junos
commit
848416bf6a
|
|
@ -6,4 +6,6 @@ __pycache__/
|
|||
/config/*.ipynb
|
||||
/statistical_analysis/*.ipynb
|
||||
/machine_learning/intermediate_results/
|
||||
/data
|
||||
/data/features/
|
||||
/data/baseline/
|
||||
/data/*input*.csv
|
||||
|
|
|
|||
|
|
@ -0,0 +1,4 @@
|
|||
[submodule "rapids"]
|
||||
path = rapids
|
||||
url = https://repo.ijs.si/junoslukan/rapids.git
|
||||
branch = master
|
||||
|
|
@ -4,4 +4,17 @@
|
|||
<component name="PyCharmProfessionalAdvertiser">
|
||||
<option name="shown" value="true" />
|
||||
</component>
|
||||
<component name="RMarkdownSettings">
|
||||
<option name="renderProfiles">
|
||||
<map>
|
||||
<entry key="file://$PROJECT_DIR$/rapids/src/visualization/merge_heatmap_sensors_per_minute_per_time_segment.Rmd">
|
||||
<value>
|
||||
<RMarkdownRenderProfile>
|
||||
<option name="outputDirectoryUrl" value="file://$PROJECT_DIR$/rapids/src/visualization" />
|
||||
</RMarkdownRenderProfile>
|
||||
</value>
|
||||
</entry>
|
||||
</map>
|
||||
</option>
|
||||
</component>
|
||||
</project>
|
||||
|
|
@ -0,0 +1,4 @@
|
|||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<project version="4">
|
||||
<component name="SmkProjectSettings" sdk="Python 3.10 (snakemake)" enabled="true" />
|
||||
</project>
|
||||
|
|
@ -2,5 +2,6 @@
|
|||
<project version="4">
|
||||
<component name="VcsDirectoryMappings">
|
||||
<mapping directory="$PROJECT_DIR$" vcs="Git" />
|
||||
<mapping directory="$PROJECT_DIR$/rapids" vcs="Git" />
|
||||
</component>
|
||||
</project>
|
||||
128
README.md
128
README.md
|
|
@ -32,4 +32,130 @@ To install:
|
|||
|
||||
```
|
||||
DB_PASSWORD=database-password
|
||||
```
|
||||
```
|
||||
|
||||
# RAPIDS
|
||||
|
||||
To install RAPIDS, follow the [instructions on their webpage](https://www.rapids.science/1.6/setup/installation/).
|
||||
|
||||
Here, I include additional information related to the installation and specific to the STRAW2analysis project.
|
||||
The installation was tested on Windows using Ubuntu 20.04 on Windows Subsystem for Linux ([WSL2](https://docs.microsoft.com/en-us/windows/wsl/install)).
|
||||
|
||||
## Custom configuration
|
||||
### Credentials
|
||||
|
||||
As mentioned under [Database in RAPIDS documentation](https://www.rapids.science/1.6/snippets/database/), a `credentials.yaml` file is needed to connect to a database.
|
||||
It should contain:
|
||||
|
||||
```yaml
|
||||
PSQL_STRAW:
|
||||
database: staw
|
||||
host: 212.235.208.113
|
||||
password: password
|
||||
port: 5432
|
||||
user: staw_db
|
||||
```
|
||||
|
||||
where`password` needs to be specified as well.
|
||||
|
||||
## Possible installation issues
|
||||
### Missing dependencies for RPostgres
|
||||
|
||||
To install `RPostgres` R package (used to connect to the PostgreSQL database), an error might occur:
|
||||
|
||||
```text
|
||||
------------------------- ANTICONF ERROR ---------------------------
|
||||
Configuration failed because libpq was not found. Try installing:
|
||||
* deb: libpq-dev (Debian, Ubuntu, etc)
|
||||
* rpm: postgresql-devel (Fedora, EPEL)
|
||||
* rpm: postgreql8-devel, psstgresql92-devel, postgresql93-devel, or postgresql94-devel (Amazon Linux)
|
||||
* csw: postgresql_dev (Solaris)
|
||||
* brew: libpq (OSX)
|
||||
If libpq is already installed, check that either:
|
||||
(i) 'pkg-config' is in your PATH AND PKG_CONFIG_PATH contains a libpq.pc file; or
|
||||
(ii) 'pg_config' is in your PATH.
|
||||
If neither can detect , you can set INCLUDE_DIR
|
||||
and LIB_DIR manually via:
|
||||
R CMD INSTALL --configure-vars='INCLUDE_DIR=... LIB_DIR=...'
|
||||
--------------------------[ ERROR MESSAGE ]----------------------------
|
||||
<stdin>:1:10: fatal error: libpq-fe.h: No such file or directory
|
||||
compilation terminated.
|
||||
```
|
||||
|
||||
The library requires `libpq` for compiling from source, so install accordingly.
|
||||
|
||||
### Timezone environment variable for tidyverse (relevant for WSL2)
|
||||
|
||||
One of the R packages, `tidyverse` might need access to the `TZ` environment variable during the installation.
|
||||
On Ubuntu 20.04 on WSL2 this triggers the following error:
|
||||
|
||||
```text
|
||||
> install.packages('tidyverse')
|
||||
|
||||
ERROR: configuration failed for package ‘xml2’
|
||||
System has not been booted with systemd as init system (PID 1). Can't operate.
|
||||
Failed to create bus connection: Host is down
|
||||
Warning in system("timedatectl", intern = TRUE) :
|
||||
running command 'timedatectl' had status 1
|
||||
Error in loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]]) :
|
||||
namespace ‘xml2’ 1.3.1 is already loaded, but >= 1.3.2 is required
|
||||
Calls: <Anonymous> ... namespaceImportFrom -> asNamespace -> loadNamespace
|
||||
Execution halted
|
||||
ERROR: lazy loading failed for package ‘tidyverse’
|
||||
```
|
||||
|
||||
This happens because WSL2 does not use the `timedatectl` service, which provides this variable.
|
||||
|
||||
```bash
|
||||
~$ timedatectl
|
||||
System has not been booted with systemd as init system (PID 1). Can't operate.
|
||||
Failed to create bus connection: Host is down
|
||||
```
|
||||
|
||||
and later
|
||||
|
||||
```bash
|
||||
Warning message:
|
||||
In system("timedatectl", intern = TRUE) :
|
||||
running command 'timedatectl' had status 1
|
||||
Execution halted
|
||||
```
|
||||
|
||||
This can be amended by setting the environment variable manually before attempting to install `tidyverse`:
|
||||
|
||||
```bash
|
||||
export TZ='Europe/Ljubljana'
|
||||
```
|
||||
|
||||
## Possible runtime issues
|
||||
### Unix end of line characters
|
||||
|
||||
Upon running rapids, an error might occur:
|
||||
|
||||
```bash
|
||||
/usr/bin/env: ‘python3\r’: No such file or directory
|
||||
```
|
||||
|
||||
This is due to Windows style end of line characters.
|
||||
To amend this, I added a `.gitattributes` files to force `git` to checkout `rapids` using Unix EOL characters.
|
||||
If this still fails, `dos2unix` can be used to change them.
|
||||
|
||||
### System has not been booted with systemd as init system (PID 1)
|
||||
|
||||
See [the installation issue above](#Timezone-environment-variable-for-tidyverse-(relevant-for-WSL2)).
|
||||
|
||||
## Update RAPIDS
|
||||
|
||||
To update RAPIDS, first pull and merge [origin]( https://github.com/carissalow/rapids), such as with:
|
||||
|
||||
```commandline
|
||||
git fetch --progress "origin" refs/heads/master
|
||||
git merge --no-ff origin/master
|
||||
```
|
||||
|
||||
Next, update the conda and R virtual environment.
|
||||
|
||||
```bash
|
||||
R -e 'renv::restore(repos = c(CRAN = "https://packagemanager.rstudio.com/all/__linux__/focal/latest"))'
|
||||
```
|
||||
|
||||
|
|
|
|||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,323 @@
|
|||
# ---
|
||||
# jupyter:
|
||||
# jupytext:
|
||||
# formats: ipynb,py:percent
|
||||
# text_representation:
|
||||
# extension: .py
|
||||
# format_name: percent
|
||||
# format_version: '1.3'
|
||||
# jupytext_version: 1.13.0
|
||||
# kernelspec:
|
||||
# display_name: straw2analysis
|
||||
# language: python
|
||||
# name: straw2analysis
|
||||
# ---
|
||||
|
||||
# %%
|
||||
import os, sys
|
||||
import importlib
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
|
||||
# import plotly.graph_objects as go
|
||||
from importlib import util
|
||||
from pathlib import Path
|
||||
import yaml
|
||||
|
||||
# %%
|
||||
phone_data_yield = pd.read_csv(
|
||||
"../rapids/data/interim/p011/phone_yielded_timestamps_with_datetime.csv",
|
||||
parse_dates=["local_date_time"],
|
||||
)
|
||||
time_segments_labels = pd.read_csv(
|
||||
"../rapids/data/interim/time_segments/p011_time_segments_labels.csv"
|
||||
)
|
||||
|
||||
# %%
|
||||
phone_data_yield["assigned_segments"] = phone_data_yield[
|
||||
"assigned_segments"
|
||||
].str.replace(r"_RR\d+SS#", "#")
|
||||
time_segments_labels["label"] = time_segments_labels["label"].str.replace(
|
||||
r"_RR\d+SS$", ""
|
||||
)
|
||||
|
||||
|
||||
# %% tags=[]
|
||||
def filter_data_by_segment(data, time_segment):
|
||||
data.dropna(subset=["assigned_segments"], inplace=True)
|
||||
if data.shape[0] == 0: # data is empty
|
||||
data["local_segment"] = data["timestamps_segment"] = None
|
||||
return data
|
||||
|
||||
datetime_regex = "[0-9]{4}[\-|\/][0-9]{2}[\-|\/][0-9]{2} [0-9]{2}:[0-9]{2}:[0-9]{2}"
|
||||
timestamps_regex = "[0-9]{13}"
|
||||
segment_regex = "\[({}#{},{};{},{})\]".format(
|
||||
time_segment, datetime_regex, datetime_regex, timestamps_regex, timestamps_regex
|
||||
)
|
||||
data["local_segment"] = data["assigned_segments"].str.extract(
|
||||
segment_regex, expand=True
|
||||
)
|
||||
data = data.drop(columns=["assigned_segments"])
|
||||
data = data.dropna(subset=["local_segment"])
|
||||
if (
|
||||
data.shape[0] == 0
|
||||
): # there are no rows belonging to time_segment after droping na
|
||||
data["timestamps_segment"] = None
|
||||
else:
|
||||
data[["local_segment", "timestamps_segment"]] = data["local_segment"].str.split(
|
||||
pat=";", n=1, expand=True
|
||||
)
|
||||
|
||||
# chunk episodes
|
||||
if (
|
||||
(not data.empty)
|
||||
and ("start_timestamp" in data.columns)
|
||||
and ("end_timestamp" in data.columns)
|
||||
):
|
||||
data = chunk_episodes(data)
|
||||
|
||||
return data
|
||||
|
||||
|
||||
# %% tags=[]
|
||||
time_segment = "daily"
|
||||
phone_data_yield_per_segment = filter_data_by_segment(phone_data_yield, time_segment)
|
||||
|
||||
# %%
|
||||
phone_data_yield.tail()
|
||||
|
||||
# %%
|
||||
phone_data_yield_per_segment.tail()
|
||||
|
||||
|
||||
# %%
|
||||
def getDataForPlot(phone_data_yield_per_segment):
|
||||
# calculate the length (in minute) of per segment instance
|
||||
phone_data_yield_per_segment["length"] = (
|
||||
phone_data_yield_per_segment["timestamps_segment"]
|
||||
.str.split(",")
|
||||
.apply(lambda x: int((int(x[1]) - int(x[0])) / (1000 * 60)))
|
||||
)
|
||||
# calculate the number of sensors logged at least one row of data per minute.
|
||||
phone_data_yield_per_segment = (
|
||||
phone_data_yield_per_segment.groupby(
|
||||
["local_segment", "length", "local_date", "local_hour", "local_minute"]
|
||||
)[["sensor", "local_date_time"]]
|
||||
.max()
|
||||
.reset_index()
|
||||
)
|
||||
# extract local start datetime of the segment from "local_segment" column
|
||||
phone_data_yield_per_segment["local_segment_start_datetimes"] = pd.to_datetime(
|
||||
phone_data_yield_per_segment["local_segment"].apply(
|
||||
lambda x: x.split("#")[1].split(",")[0]
|
||||
)
|
||||
)
|
||||
# calculate the number of minutes after local start datetime of the segment
|
||||
phone_data_yield_per_segment["minutes_after_segment_start"] = (
|
||||
(
|
||||
phone_data_yield_per_segment["local_date_time"]
|
||||
- phone_data_yield_per_segment["local_segment_start_datetimes"]
|
||||
)
|
||||
/ pd.Timedelta(minutes=1)
|
||||
).astype("int")
|
||||
|
||||
# impute missing rows with 0
|
||||
columns_for_full_index = phone_data_yield_per_segment[
|
||||
["local_segment_start_datetimes", "length"]
|
||||
].drop_duplicates(keep="first")
|
||||
columns_for_full_index = columns_for_full_index.apply(
|
||||
lambda row: [
|
||||
[row["local_segment_start_datetimes"], x] for x in range(row["length"] + 1)
|
||||
],
|
||||
axis=1,
|
||||
)
|
||||
full_index = []
|
||||
for columns in columns_for_full_index:
|
||||
full_index = full_index + columns
|
||||
full_index = pd.MultiIndex.from_tuples(
|
||||
full_index,
|
||||
names=("local_segment_start_datetimes", "minutes_after_segment_start"),
|
||||
)
|
||||
phone_data_yield_per_segment = (
|
||||
phone_data_yield_per_segment.set_index(
|
||||
["local_segment_start_datetimes", "minutes_after_segment_start"]
|
||||
)
|
||||
.reindex(full_index)
|
||||
.reset_index()
|
||||
.fillna(0)
|
||||
)
|
||||
|
||||
# transpose the dataframe per local start datetime of the segment and discard the useless index layer
|
||||
phone_data_yield_per_segment = phone_data_yield_per_segment.groupby(
|
||||
"local_segment_start_datetimes"
|
||||
)[["minutes_after_segment_start", "sensor"]].apply(
|
||||
lambda x: x.set_index("minutes_after_segment_start").transpose()
|
||||
)
|
||||
phone_data_yield_per_segment.index = phone_data_yield_per_segment.index.get_level_values(
|
||||
"local_segment_start_datetimes"
|
||||
)
|
||||
return phone_data_yield_per_segment
|
||||
|
||||
|
||||
# %%
|
||||
data_for_plot_per_segment = getDataForPlot(phone_data_yield_per_segment)
|
||||
|
||||
# %%
|
||||
# calculate the length (in minute) of per segment instance
|
||||
phone_data_yield_per_segment["length"] = (
|
||||
phone_data_yield_per_segment["timestamps_segment"]
|
||||
.str.split(",")
|
||||
.apply(lambda x: int((int(x[1]) - int(x[0])) / (1000 * 60)))
|
||||
)
|
||||
|
||||
# %%
|
||||
phone_data_yield_per_segment.tail()
|
||||
|
||||
# %%
|
||||
# calculate the number of sensors logged at least one row of data per minute.
|
||||
phone_data_yield_per_segment = (
|
||||
phone_data_yield_per_segment.groupby(
|
||||
["local_segment", "length", "local_date", "local_hour", "local_minute"]
|
||||
)[["sensor", "local_date_time"]]
|
||||
.max()
|
||||
.reset_index()
|
||||
)
|
||||
|
||||
# %%
|
||||
# extract local start datetime of the segment from "local_segment" column
|
||||
phone_data_yield_per_segment["local_segment_start_datetimes"] = pd.to_datetime(
|
||||
phone_data_yield_per_segment["local_segment"].apply(
|
||||
lambda x: x.split("#")[1].split(",")[0]
|
||||
)
|
||||
)
|
||||
|
||||
# %%
|
||||
# calculate the number of minutes after local start datetime of the segment
|
||||
phone_data_yield_per_segment["minutes_after_segment_start"] = (
|
||||
(
|
||||
phone_data_yield_per_segment["local_date_time"]
|
||||
- phone_data_yield_per_segment["local_segment_start_datetimes"]
|
||||
)
|
||||
/ pd.Timedelta(minutes=1)
|
||||
).astype("int")
|
||||
|
||||
# %%
|
||||
columns_for_full_index = phone_data_yield_per_segment[
|
||||
["local_segment_start_datetimes", "length"]
|
||||
].drop_duplicates(keep="first")
|
||||
columns_for_full_index = columns_for_full_index.apply(
|
||||
lambda row: [
|
||||
[row["local_segment_start_datetimes"], x] for x in range(row["length"] + 1)
|
||||
],
|
||||
axis=1,
|
||||
)
|
||||
|
||||
# %%
|
||||
full_index = []
|
||||
for columns in columns_for_full_index:
|
||||
full_index = full_index + columns
|
||||
full_index = pd.MultiIndex.from_tuples(
|
||||
full_index, names=("local_segment_start_datetimes", "minutes_after_segment_start")
|
||||
)
|
||||
|
||||
# %%
|
||||
phone_data_yield_per_segment.tail()
|
||||
|
||||
# %% [markdown]
|
||||
# # A workaround
|
||||
|
||||
# %%
|
||||
phone_data_yield_per_segment["local_segment_start_datetimes", "minutes_after_segment_start"] = phone_data_yield_per_segment[
|
||||
["local_segment_start_datetimes", "minutes_after_segment_start"]
|
||||
].drop_duplicates(keep="first")
|
||||
|
||||
# %%
|
||||
phone_data_yield_per_segment.set_index(
|
||||
["local_segment_start_datetimes", "minutes_after_segment_start"],
|
||||
verify_integrity=True,
|
||||
).reindex(full_index)
|
||||
|
||||
# %%
|
||||
phone_data_yield_per_segment.head()
|
||||
|
||||
|
||||
# %% [markdown]
|
||||
# # Retry
|
||||
|
||||
# %%
|
||||
def getDataForPlot(phone_data_yield_per_segment):
|
||||
# calculate the length (in minute) of per segment instance
|
||||
phone_data_yield_per_segment["length"] = (
|
||||
phone_data_yield_per_segment["timestamps_segment"]
|
||||
.str.split(",")
|
||||
.apply(lambda x: int((int(x[1]) - int(x[0])) / (1000 * 60)))
|
||||
)
|
||||
# calculate the number of sensors logged at least one row of data per minute.
|
||||
phone_data_yield_per_segment = (
|
||||
phone_data_yield_per_segment.groupby(
|
||||
["local_segment", "length", "local_date", "local_hour", "local_minute"]
|
||||
)[["sensor", "local_date_time"]]
|
||||
.max()
|
||||
.reset_index()
|
||||
)
|
||||
# extract local start datetime of the segment from "local_segment" column
|
||||
phone_data_yield_per_segment["local_segment_start_datetimes"] = pd.to_datetime(
|
||||
phone_data_yield_per_segment["local_segment"].apply(
|
||||
lambda x: x.split("#")[1].split(",")[0]
|
||||
)
|
||||
)
|
||||
# calculate the number of minutes after local start datetime of the segment
|
||||
phone_data_yield_per_segment["minutes_after_segment_start"] = (
|
||||
(
|
||||
phone_data_yield_per_segment["local_date_time"]
|
||||
- phone_data_yield_per_segment["local_segment_start_datetimes"]
|
||||
)
|
||||
/ pd.Timedelta(minutes=1)
|
||||
).astype("int")
|
||||
|
||||
# impute missing rows with 0
|
||||
columns_for_full_index = phone_data_yield_per_segment[
|
||||
["local_segment_start_datetimes", "length"]
|
||||
].drop_duplicates(keep="first")
|
||||
columns_for_full_index = columns_for_full_index.apply(
|
||||
lambda row: [
|
||||
[row["local_segment_start_datetimes"], x] for x in range(row["length"] + 1)
|
||||
],
|
||||
axis=1,
|
||||
)
|
||||
full_index = []
|
||||
for columns in columns_for_full_index:
|
||||
full_index = full_index + columns
|
||||
full_index = pd.MultiIndex.from_tuples(
|
||||
full_index,
|
||||
names=("local_segment_start_datetimes", "minutes_after_segment_start"),
|
||||
)
|
||||
phone_data_yield_per_segment = phone_data_yield_per_segment.drop_duplicates(subset=["local_segment_start_datetimes", "minutes_after_segment_start"],keep="first")
|
||||
phone_data_yield_per_segment = (
|
||||
phone_data_yield_per_segment.set_index(
|
||||
["local_segment_start_datetimes", "minutes_after_segment_start"]
|
||||
)
|
||||
.reindex(full_index)
|
||||
.reset_index()
|
||||
.fillna(0)
|
||||
)
|
||||
|
||||
# transpose the dataframe per local start datetime of the segment and discard the useless index layer
|
||||
phone_data_yield_per_segment = phone_data_yield_per_segment.groupby(
|
||||
"local_segment_start_datetimes"
|
||||
)[["minutes_after_segment_start", "sensor"]].apply(
|
||||
lambda x: x.set_index("minutes_after_segment_start").transpose()
|
||||
)
|
||||
phone_data_yield_per_segment.index = phone_data_yield_per_segment.index.get_level_values(
|
||||
"local_segment_start_datetimes"
|
||||
)
|
||||
return phone_data_yield_per_segment
|
||||
|
||||
|
||||
# %%
|
||||
phone_data_yield_per_segment = filter_data_by_segment(phone_data_yield, time_segment)
|
||||
|
||||
# %%
|
||||
data_for_plot_per_segment = getDataForPlot(phone_data_yield_per_segment)
|
||||
|
||||
# %%
|
||||
|
|
@ -46,7 +46,7 @@ from features import esm, helper, proximity
|
|||
# %% [markdown] tags=[]
|
||||
# # 1. Get the relevant data
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
# %%
|
||||
participants_inactive_usernames = participants.query_db.get_usernames(
|
||||
collection_start=datetime.date.fromisoformat("2020-08-01")
|
||||
)
|
||||
|
|
@ -56,11 +56,11 @@ ptcp_2 = participants_inactive_usernames[0:2]
|
|||
# %% [markdown] jp-MarkdownHeadingCollapsed=true tags=[]
|
||||
# ## 1.1 Labels
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
# %%
|
||||
df_esm = esm.get_esm_data(ptcp_2)
|
||||
df_esm_preprocessed = esm.preprocess_esm(df_esm)
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
# %%
|
||||
df_esm_PANAS = df_esm_preprocessed[
|
||||
(df_esm_preprocessed["questionnaire_id"] == 8)
|
||||
| (df_esm_preprocessed["questionnaire_id"] == 9)
|
||||
|
|
@ -70,7 +70,7 @@ df_esm_PANAS_clean = esm.clean_up_esm(df_esm_PANAS)
|
|||
# %% [markdown]
|
||||
# ## 1.2 Sensor data
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
# %%
|
||||
df_proximity = proximity.get_proximity_data(ptcp_2)
|
||||
df_proximity = helper.get_date_from_timestamp(df_proximity)
|
||||
df_proximity = proximity.recode_proximity(df_proximity)
|
||||
|
|
@ -81,7 +81,7 @@ df_proximity = proximity.recode_proximity(df_proximity)
|
|||
# %% [markdown]
|
||||
# # 2. Grouping/segmentation
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
# %%
|
||||
df_esm_PANAS_daily_means = (
|
||||
df_esm_PANAS_clean.groupby(["participant_id", "date_lj", "questionnaire_id"])
|
||||
.esm_user_answer_numeric.agg("mean")
|
||||
|
|
@ -89,7 +89,7 @@ df_esm_PANAS_daily_means = (
|
|||
.rename(columns={"esm_user_answer_numeric": "esm_numeric_mean"})
|
||||
)
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
# %%
|
||||
df_esm_PANAS_daily_means = (
|
||||
df_esm_PANAS_daily_means.pivot(
|
||||
index=["participant_id", "date_lj"],
|
||||
|
|
@ -102,16 +102,16 @@ df_esm_PANAS_daily_means = (
|
|||
)
|
||||
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
# %%
|
||||
df_proximity_daily_counts = proximity.count_proximity(df_proximity, ["date_lj"])
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
# %%
|
||||
df_proximity_daily_counts
|
||||
|
||||
# %% [markdown]
|
||||
# # 3. Join features (and export to csv?)
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
# %%
|
||||
df_full_data_daily_means = df_esm_PANAS_daily_means.join(
|
||||
df_proximity_daily_counts
|
||||
).reset_index()
|
||||
|
|
@ -119,13 +119,13 @@ df_full_data_daily_means = df_esm_PANAS_daily_means.join(
|
|||
# %% [markdown]
|
||||
# # 4. Machine learning model and parameters
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
# %%
|
||||
lin_reg_proximity = linear_model.LinearRegression()
|
||||
|
||||
# %% [markdown]
|
||||
# ## 4.1 Validation method
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
# %%
|
||||
logo = LeaveOneGroupOut()
|
||||
logo.get_n_splits(
|
||||
df_full_data_daily_means[["freq_prox_near", "prop_prox_near"]],
|
||||
|
|
@ -136,7 +136,7 @@ logo.get_n_splits(
|
|||
# %% [markdown]
|
||||
# ## 4.2 Fit results (export?)
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
# %%
|
||||
cross_val_score(
|
||||
lin_reg_proximity,
|
||||
df_full_data_daily_means[["freq_prox_near", "prop_prox_near"]],
|
||||
|
|
@ -147,7 +147,7 @@ cross_val_score(
|
|||
scoring="r2",
|
||||
)
|
||||
|
||||
# %% jupyter={"source_hidden": true}
|
||||
# %%
|
||||
lin_reg_proximity.fit(
|
||||
df_full_data_daily_means[["freq_prox_near", "prop_prox_near"]],
|
||||
df_full_data_daily_means["PA"],
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@
|
|||
# extension: .py
|
||||
# format_name: percent
|
||||
# format_version: '1.3'
|
||||
# jupytext_version: 1.11.4
|
||||
# jupytext_version: 1.13.0
|
||||
# kernelspec:
|
||||
# display_name: straw2analysis
|
||||
# language: python
|
||||
|
|
@ -74,3 +74,29 @@ rows_os_manufacturer = df_category_not_found["package_name"].str.contains(
|
|||
# %%
|
||||
with pd.option_context("display.max_rows", None, "display.max_columns", None):
|
||||
display(df_category_not_found.loc[~rows_os_manufacturer])
|
||||
|
||||
# %% [markdown]
|
||||
# # Export categories
|
||||
|
||||
# %% [markdown]
|
||||
# Rename all of "not_found" to "system" or "other".
|
||||
|
||||
# %%
|
||||
df_app_categories_to_export = df_app_categories.copy()
|
||||
rows_os_manufacturer_full = (df_app_categories_to_export["package_name"].str.contains(
|
||||
"|".join(manufacturers + custom_rom + other), case=False
|
||||
)) & (df_app_categories_to_export["play_store_genre"] == "not_found")
|
||||
df_app_categories_to_export.loc[rows_os_manufacturer_full, "play_store_genre"] = "System"
|
||||
|
||||
# %%
|
||||
rows_not_found = (df_app_categories_to_export["play_store_genre"] == "not_found")
|
||||
df_app_categories_to_export.loc[rows_not_found, "play_store_genre"] = "Other"
|
||||
|
||||
# %%
|
||||
df_app_categories_to_export["play_store_genre"].value_counts()
|
||||
|
||||
# %%
|
||||
df_app_categories_to_export.rename(columns={"play_store_genre": "genre"},inplace=True)
|
||||
df_app_categories_to_export.to_csv("../data/app_categories.csv", columns=["package_hash","genre"],index=False)
|
||||
|
||||
# %%
|
||||
|
|
|
|||
|
|
@ -26,10 +26,11 @@ import pandas as pd
|
|||
import seaborn as sns
|
||||
import yaml
|
||||
from pyprojroot import here
|
||||
from sklearn import linear_model, svm, kernel_ridge, gaussian_process
|
||||
from sklearn 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:
|
||||
|
|
@ -269,3 +270,203 @@ np.median(
|
|||
)
|
||||
)
|
||||
# %%
|
||||
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
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,30 @@
|
|||
from collections.abc import Collection
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from config.models import Participant, Timezone
|
||||
from setup import db_engine, session
|
||||
|
||||
|
||||
def get_timezone_data(usernames: Collection) -> pd.DataFrame:
|
||||
"""
|
||||
Read the data from the proximity sensor table and return it in a dataframe.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
usernames: Collection
|
||||
A list of usernames to put into the WHERE condition.
|
||||
|
||||
Returns
|
||||
-------
|
||||
df_proximity: pd.DataFrame
|
||||
A dataframe of proximity data.
|
||||
"""
|
||||
query_timezone = (
|
||||
session.query(Timezone, Participant.username)
|
||||
.filter(Participant.id == Timezone.participant_id)
|
||||
.filter(Participant.username.in_(usernames))
|
||||
)
|
||||
with db_engine.connect() as connection:
|
||||
df_timezone = pd.read_sql(query_timezone.statement, connection)
|
||||
return df_timezone
|
||||
|
|
@ -0,0 +1,205 @@
|
|||
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
|
||||
<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
|
||||
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<text text-anchor="start" x="139" y="-197.8" font-family="sans" font-size="10.00"> </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="251" y="-157.8" font-family="sans" font-size="10.00"> </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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<polygon fill="#57d99e" stroke="#57d99e" points="71,-212 71,-212 336,-212 336,-212 71,-212"/>
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<polygon fill="#57d99e" stroke="#57d99e" points="71,-172 71,-172 336,-172 336,-172 71,-172"/>
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<polygon fill="none" stroke="#57d99e" stroke-width="2" points="71,-129 71,-241 335,-241 335,-129 71,-129"/>
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</g>
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<!-- 1->0 -->
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<g id="edge1" class="edge">
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<title>1->0</title>
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<path fill="none" stroke="grey" stroke-width="2" d="M203,-127.88C203,-119.48 203,-110.81 203,-102.42"/>
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<polygon fill="grey" stroke="grey" stroke-width="2" points="206.5,-102.36 203,-92.36 199.5,-102.36 206.5,-102.36"/>
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</g>
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<!-- 2 -->
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<g id="node3" class="node">
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<title>2</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="7" y="-346" font-family="sans" font-size="10.00">  </text>
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<text text-anchor="start" x="321" y="-325.8" font-family="sans" font-size="10.00"> </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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<text text-anchor="start" x="7" y="-288" font-family="monospace" font-size="10.00">data/external/timezone.csv</text>
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<polygon fill="#86d957" stroke="#86d957" points="1,-358 1,-358 406,-358 406,-358 1,-358"/>
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<polygon fill="#86d957" stroke="#86d957" points="1,-340 1,-340 406,-340 406,-340 1,-340"/>
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<polygon fill="none" stroke="#86d957" stroke-width="2" points="1,-279 1,-387 405,-387 405,-279 1,-279"/>
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</g>
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<!-- 2->1 -->
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<g id="edge2" class="edge">
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<title>2->1</title>
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<path fill="none" stroke="grey" stroke-width="2" d="M203,-277.63C203,-269.45 203,-260.93 203,-252.53"/>
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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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</g>
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</g>
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</svg>
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|
After Width: | Height: | Size: 4.6 KiB |
|
|
@ -0,0 +1,69 @@
|
|||
import datetime
|
||||
import os
|
||||
import sys
|
||||
|
||||
nb_dir = os.path.split(os.getcwd())[0]
|
||||
if nb_dir not in sys.path:
|
||||
sys.path.append(nb_dir)
|
||||
|
||||
import pandas as pd
|
||||
from features.timezone import get_timezone_data
|
||||
from pyprojroot import here
|
||||
|
||||
import participants.query_db
|
||||
|
||||
participants_inactive_usernames = participants.query_db.get_usernames(
|
||||
tester=False, # True participants are wanted.
|
||||
active=False, # They have all finished their participation.
|
||||
collection_start=datetime.date.fromisoformat(
|
||||
"2020-08-01"
|
||||
), # This is the timeframe of the main study.
|
||||
last_upload=datetime.date.fromisoformat("2021-09-01"),
|
||||
)
|
||||
|
||||
participants_overview_si = pd.read_csv(
|
||||
snakemake.params["baseline_folder"] + "Participants_overview_Slovenia.csv", sep=";"
|
||||
)
|
||||
participants_overview_be = pd.read_csv(
|
||||
snakemake.params["baseline_folder"]+ "Participants_overview_Belgium.csv", sep=";"
|
||||
)
|
||||
|
||||
participants_true_si = participants_overview_si[
|
||||
participants_overview_si["Wristband_SerialNo"] != "DECLINED"
|
||||
]
|
||||
participants_true_be = participants_overview_be[
|
||||
participants_overview_be["SmartphoneBrand+Generation"].str.slice(0, 3) != "Not"
|
||||
]
|
||||
|
||||
# Concatenate participants from both countries.
|
||||
participants_usernames_empatica = pd.concat(
|
||||
[participants_true_be, participants_true_si]
|
||||
)
|
||||
# Filter only the participants from the main study (queried from the database).
|
||||
participants_usernames_empatica = participants_usernames_empatica[
|
||||
participants_usernames_empatica["Username"].isin(participants_inactive_usernames)
|
||||
]
|
||||
# Rename and select columns.
|
||||
participants_usernames_empatica = participants_usernames_empatica.rename(
|
||||
columns={"Username": "label", "Wristband_SerialNo": "empatica_id"}
|
||||
)[["label", "empatica_id"]]
|
||||
# Adapt for csv export.
|
||||
participants_usernames_empatica["empatica_id"] = participants_usernames_empatica[
|
||||
"empatica_id"
|
||||
].str.replace(",", ";")
|
||||
|
||||
participants_usernames_empatica.to_csv(
|
||||
snakemake.output["usernames_file"],
|
||||
header=True,
|
||||
index=False,
|
||||
line_terminator="\n",
|
||||
)
|
||||
|
||||
timezone_df = get_timezone_data(participants_inactive_usernames)
|
||||
|
||||
timezone_df.to_csv(
|
||||
snakemake.output["timezone_file"],
|
||||
header=True,
|
||||
index=False,
|
||||
line_terminator="\n",
|
||||
)
|
||||
2
rapids
2
rapids
|
|
@ -1 +1 @@
|
|||
Subproject commit a620def209e9f043f852097d0318cb45bde74467
|
||||
Subproject commit f78aa3e7b3567423b44045766b230cd60d557cb0
|
||||
Loading…
Reference in New Issue