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2d2f0b916f
...
e2e268148d
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@ -5,7 +5,3 @@ __pycache__/
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/exploration/*.ipynb
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/config/*.ipynb
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/statistical_analysis/*.ipynb
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/machine_learning/intermediate_results/
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/data/features/
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/data/baseline/
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/data/*input*.csv
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||||
|
|
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@ -1,4 +0,0 @@
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[submodule "rapids"]
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path = rapids
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url = https://repo.ijs.si/junoslukan/rapids.git
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branch = master
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||||
|
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@ -4,17 +4,4 @@
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|||
<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">
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||||
<value>
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||||
<RMarkdownRenderProfile>
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||||
<option name="outputDirectoryUrl" value="file://$PROJECT_DIR$/rapids/src/visualization" />
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||||
</RMarkdownRenderProfile>
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||||
</value>
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||||
</entry>
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||||
</map>
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||||
</option>
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||||
</component>
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||||
</project>
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||||
|
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@ -1,4 +0,0 @@
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|||
<?xml version="1.0" encoding="UTF-8"?>
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||||
<project version="4">
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||||
<component name="SmkProjectSettings" sdk="Python 3.10 (snakemake)" enabled="true" />
|
||||
</project>
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||||
|
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@ -2,6 +2,5 @@
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|||
<project version="4">
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||||
<component name="VcsDirectoryMappings">
|
||||
<mapping directory="$PROJECT_DIR$" vcs="Git" />
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||||
<mapping directory="$PROJECT_DIR$/rapids" vcs="Git" />
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||||
</component>
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||||
</project>
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||||
128
README.md
128
README.md
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@ -27,135 +27,9 @@ To install:
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|||
ipython kernel install --user --name=straw2analysis
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||||
```
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||||
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||||
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:
|
||||
|
||||
```
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||||
DB_PASSWORD=database-password
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||||
```
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||||
|
||||
# RAPIDS
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||||
|
||||
To install RAPIDS, follow the [instructions on their webpage](https://www.rapids.science/1.6/setup/installation/).
|
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|
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Here, I include additional information related to the installation and specific to the STRAW2analysis project.
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||||
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
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||||
|
||||
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.
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||||
It should contain:
|
||||
|
||||
```yaml
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||||
PSQL_STRAW:
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||||
database: staw
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||||
host: 212.235.208.113
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||||
password: password
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||||
port: 5432
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||||
user: staw_db
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||||
```
|
||||
|
||||
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 ---------------------------
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||||
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=...'
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||||
--------------------------[ ERROR MESSAGE ]----------------------------
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||||
<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
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||||
> 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
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||||
Calls: <Anonymous> ... namespaceImportFrom -> asNamespace -> loadNamespace
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||||
Execution halted
|
||||
ERROR: lazy loading failed for package ‘tidyverse’
|
||||
```
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||||
|
||||
This happens because WSL2 does not use the `timedatectl` service, which provides this variable.
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||||
|
||||
```bash
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||||
~$ timedatectl
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||||
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
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||||
Warning message:
|
||||
In system("timedatectl", intern = TRUE) :
|
||||
running command 'timedatectl' had status 1
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||||
Execution halted
|
||||
```
|
||||
|
||||
This can be amended by setting the environment variable manually before attempting to install `tidyverse`:
|
||||
|
||||
```bash
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||||
export TZ='Europe/Ljubljana'
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||||
```
|
||||
|
||||
## Possible runtime issues
|
||||
### Unix end of line characters
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||||
|
||||
Upon running rapids, an error might occur:
|
||||
|
||||
```bash
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||||
/usr/bin/env: ‘python3\r’: No such file or directory
|
||||
```
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||||
|
||||
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.
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||||
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"))'
|
||||
```
|
||||
|
||||
|
|
|
|||
|
|
@ -12,11 +12,9 @@ dependencies:
|
|||
- mypy
|
||||
- nodejs
|
||||
- pandas
|
||||
- psycopg2 >= 2.9.1
|
||||
- psycopg2
|
||||
- python-dotenv
|
||||
- pytz
|
||||
- pyprojroot
|
||||
- pyyaml
|
||||
- seaborn
|
||||
- scikit-learn
|
||||
- sqlalchemy
|
||||
|
|
|
|||
|
|
@ -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
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||||
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"
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
|
|
@ -1,323 +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 os, sys
|
||||
import importlib
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
|
||||
# import plotly.graph_objects as go
|
||||
from importlib import util
|
||||
from pathlib import Path
|
||||
import yaml
|
||||
|
||||
# %%
|
||||
phone_data_yield = pd.read_csv(
|
||||
"../rapids/data/interim/p011/phone_yielded_timestamps_with_datetime.csv",
|
||||
parse_dates=["local_date_time"],
|
||||
)
|
||||
time_segments_labels = pd.read_csv(
|
||||
"../rapids/data/interim/time_segments/p011_time_segments_labels.csv"
|
||||
)
|
||||
|
||||
# %%
|
||||
phone_data_yield["assigned_segments"] = phone_data_yield[
|
||||
"assigned_segments"
|
||||
].str.replace(r"_RR\d+SS#", "#")
|
||||
time_segments_labels["label"] = time_segments_labels["label"].str.replace(
|
||||
r"_RR\d+SS$", ""
|
||||
)
|
||||
|
||||
|
||||
# %% tags=[]
|
||||
def filter_data_by_segment(data, time_segment):
|
||||
data.dropna(subset=["assigned_segments"], inplace=True)
|
||||
if data.shape[0] == 0: # data is empty
|
||||
data["local_segment"] = data["timestamps_segment"] = None
|
||||
return data
|
||||
|
||||
datetime_regex = "[0-9]{4}[\-|\/][0-9]{2}[\-|\/][0-9]{2} [0-9]{2}:[0-9]{2}:[0-9]{2}"
|
||||
timestamps_regex = "[0-9]{13}"
|
||||
segment_regex = "\[({}#{},{};{},{})\]".format(
|
||||
time_segment, datetime_regex, datetime_regex, timestamps_regex, timestamps_regex
|
||||
)
|
||||
data["local_segment"] = data["assigned_segments"].str.extract(
|
||||
segment_regex, expand=True
|
||||
)
|
||||
data = data.drop(columns=["assigned_segments"])
|
||||
data = data.dropna(subset=["local_segment"])
|
||||
if (
|
||||
data.shape[0] == 0
|
||||
): # there are no rows belonging to time_segment after droping na
|
||||
data["timestamps_segment"] = None
|
||||
else:
|
||||
data[["local_segment", "timestamps_segment"]] = data["local_segment"].str.split(
|
||||
pat=";", n=1, expand=True
|
||||
)
|
||||
|
||||
# chunk episodes
|
||||
if (
|
||||
(not data.empty)
|
||||
and ("start_timestamp" in data.columns)
|
||||
and ("end_timestamp" in data.columns)
|
||||
):
|
||||
data = chunk_episodes(data)
|
||||
|
||||
return data
|
||||
|
||||
|
||||
# %% tags=[]
|
||||
time_segment = "daily"
|
||||
phone_data_yield_per_segment = filter_data_by_segment(phone_data_yield, time_segment)
|
||||
|
||||
# %%
|
||||
phone_data_yield.tail()
|
||||
|
||||
# %%
|
||||
phone_data_yield_per_segment.tail()
|
||||
|
||||
|
||||
# %%
|
||||
def getDataForPlot(phone_data_yield_per_segment):
|
||||
# calculate the length (in minute) of per segment instance
|
||||
phone_data_yield_per_segment["length"] = (
|
||||
phone_data_yield_per_segment["timestamps_segment"]
|
||||
.str.split(",")
|
||||
.apply(lambda x: int((int(x[1]) - int(x[0])) / (1000 * 60)))
|
||||
)
|
||||
# calculate the number of sensors logged at least one row of data per minute.
|
||||
phone_data_yield_per_segment = (
|
||||
phone_data_yield_per_segment.groupby(
|
||||
["local_segment", "length", "local_date", "local_hour", "local_minute"]
|
||||
)[["sensor", "local_date_time"]]
|
||||
.max()
|
||||
.reset_index()
|
||||
)
|
||||
# extract local start datetime of the segment from "local_segment" column
|
||||
phone_data_yield_per_segment["local_segment_start_datetimes"] = pd.to_datetime(
|
||||
phone_data_yield_per_segment["local_segment"].apply(
|
||||
lambda x: x.split("#")[1].split(",")[0]
|
||||
)
|
||||
)
|
||||
# calculate the number of minutes after local start datetime of the segment
|
||||
phone_data_yield_per_segment["minutes_after_segment_start"] = (
|
||||
(
|
||||
phone_data_yield_per_segment["local_date_time"]
|
||||
- phone_data_yield_per_segment["local_segment_start_datetimes"]
|
||||
)
|
||||
/ pd.Timedelta(minutes=1)
|
||||
).astype("int")
|
||||
|
||||
# impute missing rows with 0
|
||||
columns_for_full_index = phone_data_yield_per_segment[
|
||||
["local_segment_start_datetimes", "length"]
|
||||
].drop_duplicates(keep="first")
|
||||
columns_for_full_index = columns_for_full_index.apply(
|
||||
lambda row: [
|
||||
[row["local_segment_start_datetimes"], x] for x in range(row["length"] + 1)
|
||||
],
|
||||
axis=1,
|
||||
)
|
||||
full_index = []
|
||||
for columns in columns_for_full_index:
|
||||
full_index = full_index + columns
|
||||
full_index = pd.MultiIndex.from_tuples(
|
||||
full_index,
|
||||
names=("local_segment_start_datetimes", "minutes_after_segment_start"),
|
||||
)
|
||||
phone_data_yield_per_segment = (
|
||||
phone_data_yield_per_segment.set_index(
|
||||
["local_segment_start_datetimes", "minutes_after_segment_start"]
|
||||
)
|
||||
.reindex(full_index)
|
||||
.reset_index()
|
||||
.fillna(0)
|
||||
)
|
||||
|
||||
# transpose the dataframe per local start datetime of the segment and discard the useless index layer
|
||||
phone_data_yield_per_segment = phone_data_yield_per_segment.groupby(
|
||||
"local_segment_start_datetimes"
|
||||
)[["minutes_after_segment_start", "sensor"]].apply(
|
||||
lambda x: x.set_index("minutes_after_segment_start").transpose()
|
||||
)
|
||||
phone_data_yield_per_segment.index = phone_data_yield_per_segment.index.get_level_values(
|
||||
"local_segment_start_datetimes"
|
||||
)
|
||||
return phone_data_yield_per_segment
|
||||
|
||||
|
||||
# %%
|
||||
data_for_plot_per_segment = getDataForPlot(phone_data_yield_per_segment)
|
||||
|
||||
# %%
|
||||
# calculate the length (in minute) of per segment instance
|
||||
phone_data_yield_per_segment["length"] = (
|
||||
phone_data_yield_per_segment["timestamps_segment"]
|
||||
.str.split(",")
|
||||
.apply(lambda x: int((int(x[1]) - int(x[0])) / (1000 * 60)))
|
||||
)
|
||||
|
||||
# %%
|
||||
phone_data_yield_per_segment.tail()
|
||||
|
||||
# %%
|
||||
# calculate the number of sensors logged at least one row of data per minute.
|
||||
phone_data_yield_per_segment = (
|
||||
phone_data_yield_per_segment.groupby(
|
||||
["local_segment", "length", "local_date", "local_hour", "local_minute"]
|
||||
)[["sensor", "local_date_time"]]
|
||||
.max()
|
||||
.reset_index()
|
||||
)
|
||||
|
||||
# %%
|
||||
# extract local start datetime of the segment from "local_segment" column
|
||||
phone_data_yield_per_segment["local_segment_start_datetimes"] = pd.to_datetime(
|
||||
phone_data_yield_per_segment["local_segment"].apply(
|
||||
lambda x: x.split("#")[1].split(",")[0]
|
||||
)
|
||||
)
|
||||
|
||||
# %%
|
||||
# calculate the number of minutes after local start datetime of the segment
|
||||
phone_data_yield_per_segment["minutes_after_segment_start"] = (
|
||||
(
|
||||
phone_data_yield_per_segment["local_date_time"]
|
||||
- phone_data_yield_per_segment["local_segment_start_datetimes"]
|
||||
)
|
||||
/ pd.Timedelta(minutes=1)
|
||||
).astype("int")
|
||||
|
||||
# %%
|
||||
columns_for_full_index = phone_data_yield_per_segment[
|
||||
["local_segment_start_datetimes", "length"]
|
||||
].drop_duplicates(keep="first")
|
||||
columns_for_full_index = columns_for_full_index.apply(
|
||||
lambda row: [
|
||||
[row["local_segment_start_datetimes"], x] for x in range(row["length"] + 1)
|
||||
],
|
||||
axis=1,
|
||||
)
|
||||
|
||||
# %%
|
||||
full_index = []
|
||||
for columns in columns_for_full_index:
|
||||
full_index = full_index + columns
|
||||
full_index = pd.MultiIndex.from_tuples(
|
||||
full_index, names=("local_segment_start_datetimes", "minutes_after_segment_start")
|
||||
)
|
||||
|
||||
# %%
|
||||
phone_data_yield_per_segment.tail()
|
||||
|
||||
# %% [markdown]
|
||||
# # A workaround
|
||||
|
||||
# %%
|
||||
phone_data_yield_per_segment["local_segment_start_datetimes", "minutes_after_segment_start"] = phone_data_yield_per_segment[
|
||||
["local_segment_start_datetimes", "minutes_after_segment_start"]
|
||||
].drop_duplicates(keep="first")
|
||||
|
||||
# %%
|
||||
phone_data_yield_per_segment.set_index(
|
||||
["local_segment_start_datetimes", "minutes_after_segment_start"],
|
||||
verify_integrity=True,
|
||||
).reindex(full_index)
|
||||
|
||||
# %%
|
||||
phone_data_yield_per_segment.head()
|
||||
|
||||
|
||||
# %% [markdown]
|
||||
# # Retry
|
||||
|
||||
# %%
|
||||
def getDataForPlot(phone_data_yield_per_segment):
|
||||
# calculate the length (in minute) of per segment instance
|
||||
phone_data_yield_per_segment["length"] = (
|
||||
phone_data_yield_per_segment["timestamps_segment"]
|
||||
.str.split(",")
|
||||
.apply(lambda x: int((int(x[1]) - int(x[0])) / (1000 * 60)))
|
||||
)
|
||||
# calculate the number of sensors logged at least one row of data per minute.
|
||||
phone_data_yield_per_segment = (
|
||||
phone_data_yield_per_segment.groupby(
|
||||
["local_segment", "length", "local_date", "local_hour", "local_minute"]
|
||||
)[["sensor", "local_date_time"]]
|
||||
.max()
|
||||
.reset_index()
|
||||
)
|
||||
# extract local start datetime of the segment from "local_segment" column
|
||||
phone_data_yield_per_segment["local_segment_start_datetimes"] = pd.to_datetime(
|
||||
phone_data_yield_per_segment["local_segment"].apply(
|
||||
lambda x: x.split("#")[1].split(",")[0]
|
||||
)
|
||||
)
|
||||
# calculate the number of minutes after local start datetime of the segment
|
||||
phone_data_yield_per_segment["minutes_after_segment_start"] = (
|
||||
(
|
||||
phone_data_yield_per_segment["local_date_time"]
|
||||
- phone_data_yield_per_segment["local_segment_start_datetimes"]
|
||||
)
|
||||
/ pd.Timedelta(minutes=1)
|
||||
).astype("int")
|
||||
|
||||
# impute missing rows with 0
|
||||
columns_for_full_index = phone_data_yield_per_segment[
|
||||
["local_segment_start_datetimes", "length"]
|
||||
].drop_duplicates(keep="first")
|
||||
columns_for_full_index = columns_for_full_index.apply(
|
||||
lambda row: [
|
||||
[row["local_segment_start_datetimes"], x] for x in range(row["length"] + 1)
|
||||
],
|
||||
axis=1,
|
||||
)
|
||||
full_index = []
|
||||
for columns in columns_for_full_index:
|
||||
full_index = full_index + columns
|
||||
full_index = pd.MultiIndex.from_tuples(
|
||||
full_index,
|
||||
names=("local_segment_start_datetimes", "minutes_after_segment_start"),
|
||||
)
|
||||
phone_data_yield_per_segment = phone_data_yield_per_segment.drop_duplicates(subset=["local_segment_start_datetimes", "minutes_after_segment_start"],keep="first")
|
||||
phone_data_yield_per_segment = (
|
||||
phone_data_yield_per_segment.set_index(
|
||||
["local_segment_start_datetimes", "minutes_after_segment_start"]
|
||||
)
|
||||
.reindex(full_index)
|
||||
.reset_index()
|
||||
.fillna(0)
|
||||
)
|
||||
|
||||
# transpose the dataframe per local start datetime of the segment and discard the useless index layer
|
||||
phone_data_yield_per_segment = phone_data_yield_per_segment.groupby(
|
||||
"local_segment_start_datetimes"
|
||||
)[["minutes_after_segment_start", "sensor"]].apply(
|
||||
lambda x: x.set_index("minutes_after_segment_start").transpose()
|
||||
)
|
||||
phone_data_yield_per_segment.index = phone_data_yield_per_segment.index.get_level_values(
|
||||
"local_segment_start_datetimes"
|
||||
)
|
||||
return phone_data_yield_per_segment
|
||||
|
||||
|
||||
# %%
|
||||
phone_data_yield_per_segment = filter_data_by_segment(phone_data_yield, time_segment)
|
||||
|
||||
# %%
|
||||
data_for_plot_per_segment = getDataForPlot(phone_data_yield_per_segment)
|
||||
|
||||
# %%
|
||||
|
|
@ -1,473 +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": true}
|
||||
# %matplotlib inline
|
||||
import datetime
|
||||
import importlib
|
||||
import os
|
||||
import sys
|
||||
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import pandas as pd
|
||||
import seaborn as sns
|
||||
import yaml
|
||||
from pyprojroot import here
|
||||
from sklearn import linear_model, svm, kernel_ridge, gaussian_process, ensemble
|
||||
from sklearn.model_selection import LeaveOneGroupOut, cross_val_score
|
||||
from sklearn.metrics import mean_squared_error, r2_score
|
||||
from sklearn.impute import SimpleImputer
|
||||
from xgboost import XGBRegressor
|
||||
|
||||
nb_dir = os.path.split(os.getcwd())[0]
|
||||
if nb_dir not in sys.path:
|
||||
sys.path.append(nb_dir)
|
||||
|
||||
import machine_learning.features_sensor
|
||||
import machine_learning.labels
|
||||
import machine_learning.model
|
||||
|
||||
# %% [markdown]
|
||||
# # RAPIDS models
|
||||
|
||||
# %% [markdown]
|
||||
# ## PANAS negative affect
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
# model_input = pd.read_csv("../data/input_PANAS_NA.csv") # Nestandardizirani podatki
|
||||
model_input = pd.read_csv("../data/z_input_PANAS_NA.csv") # Standardizirani podatki
|
||||
|
||||
# %% [markdown]
|
||||
# ### NaNs before dropping cols and rows
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
sns.set(rc={"figure.figsize":(16, 8)})
|
||||
sns.heatmap(model_input.sort_values('pid').set_index('pid').isna(), cbar=False)
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
nan_cols = list(model_input.loc[:, model_input.isna().all()].columns)
|
||||
nan_cols
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
model_input.dropna(axis=1, how="all", inplace=True)
|
||||
model_input.dropna(axis=0, how="any", subset=["target"], inplace=True)
|
||||
|
||||
# %% [markdown]
|
||||
# ### NaNs after dropping NaN cols and rows where target is NaN
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
sns.set(rc={"figure.figsize":(16, 8)})
|
||||
sns.heatmap(model_input.sort_values('pid').set_index('pid').isna(), cbar=False)
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
|
||||
#if "pid" in model_input.columns:
|
||||
# index_columns.append("pid")
|
||||
model_input.set_index(index_columns, inplace=True)
|
||||
|
||||
data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"]
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
categorical_feature_colnames = ["gender", "startlanguage"]
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
categorical_features = data_x[categorical_feature_colnames].copy()
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
mode_categorical_features = categorical_features.mode().iloc[0]
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
# fillna with mode
|
||||
categorical_features = categorical_features.fillna(mode_categorical_features)
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
# one-hot encoding
|
||||
categorical_features = categorical_features.apply(lambda col: col.astype("category"))
|
||||
if not categorical_features.empty:
|
||||
categorical_features = pd.get_dummies(categorical_features)
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
numerical_features = data_x.drop(categorical_feature_colnames, axis=1)
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
train_x = pd.concat([numerical_features, categorical_features], axis=1)
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
train_x.dtypes
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
logo = LeaveOneGroupOut()
|
||||
logo.get_n_splits(
|
||||
train_x,
|
||||
data_y,
|
||||
groups=data_groups,
|
||||
)
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
sum(data_y.isna())
|
||||
|
||||
# %% [markdown]
|
||||
# ### Linear Regression
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
lin_reg_rapids = linear_model.LinearRegression()
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
lin_reg_scores = cross_val_score(
|
||||
lin_reg_rapids,
|
||||
X=imputer.fit_transform(train_x),
|
||||
y=data_y,
|
||||
groups=data_groups,
|
||||
cv=logo,
|
||||
n_jobs=-1,
|
||||
scoring='r2'
|
||||
)
|
||||
lin_reg_scores
|
||||
np.median(lin_reg_scores)
|
||||
|
||||
# %% [markdown]
|
||||
# ### Ridge regression
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
ridge_reg = linear_model.Ridge(alpha=.5)
|
||||
|
||||
# %% tags=[] jupyter={"source_hidden": true}
|
||||
ridge_reg_scores = cross_val_score(
|
||||
ridge_reg,
|
||||
X=imputer.fit_transform(train_x),
|
||||
y=data_y,
|
||||
groups=data_groups,
|
||||
cv=logo,
|
||||
n_jobs=-1,
|
||||
scoring="r2"
|
||||
)
|
||||
np.median(ridge_reg_scores)
|
||||
|
||||
# %% [markdown]
|
||||
# ### Lasso
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
lasso_reg = linear_model.Lasso(alpha=0.1)
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
lasso_reg_score = cross_val_score(
|
||||
lasso_reg,
|
||||
X=imputer.fit_transform(train_x),
|
||||
y=data_y,
|
||||
groups=data_groups,
|
||||
cv=logo,
|
||||
n_jobs=-1,
|
||||
scoring="r2"
|
||||
)
|
||||
np.median(lasso_reg_score)
|
||||
|
||||
# %% [markdown]
|
||||
# ### Bayesian Ridge
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
bayesian_ridge_reg = linear_model.BayesianRidge()
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
bayesian_ridge_reg_score = cross_val_score(
|
||||
bayesian_ridge_reg,
|
||||
X=imputer.fit_transform(train_x),
|
||||
y=data_y,
|
||||
groups=data_groups,
|
||||
cv=logo,
|
||||
n_jobs=-1,
|
||||
scoring="r2"
|
||||
)
|
||||
np.median(bayesian_ridge_reg_score)
|
||||
|
||||
# %% [markdown]
|
||||
# ### RANSAC (outlier robust regression)
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
ransac_reg = linear_model.RANSACRegressor()
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
np.median(
|
||||
cross_val_score(
|
||||
ransac_reg,
|
||||
X=imputer.fit_transform(train_x),
|
||||
y=data_y,
|
||||
groups=data_groups,
|
||||
cv=logo,
|
||||
n_jobs=-1,
|
||||
scoring="r2"
|
||||
)
|
||||
)
|
||||
|
||||
# %% [markdown]
|
||||
# ### Support vector regression
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
svr = svm.SVR()
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
np.median(
|
||||
cross_val_score(
|
||||
svr,
|
||||
X=imputer.fit_transform(train_x),
|
||||
y=data_y,
|
||||
groups=data_groups,
|
||||
cv=logo,
|
||||
n_jobs=-1,
|
||||
scoring="r2"
|
||||
)
|
||||
)
|
||||
|
||||
# %% [markdown]
|
||||
# ### Kernel Ridge regression
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
kridge = kernel_ridge.KernelRidge()
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
np.median(
|
||||
cross_val_score(
|
||||
kridge,
|
||||
X=imputer.fit_transform(train_x),
|
||||
y=data_y,
|
||||
groups=data_groups,
|
||||
cv=logo,
|
||||
n_jobs=-1,
|
||||
scoring="r2"
|
||||
)
|
||||
)
|
||||
# %% [markdown]
|
||||
# ### Gaussian Process Regression
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
gpr = gaussian_process.GaussianProcessRegressor()
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
|
||||
np.median(
|
||||
cross_val_score(
|
||||
gpr,
|
||||
X=imputer.fit_transform(train_x),
|
||||
y=data_y,
|
||||
groups=data_groups,
|
||||
cv=logo,
|
||||
n_jobs=-1,
|
||||
scoring="r2"
|
||||
)
|
||||
)
|
||||
# %%
|
||||
def insert_row(df, row):
|
||||
return pd.concat([df, pd.DataFrame([row], columns=df.columns)], ignore_index=True)
|
||||
|
||||
# %%
|
||||
def run_all_models(input_csv):
|
||||
# Prepare data
|
||||
model_input = pd.read_csv(input_csv)
|
||||
model_input.dropna(axis=1, how="all", inplace=True)
|
||||
model_input.dropna(axis=0, how="any", subset=["target"], inplace=True)
|
||||
|
||||
index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
|
||||
model_input.set_index(index_columns, inplace=True)
|
||||
|
||||
data_x, data_y, data_groups = model_input.drop(["target", "pid"], axis=1), model_input["target"], model_input["pid"]
|
||||
|
||||
categorical_feature_colnames = ["gender", "startlanguage"]
|
||||
categorical_features = data_x[categorical_feature_colnames].copy()
|
||||
mode_categorical_features = categorical_features.mode().iloc[0]
|
||||
# fillna with mode
|
||||
categorical_features = categorical_features.fillna(mode_categorical_features)
|
||||
# one-hot encoding
|
||||
categorical_features = categorical_features.apply(lambda col: col.astype("category"))
|
||||
if not categorical_features.empty:
|
||||
categorical_features = pd.get_dummies(categorical_features)
|
||||
|
||||
numerical_features = data_x.drop(categorical_feature_colnames, axis=1)
|
||||
|
||||
train_x = pd.concat([numerical_features, categorical_features], axis=1)
|
||||
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
|
||||
train_x_imputed = imputer.fit_transform(train_x)
|
||||
|
||||
# Prepare cross validation
|
||||
logo = LeaveOneGroupOut()
|
||||
logo.get_n_splits(
|
||||
train_x,
|
||||
data_y,
|
||||
groups=data_groups,
|
||||
)
|
||||
scores = pd.DataFrame(columns=["method", "median", "max"])
|
||||
|
||||
# Validate models
|
||||
lin_reg_rapids = linear_model.LinearRegression()
|
||||
lin_reg_scores = cross_val_score(
|
||||
lin_reg_rapids,
|
||||
X=train_x_imputed,
|
||||
y=data_y,
|
||||
groups=data_groups,
|
||||
cv=logo,
|
||||
n_jobs=-1,
|
||||
scoring='r2'
|
||||
)
|
||||
print("Linear regression:")
|
||||
print(np.median(lin_reg_scores))
|
||||
scores = insert_row(scores, ["Linear regression",np.median(lin_reg_scores),np.max(lin_reg_scores)])
|
||||
|
||||
ridge_reg = linear_model.Ridge(alpha=.5)
|
||||
ridge_reg_scores = cross_val_score(
|
||||
ridge_reg,
|
||||
X=train_x_imputed,
|
||||
y=data_y,
|
||||
groups=data_groups,
|
||||
cv=logo,
|
||||
n_jobs=-1,
|
||||
scoring="r2"
|
||||
)
|
||||
print("Ridge regression")
|
||||
print(np.median(ridge_reg_scores))
|
||||
scores = insert_row(scores, ["Ridge regression",np.median(ridge_reg_scores),np.max(ridge_reg_scores)])
|
||||
|
||||
lasso_reg = linear_model.Lasso(alpha=0.1)
|
||||
lasso_reg_score = cross_val_score(
|
||||
lasso_reg,
|
||||
X=train_x_imputed,
|
||||
y=data_y,
|
||||
groups=data_groups,
|
||||
cv=logo,
|
||||
n_jobs=-1,
|
||||
scoring="r2"
|
||||
)
|
||||
print("Lasso regression")
|
||||
print(np.median(lasso_reg_score))
|
||||
scores = insert_row(scores, ["Lasso regression",np.median(lasso_reg_score),np.max(lasso_reg_score)])
|
||||
|
||||
bayesian_ridge_reg = linear_model.BayesianRidge()
|
||||
bayesian_ridge_reg_score = cross_val_score(
|
||||
bayesian_ridge_reg,
|
||||
X=train_x_imputed,
|
||||
y=data_y,
|
||||
groups=data_groups,
|
||||
cv=logo,
|
||||
n_jobs=-1,
|
||||
scoring="r2"
|
||||
)
|
||||
print("Bayesian Ridge")
|
||||
print(np.median(bayesian_ridge_reg_score))
|
||||
scores = insert_row(scores, ["Bayesian Ridge",np.median(bayesian_ridge_reg_score),np.max(bayesian_ridge_reg_score)])
|
||||
|
||||
ransac_reg = linear_model.RANSACRegressor()
|
||||
ransac_reg_score = cross_val_score(
|
||||
ransac_reg,
|
||||
X=train_x_imputed,
|
||||
y=data_y,
|
||||
groups=data_groups,
|
||||
cv=logo,
|
||||
n_jobs=-1,
|
||||
scoring="r2"
|
||||
)
|
||||
print("RANSAC (outlier robust regression)")
|
||||
print(np.median(ransac_reg_score))
|
||||
scores = insert_row(scores, ["RANSAC",np.median(ransac_reg_score),np.max(ransac_reg_score)])
|
||||
|
||||
svr = svm.SVR()
|
||||
svr_score = cross_val_score(
|
||||
svr,
|
||||
X=train_x_imputed,
|
||||
y=data_y,
|
||||
groups=data_groups,
|
||||
cv=logo,
|
||||
n_jobs=-1,
|
||||
scoring="r2"
|
||||
)
|
||||
print("Support vector regression")
|
||||
print(np.median(svr_score))
|
||||
scores = insert_row(scores, ["Support vector regression",np.median(svr_score),np.max(svr_score)])
|
||||
|
||||
kridge = kernel_ridge.KernelRidge()
|
||||
kridge_score = cross_val_score(
|
||||
kridge,
|
||||
X=train_x_imputed,
|
||||
y=data_y,
|
||||
groups=data_groups,
|
||||
cv=logo,
|
||||
n_jobs=-1,
|
||||
scoring="r2"
|
||||
)
|
||||
print("Kernel Ridge regression")
|
||||
print(np.median(kridge_score))
|
||||
scores = insert_row(scores, ["Kernel Ridge regression",np.median(kridge_score),np.max(kridge_score)])
|
||||
|
||||
gpr = gaussian_process.GaussianProcessRegressor()
|
||||
gpr_score = cross_val_score(
|
||||
gpr,
|
||||
X=train_x_imputed,
|
||||
y=data_y,
|
||||
groups=data_groups,
|
||||
cv=logo,
|
||||
n_jobs=-1,
|
||||
scoring="r2"
|
||||
)
|
||||
print("Gaussian Process Regression")
|
||||
print(np.median(gpr_score))
|
||||
scores = insert_row(scores, ["Gaussian Process Regression",np.median(gpr_score),np.max(gpr_score)])
|
||||
|
||||
rfr = ensemble.RandomForestRegressor(max_features=0.3, n_jobs=-1)
|
||||
rfr_score = cross_val_score(
|
||||
rfr,
|
||||
X=train_x_imputed,
|
||||
y=data_y,
|
||||
groups=data_groups,
|
||||
cv=logo,
|
||||
n_jobs=-1,
|
||||
scoring="r2"
|
||||
)
|
||||
print("Random Forest Regression")
|
||||
print(np.median(rfr_score))
|
||||
scores = insert_row(scores, ["Random Forest Regression",np.median(rfr_score),np.max(rfr_score)])
|
||||
|
||||
xgb = XGBRegressor()
|
||||
xgb_score = cross_val_score(
|
||||
xgb,
|
||||
X=train_x_imputed,
|
||||
y=data_y,
|
||||
groups=data_groups,
|
||||
cv=logo,
|
||||
n_jobs=-1,
|
||||
scoring="r2"
|
||||
)
|
||||
print("XGBoost Regressor")
|
||||
print(np.median(xgb_score))
|
||||
scores = insert_row(scores, ["XGBoost Regressor",np.median(xgb_score),np.max(xgb_score)])
|
||||
|
||||
ada = ensemble.AdaBoostRegressor()
|
||||
ada_score = cross_val_score(
|
||||
ada,
|
||||
X=train_x_imputed,
|
||||
y=data_y,
|
||||
groups=data_groups,
|
||||
cv=logo,
|
||||
n_jobs=-1,
|
||||
scoring="r2"
|
||||
)
|
||||
print("ADA Boost Regressor")
|
||||
print(np.median(ada_score))
|
||||
scores = insert_row(scores, ["ADA Boost Regressor",np.median(ada_score),np.max(ada_score)])
|
||||
|
||||
return scores
|
||||
|
||||
|
||||
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -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)
|
||||
|
||||
# %%
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@
|
|||
# extension: .py
|
||||
# format_name: percent
|
||||
# format_version: '1.3'
|
||||
# jupytext_version: 1.13.0
|
||||
# jupytext_version: 1.11.2
|
||||
# kernelspec:
|
||||
# display_name: straw2analysis
|
||||
# language: python
|
||||
|
|
@ -17,7 +17,6 @@
|
|||
# %%
|
||||
import os
|
||||
import sys
|
||||
import datetime
|
||||
|
||||
import seaborn as sns
|
||||
|
||||
|
|
@ -27,7 +26,6 @@ if nb_dir not in sys.path:
|
|||
import participants.query_db
|
||||
from features.esm import *
|
||||
from features.esm_JCQ import *
|
||||
from features.esm_SAM import *
|
||||
|
||||
# %%
|
||||
participants_inactive_usernames = participants.query_db.get_usernames(
|
||||
|
|
@ -101,12 +99,6 @@ df_esm_PANAS_summary_participant[df_esm_PANAS_summary_participant["std"] < 0.1]
|
|||
# %% [markdown]
|
||||
# # Stress appraisal measure
|
||||
|
||||
# %%
|
||||
df_SAM_all = extract_stressful_events(df_esm_inactive)
|
||||
|
||||
# %%
|
||||
df_SAM_all.head()
|
||||
|
||||
# %%
|
||||
df_esm_SAM = df_esm_preprocessed[
|
||||
(df_esm_preprocessed["questionnaire_id"] >= 87)
|
||||
|
|
|
|||
|
|
@ -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.
|
||||
|
|
@ -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)
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
@ -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:
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
@ -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(
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
|
@ -1,205 +0,0 @@
|
|||
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
|
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<text text-anchor="middle" x="187" y="-15.5" font-family="sans" font-size="10.00">all</text>
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<title>5->8</title>
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<title>6</title>
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<!-- 6->4 -->
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<title>6->4</title>
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<!-- 7 -->
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<g id="node8" class="node">
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<title>7</title>
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<title>7->6</title>
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<title>8->0</title>
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<title>9</title>
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<!-- 8->9 -->
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<title>8->9</title>
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<!-- 9->0 -->
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<g id="edge6" class="edge">
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<title>9->0</title>
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<!-- 10 -->
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<g id="node11" class="node">
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<title>10</title>
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<text text-anchor="middle" x="405" y="-159.5" font-family="sans" font-size="10.00">merge_sensor_features_for_individual_participants</text>
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</g>
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<!-- 9->10 -->
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<g id="edge18" class="edge">
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<title>9->10</title>
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<!-- 10->0 -->
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<g id="edge7" class="edge">
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<title>10->0</title>
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<!-- 11 -->
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<g id="node12" class="node">
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<title>11</title>
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</g>
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<g id="edge19" class="edge">
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<title>10->11</title>
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<g id="edge8" class="edge">
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<title>11->0</title>
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<text text-anchor="start" x="81" y="-71.6" font-family="sans" font-weight="bold" font-size="18.00">create_participants_files</text>
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<text text-anchor="start" x="81" y="-47.8" font-family="sans" font-size="10.00"> </text>
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<text text-anchor="start" x="85" y="-47.8" font-family="sans" font-weight="bold" font-size="14.00">↪ input</text>
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<text text-anchor="start" x="143" y="-47.8" font-family="sans" font-size="10.00"> </text>
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<text text-anchor="start" x="81" y="-28" font-family="monospace" font-size="10.00">data/external/example_participants.csv</text>
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<text text-anchor="start" x="319" y="-10" font-family="sans" font-size="10.00">  </text>
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<text text-anchor="start" x="77" y="-221.6" font-family="sans" font-weight="bold" font-size="18.00">prepare_participants_csv</text>
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<text text-anchor="start" x="81" y="-197.8" font-family="sans" font-weight="bold" font-size="14.00">↪ input</text>
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<text text-anchor="start" x="77" y="-178" font-family="monospace" font-size="10.00">data/external/example_usernames.csv</text>
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<text text-anchor="start" x="255" y="-157.8" font-family="sans" font-weight="bold" font-size="14.00">output →</text>
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<text text-anchor="start" x="325" y="-157.8" font-family="sans" font-size="10.00"> </text>
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<text text-anchor="start" x="77" y="-138" font-family="monospace" font-size="10.00">data/external/example_participants.csv</text>
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<g id="edge1" class="edge">
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<title>1->0</title>
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<text text-anchor="start" x="7" y="-367.6" font-family="sans" font-weight="bold" font-size="18.00">query_usernames_device_empatica_ids</text>
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<text text-anchor="start" x="325" y="-325.8" font-family="sans" font-weight="bold" font-size="14.00">output →</text>
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<text text-anchor="start" x="395" y="-325.8" font-family="sans" font-size="10.00"> </text>
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<text text-anchor="start" x="7" y="-306" font-family="monospace" font-size="10.00">data/external/example_usernames.csv</text>
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<g id="edge2" class="edge">
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<title>2->1</title>
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<polygon fill="grey" stroke="grey" stroke-width="2" points="206.5,-252.36 203,-242.36 199.5,-252.36 206.5,-252.36"/>
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</svg>
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|
Before Width: | Height: | Size: 4.6 KiB |
|
|
@ -1,5 +0,0 @@
|
|||
grouping_variable: date_lj
|
||||
features:
|
||||
proximity:
|
||||
all
|
||||
participants_usernames: [nokia_0000003]
|
||||
|
|
@ -1,6 +0,0 @@
|
|||
grouping_variable: date_lj
|
||||
labels:
|
||||
PANAS:
|
||||
- PA
|
||||
- NA
|
||||
participants_usernames: [nokia_0000003]
|
||||
|
|
@ -1,6 +0,0 @@
|
|||
grouping_variable: date_lj
|
||||
features:
|
||||
proximity:
|
||||
all
|
||||
communication:
|
||||
all
|
||||
|
|
@ -1,5 +0,0 @@
|
|||
grouping_variable: date_lj
|
||||
labels:
|
||||
PANAS:
|
||||
- PA
|
||||
- NA
|
||||
|
|
@ -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()
|
||||
|
|
@ -1,57 +0,0 @@
|
|||
from pathlib import Path
|
||||
|
||||
import pandas as pd
|
||||
|
||||
|
||||
def safe_outer_merge_on_index(left: pd.DataFrame, right: pd.DataFrame) -> pd.DataFrame:
|
||||
if left.empty:
|
||||
return right
|
||||
elif right.empty:
|
||||
return left
|
||||
else:
|
||||
return pd.merge(
|
||||
left,
|
||||
right,
|
||||
how="outer",
|
||||
left_index=True,
|
||||
right_index=True,
|
||||
validate="one_to_one",
|
||||
)
|
||||
|
||||
|
||||
def to_csv_with_settings(
|
||||
df: pd.DataFrame, folder: Path, filename_prefix: str, data_type: str
|
||||
) -> None:
|
||||
full_path = construct_full_path(folder, filename_prefix, data_type)
|
||||
df.to_csv(
|
||||
path_or_buf=full_path,
|
||||
sep=",",
|
||||
na_rep="NA",
|
||||
header=True,
|
||||
index=True,
|
||||
encoding="utf-8",
|
||||
)
|
||||
print("Exported the dataframe to " + str(full_path))
|
||||
|
||||
|
||||
def read_csv_with_settings(
|
||||
folder: Path, filename_prefix: str, data_type: str, grouping_variable: list
|
||||
) -> pd.DataFrame:
|
||||
full_path = construct_full_path(folder, filename_prefix, data_type)
|
||||
return pd.read_csv(
|
||||
filepath_or_buffer=full_path,
|
||||
sep=",",
|
||||
header=0,
|
||||
na_values="NA",
|
||||
encoding="utf-8",
|
||||
index_col=(["participant_id"] + grouping_variable),
|
||||
parse_dates=True,
|
||||
infer_datetime_format=True,
|
||||
cache_dates=True,
|
||||
)
|
||||
|
||||
|
||||
def construct_full_path(folder: Path, filename_prefix: str, data_type: str) -> Path:
|
||||
export_filename = filename_prefix + "_" + data_type + ".csv"
|
||||
full_path = folder / export_filename
|
||||
return full_path
|
||||
|
|
@ -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()
|
||||
|
|
@ -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",
|
||||
)
|
||||
|
|
@ -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))
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
|
|
@ -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",
|
||||
)
|
||||
1
rapids
1
rapids
|
|
@ -1 +0,0 @@
|
|||
Subproject commit f78aa3e7b3567423b44045766b230cd60d557cb0
|
||||
|
|
@ -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("-------------------------------------")
|
||||
|
|
|
|||
|
|
@ -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,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
|
@ -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):
|
||||
|
|
|
|||
Loading…
Reference in New Issue