_df = pd.DataFrame(gaussian_nb_scores)[test_metrics]
scores_df = aggregate_and_transpose(scores_df, statistics=["max", "mean"])
scores_df = pd.concat(
[
scores_df,
aggregate_confusion_matrix(gaussian_nb_confusion_matrix).rename(
columns={"sum": "mean"}
# Note: the column is misleadingly renamed to get concise output.
),
]
)
scores_df["method"] = "gaussian_naive_bayes"
scores = pd.concat([scores, scores_df])
del gaussian_nb
del gaussian_nb_scores
del gaussian_nb_confusion_matrix
sgdc = linear_model.SGDClassifier()
sgdc_scores = cross_validate(
sgdc,
X=data_x,
y=data_y,
groups=data_groups,
cv=cross_validator,
n_jobs=-1,
scoring=metrics,
)
sgdc_confusion_matrix = cross_validate(
sgdc,
X=data_x,
y=data_y,
groups=data_groups,
cv=cross_validator,
n_jobs=-1,
scoring=confusion_matrix_scorer,
)
print("Stochastic Gradient Descent")
scores_df = pd.DataFrame(sgdc_scores)[test_metrics]
scores_df = aggregate_and_transpose(scores_df, statistics=["max", "mean"])
scores_df = pd.concat(
[
scores_df,
aggregate_confusion_matrix(sgdc_confusion_matrix).rename(
columns={"sum": "mean"}
# Note: the column is misleadingly renamed to get concise output.
),
]
)
scores_df["method"] = "stochastic_gradient_descent_classifier"
scores = pd.concat([scores, scores_df])
del sgdc
del sgdc_scores
del sgdc_confusion_matrix
rfc = ensemble.RandomForestClassifier()
rfc_scores = cross_validate(
rfc,
X=data_x,
y=data_y,
groups=data_groups,
cv=cross_validator,
n_jobs=-1,
scoring=metrics,
)
rfc_confusion_matrix = cross_validate(
rfc,
X=data_x,
y=data_y,
groups=data_groups,
cv=cross_validator,
n_jobs=-1,
scoring=confusion_matrix_scorer,
)
print("Random Forest")
scores_df = pd.DataFrame(rfc_scores)[test_metrics]
scores_df = aggregate_and_transpose(scores_df, statistics=["max", "mean"])
scores_df = pd.concat(
[
scores_df,
aggregate_confusion_matrix(rfc_confusion_matrix).rename(
columns={"sum": "mean"}
# Note: the column is misleadingly renamed to get concise output.
),
]
)
scores_df["method"] = "random_forest_classifier"
scores = pd.concat([scores, scores_df])
del rfc
del rfc_scores
del rfc_confusion_matrix
xgb_classifier = XGBClassifier()
xgb_scores = cross_validate(
xgb_classifier,
X=data_x,
y=data_y,
groups=data_groups,
cv=cross_validator,
n_jobs=-1,
scoring=metrics,
)
xgb_confusion_matrix = cross_validate(
xgb_classifier,
X=data_x,
y=data_y,
groups=data_groups,
cv=cross_validator,
n_jobs=-1,
scoring=confusion_matrix_scorer,
)
print("XGBoost")
scores_df = pd.DataFrame(xgb_scores)[test_metrics]
scores_df = aggregate_and_transpose(scores_df, statistics=["max", "mean"])
scores_df = pd.concat(
[
scores_df,
aggregate_confusion_matrix(xgb_confusion_matrix).rename(
columns={"sum": "mean"}
# Note: the column is misleadingly renamed to get concise output.
),
]
)
scores_df["method"] = "XGBoost_classifier"
scores = pd.concat([scores, scores_df])
del xgb_classifier
del xgb_scores
del xgb_confusion_matrix
return scores

135
machine_learning/labels.py
View File

@ -1,135 +0,0 @@
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()

47
machine_learning/model.py
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@ -1,47 +0,0 @@
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",
)

147
machine_learning/pipeline.py
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@ -1,32 +1,125 @@
import numpy as np
import yaml
from sklearn import linear_model
import datetime
from machine_learning.features_sensor import SensorFeatures
from machine_learning.labels import Labels
from machine_learning.model import ModelValidation
import pandas as pd
from sklearn.model_selection import cross_val_score
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()
import participants.query_db
from features import esm, helper, proximity
from machine_learning import QUESTIONNAIRE_IDS, QUESTIONNAIRE_IDS_RENAME
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",
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.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))

133
machine_learning/preprocessing.py
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@ -1,133 +0,0 @@
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
class Preprocessing:
"""This class presents Preprocessing methods which can be used in context of an individual CV iteration or, simply, on whole data.
It's blind to the test data - e.g, it imputes the test data with train data mean.
This means, it somehow needs an access to the information about data split. In context
"""
def __init__(self, train_X, train_y, test_X, test_y):
self.train_X = train_X
self.train_y = train_y
self.test_X = test_X
self.test_y = test_y
def one_hot_encoder(self, categorical_features, numerical_features, mode):
"""
This code is an implementation of one-hot encoding. It takes in two data sets,
one with categorical features and one with numerical features and a mode parameter.
First it uses the fillna() function to fill in any missing values present in the
categorical data set with the mode value. Then it uses the apply () method to
convert each column of the data set into a category data type which is then
transformed using the pd.get_dummies() function. Finally it concatenates the
numerical data set and the transformed categorical data set using pd.concat() and
returns it.
Args:
categorical_features (DataFrame): DataFrame including only categorical columns.
numerical_features (_type_): DataFrame including only numerical columns.
mode (int): Mode of the column with which DataFrame is filled.
Returns:
DataFrame: Hot-One Encoded DataFrame.
"""
# Fill train set with mode
categorical_features = categorical_features.fillna(mode)
# 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)
return pd.concat([numerical_features, categorical_features], axis=1), categorical_features.columns.tolist()
def one_hot_encode_train_and_test_sets(self, categorical_columns=["gender", "startlanguage", "mostcommonactivity", "homelabel"]):
"""
This code is used to transform categorical data into numerical representations.
It first identifies the categorical columns, then copies them and saves them as
a new dataset. The missing data is filled with the mode (most frequent value in
the respective column). This new dataset is then subjected to one-hot encoding,
which is a process of transforming categorical data into machine interpretable
numerical form by converting categories into multiple binary outcome variables.
These encoded values are then concatenated to the numerical features prior to
being returned as the final dataset.
Args:
categorical_columns (list, optional): List of categorical columns in the dataset.
Defaults to ["gender", "startlanguage", "mostcommonactivity", "homelabel"].
"""
categorical_columns = [col for col in self.train_X.columns if col in categorical_columns]
# For train set
train_X_categorical_features = self.train_X[categorical_columns].copy()
train_X_numerical_features = self.train_X.drop(categorical_columns, axis=1)
mode_train_X_categorical_features = train_X_categorical_features.mode().iloc[0]
self.train_X, train_cat_col_names = self.one_hot_encoder(train_X_categorical_features, train_X_numerical_features, mode_train_X_categorical_features)
encoded_categorical_features = [col for col in self.train_X.columns if col.startswith(tuple(categorical_columns))]
# For test set
test_X_categorical_features = self.test_X[categorical_columns].copy()
test_X_numerical_features = self.test_X.drop(categorical_columns, axis=1)
self.test_X, test_cat_col_names = self.one_hot_encoder(test_X_categorical_features, test_X_numerical_features, mode_train_X_categorical_features)
# Create categorical columns that were not found in test set and fill them with 0
missing_cols = [col for col in train_cat_col_names if col not in test_cat_col_names]
self.test_X[missing_cols] = 0
# Sort column names alphabetically
self.train_X = self.train_X.reindex(sorted(self.train_X.columns), axis=1)
self.test_X = self.test_X.reindex(sorted(self.test_X.columns), axis=1)
def imputer(self, interval_feature_list, other_feature_list, groupby_feature="pid"):
# TODO: TESTING
if groupby:
# Interval numerical features # TODO: How can we get and assign appropriate groupby means and assign them to correct columns?
# VVVVV ...... IN PROGRES ...... VVVVV
means = self.train_X[interval_feature_list].groupby(groupby_feature).mean()
self.train_X[self.train_X.loc[:, ~self.train_X.columns.isin([groupby_feature] + other_feature_list)]] = \
self.train_X[interval_feature_list].groupby(groupby_feature).apply(lambda x: x.fillna(x.mean()))
self.test_X[self.test_X.loc[:, ~self.test_X.columns.isin([groupby_feature] + other_feature_list)]] = \
self.test_X[interval_feature_list].groupby(groupby_feature).apply(lambda x: x.fillna(x.mean()))
# Other features
self.train_X[self.train_X.loc[:, ~self.train_X.columns.isin([groupby_feature] + interval_feature_list)]] = \
self.train_X[other_feature_list].groupby(groupby_feature).apply(lambda x: x.fillna(x.median()))
else:
# Interval numerical features
means = self.train_X[interval_feature_list].mean()
self.train_X[interval_feature_list].fillna(means, inplace=True)
self.test_X[interval_feature_list].fillna(means, inplace=True)
# Other features
medians = self.train_X[other_feature_list].median()
self.train_X[other_feature_list].fillna(medians, inplace=True)
self.test_X[other_feature_list].fillna(medians, inplace=True)
def get_train_test_sets(self):
"""Train and test sets getter
Returns:
tuple of Pandas DataFrames: Gets train test sets in traditional sklearn format.
"""
return self.train_X, self.train_y, self.test_X, self.test_y

69
participants/prepare_usernames_file.py
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@ -1,69 +0,0 @@
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",
)

147
presentation/ApplicationCategories.R
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@ -1,147 +0,0 @@
library(conflicted)
library(yaml)
library(RPostgreSQL)
library(tidyverse)
conflicts_prefer(
dplyr::filter,
dplyr::lag
)
library(magrittr)
# read the password from file
credentials <- yaml.load_file("../rapids/credentials.yaml")
pw <- credentials$PSQL_STRAW$password
# load the PostgreSQL driver
drv <- RPostgres::Postgres()
# creates a connection to the postgres database
# note that "con" will be used later in each connection to the database
con <- RPostgres::dbConnect(drv,
dbname = "staw",
host = "eol.ijs.si", port = 5432,
user = "staw_db", password = pw
)
rm(pw, credentials) # removes the password
# check for the bluetooth table, an example
dbExistsTable(con, "app_categories")
df_app_categories <- tbl(con, "app_categories") %>%
collect()
head(df_app_categories)
table(df_app_categories$play_store_genre)
df_app_categories %>%
filter(play_store_genre == "not_found") %>%
group_by(play_store_response) %>%
count()
# All "not_found" have an HTTP status of 404.
df_app_categories %>%
filter(play_store_genre == "not_found") %>%
group_by(package_name) %>%
count() %>%
arrange(desc(n))
# All "not_found" apps are unique.
# Exclude phone manufacturers, custom ROM names and similar.
manufacturers <- c(
"samsung",
"oneplus",
"huawei",
"xiaomi",
"lge",
"motorola",
"miui",
"lenovo",
"oppo",
"mediatek"
)
custom_rom <- c("coloros", "lineageos", "myos", "cyanogenmod", "foundation.e")
other <- c("android", "wssyncmldm")
grep_pattern <- paste(c(manufacturers, custom_rom, other), collapse = "|")
rows_os_manufacturer <- grepl(grep_pattern, df_app_categories$package_name)
# Explore what remains after excluding above.
df_app_categories[!rows_os_manufacturer, ] %>%
filter(play_store_genre == "not_found")
# Also check the relationship between is_system_app and System category.
tbl(con, "applications") %>%
filter(is_system_app, play_store_genre != "System") %>%
count()
# They are perfectly correlated.
# Manually classify apps
df_app_categories[df_app_categories$play_store_genre == "not_found",] <-
df_app_categories %>%
filter(play_store_genre == "not_found") %>%
mutate(
play_store_genre =
case_when(
str_detect(str_to_lower(package_name), grep_pattern) ~ "System",
str_detect(str_to_lower(package_name), "straw") ~ "STRAW",
str_detect(str_to_lower(package_name), "chromium") ~ "Communication", # Same as chrome.
str_detect(str_to_lower(package_name), "skype") ~ "Communication", # Skype Lite not classified.
str_detect(str_to_lower(package_name), "imsservice") ~ "Communication", # IP Multimedia Subsystem
str_detect(str_to_lower(package_name), paste(c("covid", "empatica"), collapse = "|")) ~ "Medical",
str_detect(str_to_lower(package_name), paste(c("libri", "tachiyomi"), collapse = "|")) ~ "Books & Reference",
str_detect(str_to_lower(package_name), paste(c("bricks", "chess"), collapse = "|")) ~ "Casual",
str_detect(str_to_lower(package_name), "weather") ~ "Weather",
str_detect(str_to_lower(package_name), "excel") ~ "Productivity",
str_detect(str_to_lower(package_name), paste(c("qr", "barcode", "archimedes", "mixplorer", "winrar", "filemanager", "shot", "faceunlock", "signin", "milink"), collapse = "|")) ~ "Tools",
str_detect(str_to_lower(package_name), "stupeflix") ~ "Photography",
str_detect(str_to_lower(package_name), "anyme") ~ "Entertainment",
str_detect(str_to_lower(package_name), "vanced") ~ "Video Players & Editors",
str_detect(str_to_lower(package_name), paste(c("music", "radio", "dolby"), collapse = "|")) ~ "Music & Audio",
str_detect(str_to_lower(package_name), paste(c("tensorflow", "object_detection"), collapse = "|")) ~ "Education",
.default = play_store_genre
)
)
# Explore what remains after classifying above.
df_app_categories %>%
filter(play_store_genre == "not_found")
# After this, 13 applications remain, which I will classify as "Other".
# Correct some mistakes
# And classify 'not_found'
df_app_categories %<>%
mutate(
play_store_genre = {
function(x) {
case_when(
x == "Education,Education" ~ "Education",
x == "EducationEducation" ~ "Education",
x == "not_found" ~ "Other",
.default = x
)
}
}(play_store_genre)
) %>%
select(-package_name) %>%
rename(
genre = play_store_genre,
package_name = package_hash
)
table(df_app_categories$genre)
df_app_categories %>%
group_by(genre) %>%
count() %>%
arrange(desc(n)) %>%
write_csv("play_store_categories_count.csv")
write_csv(
x = select(df_app_categories, c(package_name, genre)),
file = "play_store_application_genre_catalogue.csv"
)
dbDisconnect(con)

103
presentation/StressfulEvents.Rmd
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@ -1,103 +0,0 @@
---
title: "Stressful event detection"
output: html_notebook
---
```{r chunk_options, include=FALSE}
knitr::opts_chunk$set(
comment = "#>", echo = FALSE, fig.width = 6
)
```
```{r libraries, include=FALSE}
library(knitr)
library(kableExtra)
library(stringr)
library(RColorBrewer)
library(magrittr)
library(tidyverse)
```
```{r fig_setup, include=FALSE}
accent <- RColorBrewer::brewer.pal(7, "Accent")
```
```{r read_data, include=FALSE}
podatki <- read_csv("E:/STRAWresults/stressfulness_event_with_target_0_ver2/input_appraisal_stressfulness_event_mean.csv")
podatki %<>% mutate(pid = as_factor(pid))
```
# Event descriptions
Participants were asked "Was there a particular event that created tension in you?" with the following options:
- 0 - No
- 1 - Yes, slightly
- 2 - Yes, moderately
- 3 - Yes, considerably
- 4 - Yes, extremely
If they answered anything but "No", they were also asked about the event's perceived threat (e.g. "Did this event make you feel anxious?") and challenge (e.g. "How eager are you to tackle this event?").
We only consider general "stressfulness" in this presentation.
Most of the time, nothing stressful happened:
```{r target_table}
kable(table(podatki$target), col.names = c("stressfulness", "frequency")) %>%
kable_styling(full_width = FALSE)
```
Most participants had somewhere between 0 and 10 stressful events.
```{r target_distribution}
podatki %>%
group_by(pid) %>%
summarise(no_of_events = sum(target > 0)) %>%
ggplot(aes(no_of_events)) +
geom_histogram(binwidth = 1, fill = accent[1]) +
coord_cartesian(expand = FALSE) +
labs(x = "Number of events per participant") +
theme_classic()
```
When a stressful event occurred, participants mostly perceived it as slightly to moderately stressful on average.
```{r mean_stressfulness_distribution}
podatki %>%
filter(target > 0) %>%
group_by(pid) %>%
summarise(mean_stressfulness = mean(target)) %>%
ggplot(aes(mean_stressfulness)) +
geom_histogram(binwidth = 0.1, fill = accent[1]) +
coord_cartesian(expand = FALSE) +
labs(x = "Mean stressfulness per participant") +
theme_classic()
```
# Problem description
We are trying to predict whether a stressful event occurred, i.e. stressfulness > 0, or not (stressfulness == 0).
First, set up a leave-one-subject-out validation and use original distribution of the class variable.
For this, the majority classifier has a mean accuracy of 0.85 (and median 0.90), while the F1-score, precision and recall are all 0.
We also have an option to validate the results differently, such as with "half-loso", i.e. leaving half of the subject's data in the training set and only use half for testing, or k-fold cross-validation.
Additionally, we can undersample the majority class to balance the dataset.
# Results
## Leave one subject out, original distribution
```{r event_detection}
scores <- read_csv("event_stressful_detection_loso.csv", col_types = "ffdd")
scores_wide <- scores %>%
select(!max) %>%
pivot_wider(names_from = metric,
names_sep = "_",
values_from = mean) %>%
rename_all(~str_replace(.,"^test_",""))
kable(scores_wide, digits = 2) %>%
column_spec(4, color = 'white', background = 'black') %>%
kable_styling(full_width = TRUE)
```

127
presentation/classification.py
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# ---
# jupyter:
# jupytext:
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# 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
from sklearn import linear_model, svm, naive_bayes, neighbors, tree, ensemble
from sklearn.model_selection import LeaveOneGroupOut, cross_validate
from sklearn.dummy import DummyClassifier
from sklearn.impute import SimpleImputer
import xgboost as xg
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
from pathlib import Path
nb_dir = os.path.split(os.getcwd())[0]
if nb_dir not in sys.path:
sys.path.append(nb_dir)
import machine_learning.labels
import machine_learning.model
from machine_learning.helper import run_all_classification_models
# %% [markdown]
# # RAPIDS models
# %% [markdown]
# ## Set script's parameters
#
# %%
cv_method_str = 'logo' # logo, halflogo, 5kfold # Cross-validation method (could be regarded as a hyperparameter)
n_sl = 1 # Number of largest/smallest accuracies (of particular CV) outputs
# %% jupyter={"source_hidden": true}
filename = Path("E:/STRAWresults/inputData/stressfulness_event/input_appraisal_stressfulness_event_mean.csv")
model_input = pd.read_csv(filename)
# %% jupyter={"source_hidden": true}
index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
model_input.set_index(index_columns, inplace=True)
model_input['target'].value_counts()
# %% jupyter={"source_hidden": true}
bins = [-10, -1, 1, 10] # bins for z-scored targets
# bins = [0, 1, 4] # bins for stressfulness (1-4) target
model_input['target'], edges = pd.cut(model_input.target, bins=bins, labels=['low', 'medium', 'high'], retbins=True, right=True) #['low', 'medium', 'high']
model_input['target'].value_counts(), edges
model_input = model_input[model_input['target'] != "medium"]
model_input['target'] = model_input['target'].astype(str).apply(lambda x: 0 if x == "low" else 1)
model_input['target'].value_counts()
if cv_method_str == 'halflogo':
model_input['pid_index'] = model_input.groupby('pid').cumcount()
model_input['pid_count'] = model_input.groupby('pid')['pid'].transform('count')
model_input["pid_index"] = (model_input['pid_index'] / model_input['pid_count'] + 1).round()
model_input["pid_half"] = model_input["pid"] + "_" + model_input["pid_index"].astype(int).astype(str)
data_x, data_y, data_groups = model_input.drop(["target", "pid", "pid_index", "pid_half"], axis=1), model_input["target"], model_input["pid_half"]
else:
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"]
additional_categorical_features = [col for col in data_x.columns if "mostcommonactivity" in col or "homelabel" in col]
categorical_feature_colnames += additional_categorical_features
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)
# %% jupyter={"source_hidden": true}
cv_method = None # Defaults to 5 k-folds in cross_validate method
if cv_method_str == 'logo' or cv_method_str == 'half_logo':
cv_method = LeaveOneGroupOut()
cv_method.get_n_splits(
train_x,
data_y,
groups=data_groups,
)
# %% jupyter={"source_hidden": true}
imputer = SimpleImputer(missing_values=np.nan, strategy='median')
# %%
final_scores = run_all_classification_models(imputer.fit_transform(train_x), data_y, data_groups, cv_method)
# %%
final_scores.index.name = "metric"
final_scores = final_scores.set_index(["method", final_scores.index])
final_scores.to_csv("event_stressfulness_lmh_lh_scores.csv")

60
presentation/event_stressfulness.py
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# language: python
# name: python3
# ---
# %%
# %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
from sklearn.model_selection import LeaveOneGroupOut, LeavePGroupsOut, cross_val_score, cross_validate
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.impute import SimpleImputer
from sklearn.dummy import DummyRegressor
import xgboost as xg
from pathlib import Path
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
import machine_learning.helper
# %% tags=["active-ipynb"]
# filename = Path("E:/STRAWresults/inputData/stressfulness_event/input_appraisal_stressfulness_event_mean.csv")
# filename = Path('C:/Users/Primoz/VSCodeProjects/straw2analysis/data/stressfulness_event/input_appraisal_stressfulness_event_mean.csv')
# %%
final_scores = machine_learning.helper.run_all_regression_models(filename)
# %%
final_scores.index.name = "metric"
final_scores = final_scores.set_index(["method", final_scores.index])
# %%
final_scores.to_csv("event_stressfulness_scores.csv")

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presentation/play_store_application_genre_catalogue.csv
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presentation/play_store_categories_count.csv
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genre,n
System,261
Tools,96
Productivity,71
Health & Fitness,60
Finance,54
Communication,39
Music & Audio,39
Shopping,38
Lifestyle,33
Education,28
News & Magazines,24
Maps & Navigation,23
Entertainment,21
Business,18
Travel & Local,18
Books & Reference,16
Social,16
Weather,16
Food & Drink,14
Sports,14
Other,13
Photography,13
Puzzle,13
Video Players & Editors,12
Card,9
Casual,9
Personalization,8
Medical,7
Board,5
Strategy,4
House & Home,3
Trivia,3
Word,3
Adventure,2
Art & Design,2
Auto & Vehicles,2
Dating,2
Role Playing,2
STRAW,2
Simulation,2
"Board,Brain Games",1
"Entertainment,Music & Video",1
Parenting,1
Racing,1
1 genre n
2 System 261
3 Tools 96
4 Productivity 71
5 Health & Fitness 60
6 Finance 54
7 Communication 39
8 Music & Audio 39
9 Shopping 38
10 Lifestyle 33
11 Education 28
12 News & Magazines 24
13 Maps & Navigation 23
14 Entertainment 21
15 Business 18
16 Travel & Local 18
17 Books & Reference 16
18 Social 16
19 Weather 16
20 Food & Drink 14
21 Sports 14
22 Other 13
23 Photography 13
24 Puzzle 13
25 Video Players & Editors 12
26 Card 9
27 Casual 9
28 Personalization 8
29 Medical 7
30 Board 5
31 Strategy 4
32 House & Home 3
33 Trivia 3
34 Word 3
35 Adventure 2
36 Art & Design 2
37 Auto & Vehicles 2
38 Dating 2
39 Role Playing 2
40 STRAW 2
41 Simulation 2
42 Board,Brain Games 1
43 Entertainment,Music & Video 1
44 Parenting 1
45 Racing 1

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presentation/presentation.Rproj
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Version: 1.0
RestoreWorkspace: Default
SaveWorkspace: Default
AlwaysSaveHistory: Default
EnableCodeIndexing: Yes
UseSpacesForTab: Yes
NumSpacesForTab: 2
Encoding: UTF-8
RnwWeave: Sweave
LaTeX: pdfLaTeX
AutoAppendNewline: Yes
SpellingDictionary: en_GB

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presentation/prox_comm_PANAS_nb.py
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# ---
# jupyter:
# jupytext:
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# language: python
# name: straw2analysis
# ---
# %%
# %matplotlib inline
import yaml
from sklearn import linear_model
from sklearn.model_selection import LeaveOneGroupOut, cross_val_score
import os
import importlib
import matplotlib.pyplot as plt
import sys
import numpy as np
import seaborn as sns
import pandas as pd
nb_dir = os.path.split(os.getcwd())[0]
if nb_dir not in sys.path:
sys.path.append(nb_dir)
# %%
from machine_learning import pipeline, features_sensor, labels, model
# %%
importlib.reload(labels)
# %%
with open("./config/prox_comm_PANAS_features.yaml", "r") as file:
sensor_features_params = yaml.safe_load(file)
sensor_features = features_sensor.SensorFeatures(**sensor_features_params)
#sensor_features.set_sensor_data()
sensor_features.calculate_features(cached=True)
# %%
all_features = sensor_features.get_features("all","all")
# %%
with open("./config/prox_comm_PANAS_labels.yaml", "r") as file:
labels_params = yaml.safe_load(file)
labels_current = labels.Labels(**labels_params)
#labels_current.set_labels()
labels_current.aggregate_labels(cached=True)
# %%
model_validation = model.ModelValidation(
sensor_features.get_features("all", "all"),
labels_current.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))
# %%
model_loso_r2[model_loso_r2 > 0]
# %%
logo = LeaveOneGroupOut()
# %%
try_X = model_validation.X.reset_index().drop(["participant_id","date_lj"], axis=1)
try_y = model_validation.y.reset_index().drop(["participant_id","date_lj"], axis=1)
# %%
model_loso_mean_absolute_error = -1 * cross_val_score(
estimator=model_validation.model,
X=try_X,
y=try_y,
groups=model_validation.groups,
cv=logo.split(X=try_X, y=try_y, groups=model_validation.groups),
scoring='neg_mean_absolute_error'
)
# %%
model_loso_mean_absolute_error
# %%
np.median(model_loso_mean_absolute_error)
# %%
model_validation.model.fit(try_X, try_y)
# %%
Y_predicted = model_validation.model.predict(try_X)
# %%
try_y.rename(columns={"NA": "NA_true"}, inplace=True)
try_y["NA_predicted"] = Y_predicted
NA_long = pd.wide_to_long(
try_y.reset_index(),
i="index",
j="value",
stubnames="NA",
sep="_",
suffix=".+",
)
# %%
g1 = sns.displot(NA_long, x="NA", hue="value", binwidth=0.1, height=5, aspect=1.5)
sns.move_legend(g1, "upper left", bbox_to_anchor=(.55, .45))
g1.set_axis_labels("Daily mean", "Day count")
display(g1)
g1.savefig("prox_comm_PANAS_predictions.pdf")
# %%
from sklearn.metrics import mean_absolute_error
# %%
mean_absolute_error(try_y["NA_true"], try_y["NA_predicted"])
# %%
model_loso_mean_absolute_error

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presentation/results_presentation.py
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# %%
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, cross_validate, cross_val_predict
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.impute import SimpleImputer
from sklearn.dummy import DummyRegressor
from sklearn.decomposition import PCA
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
nb_dir = os.path.split(os.getcwd())[0]
if nb_dir not in sys.path:
sys.path.append(nb_dir)
import machine_learning.helper
# %%
segment = "intradaily_30_min"
target = "JCQ_job_demand"
csv_name = "./data/" + segment + "_all_targets/input_" + target + "_mean.csv"
#csv_name = "./data/daily_18_hours_all_targets/input_JCQ_job_demand_mean.csv"
# %%
data_x, data_y, data_groups = machine_learning.helper.prepare_model_input(csv_name)
# %%
data_y.head()
# %%
scores = machine_learning.helper.run_all_models(csv_name)
# %% jupyter={"source_hidden": true}
logo = LeaveOneGroupOut()
logo.get_n_splits(
data_x,
data_y,
groups=data_groups,
)
# %% [markdown]
# ### Baseline: Dummy Regression (mean)
dummy_regr = DummyRegressor(strategy="mean")
# %% jupyter={"source_hidden": true}
lin_reg_scores = cross_validate(
dummy_regr,
X=data_x,
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
)
print("Negative Mean Squared Error", np.median(lin_reg_scores['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.median(lin_reg_scores['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.median(lin_reg_scores['test_neg_root_mean_squared_error']))
print("R2", np.median(lin_reg_scores['test_r2']))
##################
# %%
chosen_model = "Random Forest"
rfr = ensemble.RandomForestRegressor(max_features=0.3, n_jobs=-1)
rfr_score = cross_validate(
rfr,
X=data_x,
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
)
print("Negative Mean Squared Error", np.median(rfr_score['test_neg_mean_squared_error']))
print("Negative Mean Absolute Error", np.median(rfr_score['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.median(rfr_score['test_neg_root_mean_squared_error']))
print("R2", np.median(rfr_score['test_r2']))
# %%
y_predicted = cross_val_predict(rfr, data_x, data_y, groups=data_groups, cv=logo)
#########################
# %%
chosen_model = "Bayesian Ridge"
bayesian_ridge_reg = linear_model.BayesianRidge()
bayesian_ridge_reg_score = cross_validate(
bayesian_ridge_reg,
X=data_x,
y=data_y,
groups=data_groups,
cv=logo,
n_jobs=-1,
scoring=('r2', 'neg_mean_squared_error', 'neg_mean_absolute_error', 'neg_root_mean_squared_error')
)
print("Negative Mean Absolute Error", np.median(bayesian_ridge_reg_score['test_neg_mean_absolute_error']))
print("Negative Root Mean Squared Error", np.median(bayesian_ridge_reg_score['test_neg_root_mean_squared_error']))
print("R2", np.median(bayesian_ridge_reg_score['test_r2']))
# %%
y_predicted = cross_val_predict(bayesian_ridge_reg, data_x, data_y, groups=data_groups, cv=logo)
# %%
data_y = pd.DataFrame(pd.concat([data_y, data_groups], axis=1))
data_y.rename(columns={"target": "y_true"}, inplace=True)
data_y["y_predicted"] = y_predicted
# %%
data_y.head()
# %%
g1 = sns.relplot(data=data_y, x="y_true", y="y_predicted")
#g1.set_axis_labels("true", "predicted")
#g1.map(plt.axhline, y=0, color=".7", dashes=(2, 1), zorder=0)
#g1.map(plt.axline, xy1=(0,0), slope=1)
g1.set(title=",".join([segment, target, chosen_model]))
display(g1)
g1.savefig("_".join([segment, target, chosen_model, "_relplot.pdf"]))
# %%
data_y_long = pd.wide_to_long(
data_y.reset_index(),
i=["local_segment", "pid"],
j="value",
stubnames="y",
sep="_",
suffix=".+",
)
# %%
data_y_long.head()
# %%
g2 = sns.displot(data_y_long, x="y", hue="value", binwidth=0.1, height=5, aspect=1.5)
sns.move_legend(g2, "upper left", bbox_to_anchor=(.55, .45))
g2.set(title=",".join([segment, target, chosen_model]))
g2.savefig("_".join([segment, target, chosen_model, "hist.pdf"]))
# %%
pca = PCA(n_components=2)
pca.fit(data_x)
print(pca.explained_variance_ratio_)
# %%
data_x_pca = pca.fit_transform(data_x)
data_pca = pd.DataFrame(pd.concat([data_y.reset_index()["y_true"], pd.DataFrame(data_x_pca, columns = {"pca_0", "pca_1"})], axis=1))
# %%
data_pca
# %%
g3 = sns.relplot(data = data_pca, x = "pca_0", y = "pca_1", hue = "y_true", palette = sns.color_palette("Spectral", as_cmap=True))
g3.set(title=",".join([segment, target, chosen_model]) + "\n variance explained = " + str(round(sum(pca.explained_variance_ratio_), 2)))
g3.savefig("_".join([segment, target, chosen_model, "_PCA.pdf"]))
# %%

7
pyproject.toml
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@ -1,7 +0,0 @@
[tool.isort]
profile = "black"
py_version = 311
skip_gitignore = "true"
[tool.black]
target-version = ["py311"]

1
rapids

@ -1 +0,0 @@
Subproject commit 059774bda10545a83ab282f59eb7a329fef9ee4c

5
setup.py
View File

@ -1,7 +1,8 @@
import os
import sqlalchemy.engine.url
from dotenv import load_dotenv
from sqlalchemy import URL, create_engine
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
load_dotenv()
@ -10,7 +11,7 @@ testing: bool = False
db_password = os.getenv("DB_PASSWORD")
db_uri = URL.create(
db_uri = sqlalchemy.engine.url.URL(
drivername="postgresql+psycopg2",
username="staw_db",
password=db_password,

54
statistical_analysis/adherence.py
View File

@ -6,7 +6,7 @@
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.13.0
# jupytext_version: 1.11.4
# kernelspec:
# display_name: straw2analysis
# language: python
@ -14,7 +14,25 @@
# ---
# %%
SAVE_FIGS = False
# %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
FIG_HEIGHT = 5
FIG_ASPECT = 1.7
FIG_COLOUR = "#28827C"
@ -78,41 +96,13 @@ 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["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
df_session_counts_time
# %%
tbl_session_outcomes = df_session_counts_time.reset_index()[
"session_response_cat"
"session_response"
].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("-------------------------------------")

60
statistical_analysis/scale_reliability.rmd
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@ -1,60 +0,0 @@
---
title: "Reliability of SAM threat and challenge and COPE"
output: html_notebook
---
```{r libraries, message=FALSE, warning=FALSE, include=FALSE, cache=FALSE}
library(conflicted)
library(here)
library(tidyverse)
library(magrittr)
library(lavaan)
library(kableExtra)
conflicts_prefer(
readr::col_factor,
purrr::discard,
dplyr::filter,
dplyr::lag,
purrr::set_names,
tidyr::extract,
kableExtra::group_rows
)
```
```{r style, include=FALSE, cache=FALSE}
styler::style_file(
here("statistical_analysis", "scale_reliability.Rmd"),
scope = "tokens",
indent_by = 4L
)
```
The data were preprocessed and cleaned using [expl_esm_labels.py](../exploration/expl_esm_labels.py) script and read as csv here.
```{r read_data}
COL_TYPES <- cols(
.default = col_double(),
participant_id = col_factor(),
username = col_factor(),
device_id = col_factor(),
esm_trigger = col_factor(),
esm_instructions = col_factor(),
double_esm_user_answer_timestamp = col_double(),
datetime_lj = col_datetime(format = ""),
date_lj = col_date(format = ""),
time = col_factor(),
esm_user_answer = col_factor()
)
df_SAM <- read_csv(here("data", "raw", "df_esm_SAM_threat_challenge.csv"), col_types = COL_TYPES)
df_COPE <- read_csv(here("data", "raw", "df_esm_COPE.csv"), col_types = COL_TYPES)
```
Demonstrate factor analysis for a single participant.
```{r}
df_COPE %>%
group_by(question_id, questionnaire_id) %>%
count()
```

20
straw2analysis.Rproj
View File

@ -1,20 +0,0 @@
Version: 1.0
RestoreWorkspace: Default
SaveWorkspace: Default
AlwaysSaveHistory: Default
EnableCodeIndexing: Yes
UseSpacesForTab: No
NumSpacesForTab: 4
Encoding: UTF-8
RnwWeave: Sweave
LaTeX: XeLaTeX
AutoAppendNewline: Yes
StripTrailingWhitespace: Yes
PythonType: conda
PythonVersion: 3.11.3
PythonPath: E:/ProgramData/mambaforge/envs/straw2analysis/python.exe

3
test/test_communication.py
View File

@ -88,5 +88,6 @@ 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_SMS_ALL
self.features_call_sms.columns.to_list(),
FEATURES_CALLS + FEATURES_SMS + FEATURES_CONTACT,
)

3
test/test_esm.py
View File

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

27
test/test_features_sensor.py
View File

@ -1,27 +0,0 @@
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)

4
test/test_proximity.py
View File

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