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stress_at_work_analysis
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junos:master
junos:ml_pipeline
junos:screen_sequences
junos:rapids
junos:ambient
junos:communication
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compare: junos:de10269d36b621bcbeeafcefa2cc0cb2e1a10b06
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junos:master
junos:ml_pipeline
junos:screen_sequences
junos:rapids
junos:ambient
junos:communication

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1
.gitignore vendored
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@ -6,4 +6,3 @@ __pycache__/
/config/*.ipynb
/statistical_analysis/*.ipynb
/machine_learning/intermediate_results/
/data/features/

18
README.md
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@ -142,20 +142,4 @@ 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"))'
```
See [the installation issue above](#Timezone-environment-variable-for-tidyverse-(relevant-for-WSL2)).

197
exploration/ex_ml_pipeline.py
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@ -6,7 +6,7 @@
# extension: .py
# format_name: percent
# format_version: '1.3'
# jupytext_version: 1.13.0
# jupytext_version: 1.12.0
# kernelspec:
# display_name: straw2analysis
# language: python
@ -21,15 +21,11 @@ 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
from sklearn.model_selection import LeaveOneGroupOut, cross_val_score
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.impute import SimpleImputer
nb_dir = os.path.split(os.getcwd())[0]
if nb_dir not in sys.path:
@ -261,195 +257,4 @@ model_validation.cross_validate()
# %%
model_validation.groups
# %% [markdown]
# # Use RAPIDS
# %%
with open(here("rapids/config.yaml"), "r") as file:
rapids_config = yaml.safe_load(file)
# %%
for key in rapids_config.keys():
if isinstance(rapids_config[key], dict): # Remove top-level configs
if ("PROVIDERS" in rapids_config[key]): # Retain features (that have providers)
if rapids_config[key]["PROVIDERS"]: # Remove non-implemented features
for provider in rapids_config[key]["PROVIDERS"]:
if rapids_config[key]["PROVIDERS"][provider]["COMPUTE"]: # Check that the features were actually calculated
if "FEATURES" in rapids_config[key]["PROVIDERS"][provider]:
print(key)
print(provider)
print(rapids_config[key]["PROVIDERS"][provider]["FEATURES"])
# %%
features_rapids = pd.read_csv(here("rapids/data/processed/features/all_participants/all_sensor_features.csv"), parse_dates=["local_segment_start_datetime", "local_segment_end_datetime"])
# %%
features_rapids.columns
# %%
features_rapids = features_rapids.assign(date_lj=lambda x: x.local_segment_start_datetime.dt.date)
# %%
features_rapids["participant_id"] = features_rapids["pid"].str.extract("(\d+)")
features_rapids["participant_id"] = pd.to_numeric(features_rapids["participant_id"])
features_rapids.set_index(["participant_id", "date_lj"], inplace=True)
# %%
with open("../machine_learning/config/minimal_labels.yaml", "r") as file:
labels_params = yaml.safe_load(file)
# %%
labels = machine_learning.labels.Labels(**labels_params)
labels.set_participants_label("all")
# %%
labels.aggregate_labels(cached=True)
labels_read = labels.get_aggregated_labels()
labels_read = labels_read.reset_index()
labels_read["date_lj"] = labels_read["date_lj"].dt.date
labels_read.set_index(["participant_id", "date_lj"], inplace=True)
# date_lj column is parsed as a date and represented as Timestamp, when read from csv.
# When calculated, it is represented as date.
# %%
features_rapids.shape
# %%
labels_read.shape
# %%
features_labels = features_rapids.join(labels_read, how="inner").reset_index()
# %%
features_labels.shape
# %%
features_labels.columns
# %%
imputer = SimpleImputer(missing_values=np.nan, strategy='mean')
# %%
feature_columns = features_labels.columns[6:-3]
label_column = "NA"
group_column = "pid"
# %%
lin_reg_rapids = linear_model.LinearRegression()
logo = LeaveOneGroupOut()
logo.get_n_splits(
features_labels[feature_columns],
features_labels[label_column],
groups=features_labels[group_column],
)
# %%
cross_val_score(
lin_reg_rapids,
X=imputer.fit_transform(features_labels[feature_columns]),
y=features_labels[label_column],
groups=features_labels[group_column],
cv=logo,
n_jobs=-1,
scoring="r2",
)
# %%
sns.set(rc={"figure.figsize":(16, 8)})
sns.heatmap(features_labels[feature_columns].isna(), cbar=False)
# %% [markdown] tags=[]
# ```yaml
# ALL_CLEANING_INDIVIDUAL:
# PROVIDERS:
# RAPIDS:
# COMPUTE: True
# IMPUTE_SELECTED_EVENT_FEATURES: # Fill NAs with 0 only for event-based features, see table below
# COMPUTE: True
# MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33 # Any feature value in a time segment instance with phone data yield > [MIN_DATA_YIELDED_MINUTES_TO_IMPUTE] will be replaced with a zero.
# COLS_NAN_THRESHOLD: 0.3 # Discard columns with missing value ratios higher than [COLS_NAN_THRESHOLD]. Set to 1 to disable
# COLS_VAR_THRESHOLD: True # Set to True to discard columns with zero variance
# ROWS_NAN_THRESHOLD: 1 # Discard rows with missing value ratios higher than [ROWS_NAN_THRESHOLD]. Set to 1 to disable
# DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
# DATA_YIELD_RATIO_THRESHOLD: 0.3 # Discard rows with ratiovalidyieldedhours or ratiovalidyieldedminutes feature less than [DATA_YIELD_RATIO_THRESHOLD]. The feature name is determined by [DATA_YIELD_FEATURE] parameter. Set to 0 to disable
# DROP_HIGHLY_CORRELATED_FEATURES:
# COMPUTE: False
# MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
# CORR_THRESHOLD: 0.95
# SRC_SCRIPT: src/features/all_cleaning_individual/rapids/main.R
# ```
# %%
features_rapids_cleaned = pd.read_csv(here("rapids/data/processed/features/all_participants/all_sensor_features_cleaned_rapids.csv"), parse_dates=["local_segment_start_datetime", "local_segment_end_datetime"])
features_rapids_cleaned = features_rapids_cleaned.assign(date_lj=lambda x: x.local_segment_start_datetime.dt.date)
features_rapids_cleaned["participant_id"] = features_rapids_cleaned["pid"].str.extract("(\d+)")
features_rapids_cleaned["participant_id"] = pd.to_numeric(features_rapids_cleaned["participant_id"])
features_rapids_cleaned.set_index(["participant_id", "date_lj"], inplace=True)
# %%
features_cleaned_labels = features_rapids_cleaned.join(labels_read, how="inner").reset_index()
feature_clean_columns = features_cleaned_labels.columns[6:-3]
# %%
print(feature_columns.shape)
print(feature_clean_columns.shape)
# %%
sns.set(rc={"figure.figsize":(16, 8)})
sns.heatmap(features_cleaned_labels[feature_clean_columns].isna(), cbar=False)
# %%
lin_reg_rapids_clean = linear_model.LinearRegression()
logo = LeaveOneGroupOut()
logo.get_n_splits(
features_cleaned_labels[feature_clean_columns],
features_cleaned_labels[label_column],
groups=features_cleaned_labels[group_column],
)
# %%
features_clean_imputed = imputer.fit_transform(features_cleaned_labels[feature_clean_columns])
# %%
cross_val_score(
lin_reg_rapids_clean,
X=features_clean_imputed,
y=features_cleaned_labels[label_column],
groups=features_cleaned_labels[group_column],
cv=logo,
n_jobs=-1,
scoring="r2",
)
# %%
lin_reg_full = linear_model.LinearRegression()
lin_reg_full.fit(features_clean_imputed,features_cleaned_labels[label_column])
# %%
NA_pred = lin_reg_full.predict(features_clean_imputed)
# %%
# The coefficients
print("Coefficients: \n", lin_reg_full.coef_)
# The mean squared error
print("Mean squared error: %.2f" % mean_squared_error(features_cleaned_labels[label_column], NA_pred))
# The coefficient of determination: 1 is perfect prediction
print("Coefficient of determination: %.2f" % r2_score(features_cleaned_labels[label_column], NA_pred))
# %%
feature_clean_columns[np.argmax(lin_reg_full.coef_)]
# %% [markdown]
# Ratio between stationary time and total location sensed time. A lat/long coordinate pair is labeled as stationary if its speed (distance/time) to the next coordinate pair is less than 1km/hr. A higher value represents a more stationary routine.
# %%
plt.scatter(features_clean_imputed[:,np.argmax(lin_reg_full.coef_)], features_cleaned_labels[label_column], color="black")
plt.scatter(features_clean_imputed[:,np.argmax(lin_reg_full.coef_)], NA_pred, color="red", linewidth=3)
plt.xticks()
plt.yticks()
fig = plt.gcf()
fig.set_size_inches(18.5, 10.5)
plt.show()

2
rapids

@ -1 +1 @@
Subproject commit bf9c764c97f076f4af288f7afa1a32931996b2db
Subproject commit e5cc02501f629c96641dfd1bcd1f7fcfd0d55462