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Commit d70c8f97 authored by Tomas Martinovic's avatar Tomas Martinovic
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make Rmd more portable

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FROM rocker/r-base:4.3.3
RUN apt-get update -y && apt-get install -y make pandoc zlib1g-dev libcurl4-openssl-dev libssl-dev libicu-dev libpng-dev python3 git libffi-dev && rm -rf /var/lib/apt/lists/*
RUN mkdir -p /usr/local/lib/R/etc/ /usr/lib/R/etc/
RUN echo "options(renv.config.pak.enabled = FALSE, repos = c(CRAN = 'https://cran.rstudio.com/'), download.file.method = 'libcurl', Ncpus = 4)" | tee /usr/local/lib/R/etc/Rprofile.site | tee /usr/lib/R/etc/Rprofile.site
RUN R -e 'install.packages("remotes")'
RUN R -e 'remotes::install_version("renv", version = "1.0.7")'
COPY renv.lock renv.lock
RUN R -e 'renv::restore()'
RUN R -e 'reticulate::install_python()'
COPY renv/requirements.txt requirements.txt
RUN R -e 'reticulate::virtualenv_create("hmch")'
RUN R -e 'reticulate::virtualenv_install("hmch", c("-r", "requirements.txt"))'
COPY get_features.Rmd get_features.Rmd
COPY get_results.Rmd get_results.Rmd
COPY functions functions
CMD ["R", "--quiet", "-e", "rmarkdown::render('get_features.Rmd')"]
functions/functions_df_total_cv.R
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...@@ -16,13 +16,8 @@ get_train_df <- ...@@ -16,13 +16,8 @@ get_train_df <-
) )
} }
#train_df <- get_train_df(df_features = df_autocorrelation_features) #train_df <- get_train_df(df_features = df_autocorrelation_features)
# Get features from XGBOOST---- # Get features from XGBOOST----
get_features_tree_xgboost <- get_features_tree_xgboost <-
function(train_df = train_df, function(train_df = train_df,
...@@ -55,7 +50,7 @@ get_features_tree_xgboost <- ...@@ -55,7 +50,7 @@ get_features_tree_xgboost <-
xgboost_iter = 100, xgboost_iter = 100,
set_seed = 4) { set_seed = 4) {
train_df$full_data_big_648obs <- train_df$full_data_big_648obs <-
add_column( tibble::add_column(
train_df$full_data_big_648obs, train_df$full_data_big_648obs,
activ_num = rep(1:8, 81), activ_num = rep(1:8, 81),
.before = "activity" .before = "activity"
...@@ -125,13 +120,13 @@ get_features_tree_xgboost <- ...@@ -125,13 +120,13 @@ get_features_tree_xgboost <-
dplyr::slice_head(n = xgboost_n_features) dplyr::slice_head(n = xgboost_n_features)
xgboost_important_features <- xgboost_important_features <-
xgboost_important_features |> select_if(~ !any(is.na(.))) xgboost_important_features |> dplyr::select_if(~ !any(is.na(.)))
# Select features # Select features
all_xgboost_activ_more_features <- all_xgboost_activ_more_features <-
train_df$train_data_big_648obs |> train_df$train_data_big_648obs |>
as.data.frame() |> as.data.frame() |>
dplyr::select(all_of(xgboost_important_features$Feature)) |> dplyr::select(tidyselect::all_of(xgboost_important_features$Feature)) |>
mutate( dplyr::mutate(
student = train_df$full_data_big_648obs$student, student = train_df$full_data_big_648obs$student,
activity = train_df$full_data_big_648obs$activity activity = train_df$full_data_big_648obs$activity
) )
...@@ -508,9 +503,7 @@ train_som_cv <- function(train_df, ...@@ -508,9 +503,7 @@ train_som_cv <- function(train_df,
temp_list_kfold$som_accur_kfold_test[[4]]$mnLogLoss temp_list_kfold$som_accur_kfold_test[[4]]$mnLogLoss
) )
temp_df_1 <- temp_df_1 <-
tibble::tibble( tibble::tibble(
fold_1 = c( fold_1 = c(
...@@ -683,8 +676,6 @@ get_all <- ...@@ -683,8 +676,6 @@ get_all <-
get_features_tree_xgboost( get_features_tree_xgboost(
train_df = train_df, train_df = train_df,
param_xgb_model = param_xgb_model, param_xgb_model = param_xgb_model,
nfold = nfold, nfold = nfold,
showsd = showsd, showsd = showsd,
stratified = stratified, stratified = stratified,
...@@ -1017,6 +1008,7 @@ train_test_models <- ...@@ -1017,6 +1008,7 @@ train_test_models <-
set_seed = 1245) { set_seed = 1245) {
# Temp for SOM models # Temp for SOM models
temp_tests_feat <- list() temp_tests_feat <- list()
index <- 1
# For loop for train # For loop for train
for (xgboost_n_features in xgb_features_range) { for (xgboost_n_features in xgb_features_range) {
all_som <- get_all( all_som <- get_all(
...@@ -1069,7 +1061,8 @@ train_test_models <- ...@@ -1069,7 +1061,8 @@ train_test_models <-
paste0(all_som$quality$`648obs`$`Features set`, paste0(all_som$quality$`648obs`$`Features set`,
paste0("_", xgboost_n_features, "f")) paste0("_", xgboost_n_features, "f"))
temp_tests_feat[[xgboost_n_features]] <- all_som temp_tests_feat[[index]] <- all_som
index <- index + 1
} }
...@@ -1125,7 +1118,7 @@ get_table_superclass <- ...@@ -1125,7 +1118,7 @@ get_table_superclass <-
col_totals <- base::colSums(table_som_clasif[3:10]) col_totals <- base::colSums(table_som_clasif[3:10])
# Calculate percentage contributions and append to original values # Calculate percentage contributions and append to original values
table_som_clasif <- table_som_clasif %>% table_som_clasif <- table_som_clasif |>
dplyr::group_by(superclass) |> dplyr::group_by(superclass) |>
dplyr::mutate(across(2:9, dplyr::mutate(across(2:9,
~ base::paste0(.x, " (", base::round((.x / col_totals[dplyr::cur_column()] * 100), 2 ~ base::paste0(.x, " (", base::round((.x / col_totals[dplyr::cur_column()] * 100), 2
...@@ -1135,12 +1128,12 @@ get_table_superclass <- ...@@ -1135,12 +1128,12 @@ get_table_superclass <-
# Make final table # Make final table
df_summary <- table_som_clasif %>% df_summary <- table_som_clasif |>
dplyr::group_by(superclass) %>% dplyr::group_by(superclass) |>
dplyr::summarise(dplyr::across(2:9, sum)) %>% dplyr::summarise(dplyr::across(2:9, sum)) |>
dplyr::ungroup() %>% dplyr::ungroup() |>
dplyr::mutate(total = base::rowSums(dplyr::across(2:9))) %>% dplyr::mutate(total = base::rowSums(dplyr::across(2:9))) |>
dplyr::rowwise() %>% dplyr::rowwise() |>
dplyr::mutate(dplyr::across(2:9, dplyr::mutate(dplyr::across(2:9,
~ base::paste0( ~ base::paste0(
.x, " (", base::round((.x / col_totals[dplyr::cur_column()] * 100), round_value), "%)" .x, " (", base::round((.x / col_totals[dplyr::cur_column()] * 100), round_value), "%)"
...@@ -1149,7 +1142,7 @@ get_table_superclass <- ...@@ -1149,7 +1142,7 @@ get_table_superclass <-
dplyr::ungroup() dplyr::ungroup()
# Format the percentages as requested # Format the percentages as requested
df_summary <- df_summary %>% df_summary <- df_summary |>
dplyr::mutate(dplyr::across(starts_with("perc"), ~ base::paste0(.x), .names = "formatted_{.col}")) dplyr::mutate(dplyr::across(starts_with("perc"), ~ base::paste0(.x), .names = "formatted_{.col}"))
names(df_summary) <- names(df_summary) <-
......
get_features.Rmd
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...@@ -10,6 +10,15 @@ knitr::opts_chunk$set(warning = FALSE, message = FALSE) ...@@ -10,6 +10,15 @@ knitr::opts_chunk$set(warning = FALSE, message = FALSE)
``` ```
```{r parameters} ```{r parameters}
# Set basepath for the script execution, default is the script path
base_path <- getwd()
# Data folder
if (Sys.getenv("DOCKER_RUN") == "true") {
zenodo_dir <- "/zenodo_dir"
} else {
zenodo_dir <- file.path(base_path, "data_zenodo")
}
# Chaos 01 parameters # Chaos 01 parameters
lmin_a <- 20 lmin_a <- 20
lmin_g <- 20 lmin_g <- 20
...@@ -25,9 +34,9 @@ renv::restore() ...@@ -25,9 +34,9 @@ renv::restore()
```{r install_python, include = FALSE} ```{r install_python, include = FALSE}
# Install python if not available # Install python if not available
if (!reticulate::py_available()) { #if (!reticulate::py_available()) {
reticulate::install_python() # reticulate::install_python()
} #}
# # Create `hmch` virtual environment if it does not exists yet # # Create `hmch` virtual environment if it does not exists yet
# if (!reticulate::virtualenv_exists("hmch")) { # if (!reticulate::virtualenv_exists("hmch")) {
# reticulate::virtualenv_create("hmch") # reticulate::virtualenv_create("hmch")
...@@ -40,13 +49,10 @@ if (!reticulate::py_available()) { ...@@ -40,13 +49,10 @@ if (!reticulate::py_available()) {
# reticulate::py_install("tsfresh") # reticulate::py_install("tsfresh")
# } # }
# Set basepath for the script execution, default is the script path
base_path <- getwd()
# Install required Python packages from requirements.txt # Install required Python packages from requirements.txt
reticulate::virtualenv_install("my-env_25", c("-r", file.path(base_path, "renv", "requirements.txt"))) #reticulate::virtualenv_install("hmch", c("-r", file.path(base_path, "renv", "requirements.txt")))
# Use `my-env_23` virtual environment # Use `hmch` virtual environment
reticulate::use_virtualenv("my-env_25") reticulate::use_virtualenv("hmch")
library(reticulate) library(reticulate)
``` ```
...@@ -71,7 +77,6 @@ import pandas ...@@ -71,7 +77,6 @@ import pandas
```{r prepare_input_data} ```{r prepare_input_data}
# Download data # Download data
zenodo_dir <- file.path(base_path, "data_zenodo")
#https://zenodo.org/records/10984138 #https://zenodo.org/records/10984138
if (!file.exists(file.path(zenodo_dir, "physical_exercise.csv"))) { if (!file.exists(file.path(zenodo_dir, "physical_exercise.csv"))) {
# Create folder data_zenodo if it does not exists # Create folder data_zenodo if it does not exists
...@@ -155,36 +160,36 @@ figure_1 ...@@ -155,36 +160,36 @@ figure_1
```{r compute_features} ```{r compute_features}
# Get neighborhood size for Accelerometer and Gyroscope # Get neighborhood size for Accelerometer and Gyroscope
eps_df_ALL <- get_eps(df_ALL_648obs, scale = eps_scale) eps_df_ALL <- get_eps(df_ALL_648obs, scale = eps_scale)
# # Get features for train models # # # Get features for train models
## Get RQA features + TREND # ## Get RQA features + TREND
df_ALL_rqa <- get_features_rqa_trend(df = df_ALL[1:10000, ], eps_chaos_01 = eps_df_ALL, df_ALL_rqa <- get_features_rqa_trend(df = df_ALL, eps_chaos_01 = eps_df_ALL,
lmin_a = lmin_a, lmin_g = lmin_g) lmin_a = lmin_a, lmin_g = lmin_g)
#save(df_ALL_rqa, file = "SOM_features/rqa_trend/df_ALL_rqa.RData") #save(df_ALL_rqa, file = "SOM_features/rqa_trend/df_ALL_rqa.RData")
# Autocorrelation # Autocorrelation
df_ALL_autocorrelation_features <- get_features_tsfresh_specific(df = df_ALL[1:10000, ], dic_features = py$autocorrelation_dic) df_ALL_autocorrelation_features <- get_features_tsfresh_specific(df = df_ALL, dic_features = py$autocorrelation_dic)
df_ALL_autocorrelation_features <- tidyr::drop_na(df_ALL_autocorrelation_features) df_ALL_autocorrelation_features <- tidyr::drop_na(df_ALL_autocorrelation_features)
#save(df_ALL_autocorrelation_features, file = "SOM_features/autocorr/df_ALL_autocorrelation_features.RData") save(df_ALL_autocorrelation_features, file = file.path(zenodo_dir, "df_ALL_autocorrelation_features.RData"))
# Spectral # Spectral
df_ALL_spectral_features <- get_features_tsfresh_specific(df = df_ALL[1:10000, ], dic_features = py$spectral_dic) df_ALL_spectral_features <- get_features_tsfresh_specific(df = df_ALL, dic_features = py$spectral_dic)
df_ALL_spectral_features <- tidyr::drop_na(df_ALL_spectral_features) df_ALL_spectral_features <- tidyr::drop_na(df_ALL_spectral_features)
#save(df_ALL_spectral_features, file = "SOM_features/spectral/df_ALL_spectral_features.RData") save(df_ALL_spectral_features, file = file.path(zenodo_dir, "df_ALL_spectral_features.RData"))
# MIX of RQA + Autocorrelation + Spectral # MIX of RQA + Autocorrelation + Spectral
#df_ALL_mix_features <- do.call("rbind", list(df_ALL_rqa, df_ALL_autocorrelation_features[3:141], df_ALL_spectral_features[3:2607])) df_ALL_mix_features <- do.call("rbind", list(df_ALL_rqa, df_ALL_autocorrelation_features[3:141], df_ALL_spectral_features[3:2607]))
#save(df_ALL_mix_features, file = "SOM_features/mix_rqa_trend_spectral_autocorr/df_ALL_mix_features.RData") save(df_ALL_mix_features, file = file.path(zenodo_dir, "df_ALL_mix_features.RData"))
# Tsfresh all # Tsfresh all
df_ALL_tsfresh_features <- get_features_tsfresh_specific(df = df_ALL[1:10000, ], dic_features = py$ComprehensiveFCParameters()) df_ALL_tsfresh_features <- get_features_tsfresh_specific(df = df_ALL, dic_features = py$ComprehensiveFCParameters())
# Drop NAs # Drop NAs
df_ALL_tsfresh_features <- df_ALL_tsfresh_features |> df_ALL_tsfresh_features <- df_ALL_tsfresh_features |>
dplyr::select_if(~ !any(is.na(.))) dplyr::select_if(~ !any(is.na(.)))
...@@ -194,6 +199,6 @@ df_ALL_tsfresh_features <- tidyr::drop_na(df_ALL_tsfresh_features) ...@@ -194,6 +199,6 @@ df_ALL_tsfresh_features <- tidyr::drop_na(df_ALL_tsfresh_features)
dir.create(file.path(base_path, "outputs")) dir.create(file.path(base_path, "outputs"))
} }
#save(df_ALL_tsfresh_features, save(df_ALL_tsfresh_features,
# file = file.path(base_path, "outputs/df_ALL_tsfresh_features.RData")) file = file.path(zenodo_dir, "df_ALL_tsfresh_features.RData"))
``` ```
get_results.Rmd
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...@@ -13,13 +13,43 @@ output: ...@@ -13,13 +13,43 @@ output:
knitr::opts_chunk$set(warning = FALSE, message = FALSE) knitr::opts_chunk$set(warning = FALSE, message = FALSE)
``` ```
```{r install_packages, include = FALSE}
# Install `renv` package if not available
if (!require("renv")) install.packages("renv")
# Install dependencies if not installed already
renv::restore()
```
```{r parameters} ```{r parameters}
# Set basepath for the script execution, default is the script path
base_path <- getwd()
# Input data path
# Data folder
if (Sys.getenv("DOCKER_RUN") == "true") {
zenodo_dir <- "/zenodo_dir"
} else {
zenodo_dir <- file.path(base_path, "data_zenodo")
}
# Create folder data_zenodo if it does not exists
if (!dir.exists(zenodo_dir)) {
dir.create(zenodo_dir)
}
# Output data path
output_dir <- file.path(base_path, "output")
# Create folder output if it does not exists
if (!dir.exists(output_dir)) {
dir.create(output_dir)
}
# Parameters for article "link to article" # Parameters for article "link to article"
# SOM dimentions # SOM dimentions
som_xdim <- c(5) som_xdim <- c(5)
som_ydim <- c(5) som_ydim <- c(5)
# Range of used features for SOM train e.g. 2:37, min number of features are 2 # Range of used features for SOM train e.g. 2:35, min number of features are 2
xgb_features_range <- c(27) xgb_features_range <- 24:27 # c(2:35)
best_n_features <- "27" # This needs to be character as it is used to subset by name
# XGBOOST importance type https://www.rdocumentation.org/packages/xgboost/versions/0.6.4.1/topics/xgb.importance # XGBOOST importance type https://www.rdocumentation.org/packages/xgboost/versions/0.6.4.1/topics/xgb.importance
xgboost_importance_type = c("Gain") xgboost_importance_type = c("Gain")
...@@ -32,7 +62,6 @@ activ_name_eng <- rep(c("1:4x10m Shuttle Run", ...@@ -32,7 +62,6 @@ activ_name_eng <- rep(c("1:4x10m Shuttle Run",
"6:10x Gymnastic Hoops Passing", "6:10x Gymnastic Hoops Passing",
"7:10x Bench Jumping", "7:10x Bench Jumping",
"8:Crawling repeat"), 81) "8:Crawling repeat"), 81)
stud_names_648 <- as.character(df_rqa_features$student)
# XGBOOST # XGBOOST
param_xgb_model = param_xgb_model =
...@@ -67,41 +96,58 @@ plot_pixel_size <- 500 ...@@ -67,41 +96,58 @@ plot_pixel_size <- 500
legendPos <- "below" legendPos <- "below"
``` ```
```{r install_packages, include = FALSE} ```{r prepare_input_data}
# Install `renv` package if not available
if (!require("renv")) install.packages("renv") #https://zenodo.org/records/10996083
# Install dependencies if not installed already if (!file.exists(file.path(zenodo_dir, "df_ALL_autocorrelation_features.csv")) |
renv::restore() !file.exists(file.path(zenodo_dir, "df_ALL_rqa.csv")) |
``` !file.exists(file.path(zenodo_dir, "df_ALL_spectral_features.csv")) |
!file.exists(file.path(zenodo_dir, "df_ALL_tsfresh_features.csv"))
) {
# This part is based on https://github.com/inbo/inborutils/blob/main/R/download_zenodo.R
record <-
curl::curl_fetch_memory("https://zenodo.org/api/records/10996083")$content |>
rawToChar() |>
jsonlite::fromJSON()
file_urls <- record$files$links$self
filenames <- basename(record$files$key)
destfiles <- file.path(zenodo_dir, filenames)
mapply(curl::curl_download,
file_urls,
destfiles,
MoreArgs = list(quiet = TRUE))
}
if (!file.exists(file.path(zenodo_dir, "physical_exercise.csv"))) {
# Create folder data_zenodo if it does not exists
if (!dir.exists(zenodo_dir)) {
dir.create(zenodo_dir)
}
# This part is based on https://github.com/inbo/inborutils/blob/main/R/download_zenodo.R
record <-
curl::curl_fetch_memory("https://zenodo.org/api/records/10984137")$content |>
rawToChar() |>
jsonlite::fromJSON()
file_urls <- record$files$links$self
filenames <- basename(record$files$key)
destfiles <- file.path(zenodo_dir, filenames)
mapply(curl::curl_download,
file_urls,
destfiles,
MoreArgs = list(quiet = TRUE))
}
# Load data (raw time series student movements)
df_motion <- readr::read_csv(file.path(zenodo_dir, "physical_exercise.csv"))
```{r, include = T}
# Load libraries
if (!require('tidyverse')) install.packages('tidyverse'); library('tidyverse')
if (!require('knitr')) install.packages('knitr'); library('knitr')
# SOM
if (!require('kohonen')) install.packages('kohonen'); library('kohonen')
if (!require('aweSOM')) install.packages('aweSOM'); library('aweSOM')
# XGBoost feature reduction
if (!require('xgboost')) install.packages('xgboost'); library('xgboost')
if (!require('caret')) install.packages('caret'); library('caret')
# DT
if (!require('DT')) install.packages('DT'); library('DT')
``` ```
```{r prepare_functions_and_load_data, include = T}
```{r, include = T}
# Include functions # Include functions
# Windows path source(file.path(base_path, "functions", "functions_df_total_cv.R"))
#source("D:/path_to/movement-classification/functions/functions_df_total.R")
# Linux path
#source("path_to/movement-classification/functions/functions_df_total.R")
#source("/home/andrii/Documents/it4i/movement-classification/functions/functions_df_total_cv.R")
source("D:/it4i/movement-classification/functions/functions_df_total_cv.R")
# Load data # Load data
#The Data can be found at https://zenodo.org/records/10996083 #The Data can be found at https://zenodo.org/records/10996083
...@@ -112,15 +158,13 @@ source("D:/it4i/movement-classification/functions/functions_df_total_cv.R") ...@@ -112,15 +158,13 @@ source("D:/it4i/movement-classification/functions/functions_df_total_cv.R")
#https://cran.r-project.org/web/packages/Chaos01/Chaos01.pdf #https://cran.r-project.org/web/packages/Chaos01/Chaos01.pdf
# RQA TREND # RQA TREND
#https://cran.r-project.org/web/packages/nonlinearTseries/nonlinearTseries.pdf #https://cran.r-project.org/web/packages/nonlinearTseries/nonlinearTseries.pdf
#df_rqa_features <- read_csv("/home/andrii/Documents/it4i/activity_df_pohybove_cinnosti_2024/data_zenodo/zenodo_uploaded/df_ALL_rqa.csv") df_rqa_features <- readr::read_csv(file.path(zenodo_dir, "df_ALL_rqa.csv"))
df_rqa_features <- readr::read_csv("C:/Users/Andrii/Downloads/10996083/df_ALL_rqa.csv")
# Autocorrelation # Autocorrelation
#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.autocorrelation #https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.autocorrelation
#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.agg_autocorrelation #https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.agg_autocorrelation
#df_autocorrelation_features <- read_csv("/home/andrii/Documents/it4i/activity_df_pohybove_cinnosti_2024/data_zenodo/zenodo_uploaded/df_ALL_autocorrelation_features.csv") #df_autocorrelation_features <- read_csv("/home/andrii/Documents/it4i/activity_df_pohybove_cinnosti_2024/data_zenodo/zenodo_uploaded/df_ALL_autocorrelation_features.csv")
df_autocorrelation_features <- readr::read_csv("C:/Users/Andrii/Downloads/10996083/df_ALL_autocorrelation_features.csv") df_autocorrelation_features <- readr::read_csv(file.path(zenodo_dir, "df_ALL_autocorrelation_features.csv"))
# Spectral # Spectral
#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.fft_aggregated #https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.fft_aggregated
...@@ -129,17 +173,14 @@ df_autocorrelation_features <- readr::read_csv("C:/Users/Andrii/Downloads/109960 ...@@ -129,17 +173,14 @@ df_autocorrelation_features <- readr::read_csv("C:/Users/Andrii/Downloads/109960
#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.fourier_entropy #https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.fourier_entropy
#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.spkt_welch_density #https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.spkt_welch_density
#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.ar_coefficient #https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html#tsfresh.feature_extraction.feature_calculators.ar_coefficient
#df_spectral_features <- read_csv("/home/andrii/Documents/it4i/activity_df_pohybove_cinnosti_2024/data_zenodo/zenodo_uploaded/df_ALL_spectral_features.csv") df_spectral_features <- readr::read_csv(file.path(zenodo_dir, "df_ALL_spectral_features.csv"))
df_spectral_features <- readr::read_csv("C:/Users/Andrii/Downloads/10996083/df_ALL_spectral_features.csv")
# Mix RQA/Spectral/Autocorr # Mix RQA/Spectral/Autocorr
#df_mix_features <- read_csv("/home/andrii/Documents/it4i/activity_df_pohybove_cinnosti_2024/data_zenodo/zenodo_uploaded/df_ALL_mix_features.csv") df_mix_features <- readr::read_csv(file.path(zenodo_dir, "df_ALL_mix_features.csv"))
df_mix_features <- readr::read_csv("C:/Users/Andrii/Downloads/10996083/df_ALL_mix_features.csv")
# Tsfresh all # Tsfresh all
#https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html #https://tsfresh.readthedocs.io/en/latest/api/tsfresh.feature_extraction.html
#df_tsfresh_features <- read_csv("/home/andrii/Documents/it4i/activity_df_pohybove_cinnosti_2024/data_zenodo/zenodo_uploaded/df_ALL_tsfresh_features.csv") df_tsfresh_features <- readr::read_csv(file.path(zenodo_dir, "df_ALL_tsfresh_features.csv"))
df_tsfresh_features <- readr::read_csv("C:/Users/Andrii/Downloads/10996083/df_ALL_tsfresh_features.csv")
# List of features sets for model training # List of features sets for model training
list_features_all <- list( list_features_all <- list(
...@@ -152,9 +193,11 @@ list_features_all <- list( ...@@ -152,9 +193,11 @@ list_features_all <- list(
``` ```
```{r, echo = F, include = F} ```{r train_models, echo = F, include = F}
stud_names_648 <- as.character(df_rqa_features$student)
# Train function # Train function
som_gain_27f_5x5 <- train_test_models( som_models <- train_test_models(
list_features_all = list_features_all, list_features_all = list_features_all,
stud_names_648 = stud_names_648, stud_names_648 = stud_names_648,
activ_name_eng = activ_name_eng, activ_name_eng = activ_name_eng,
...@@ -181,89 +224,78 @@ som_gain_27f_5x5 <- train_test_models( ...@@ -181,89 +224,78 @@ som_gain_27f_5x5 <- train_test_models(
plot_pixel_size = plot_pixel_size, plot_pixel_size = plot_pixel_size,
legendPos = legendPos legendPos = legendPos
) )
# Save you trained results
#saveRDS(som_gain_27f_5x5, file = "/home/andrii/Documents/it4i/movement-classification/trained_models/som_gain_27f_5x5.RDS")
names(som_models) <- xgb_features_range |> as.character()
# Get the SOM quality metrics of you trained models
df_som_acc <- get_acc_all_features(som_list_feat = som_gain_27f_5x5)
# Save you results
#saveRDS(df_som_acc, file = "/home/andrii/Documents/it4i/movement-classification/df_results/df_som_acc.RDS")
``` ```
```{r fig1, fig.align='center', out.width="100%", fig.cap = "\\label{fig1}Example of a time series from an accelerometer recording.", echo = FALSE, include = T} ```{r fig1, fig.align='center', out.width="100%", fig.cap = "\\label{fig1}Example of a time series from an accelerometer recording.", echo = FALSE, include = T}
# For Figure 1: Example of a time series from an accelerometer recording # For Figure 1: Example of a time series from an accelerometer recording
#measured during a 4 x 10 meter run by one of the participants #measured during a 4 x 10 meter run by one of the participants
# Get the RAW time series https://zenodo.org/records/10984138
# Load data to R df_motion <- df_motion |>
# df_motion <- read_csv("C:/Users/Andrii/Downloads/10984138/physical_exercise.csv") dplyr::group_by(activity_id, student_id) |>
# dplyr::mutate(ts_length = 1:dplyr::n()) |>
# df_motion <- df_motion |> tidyr::nest(data = !tidyselect::one_of(c("student_id", "activity_id"))) |>
# group_by(activity_id, student_id) |> dplyr::ungroup()
# mutate(ts_length = 1:n()) |>
# tidyr::nest(data = !one_of(c("student_id", "activity_id"))) |> # Load one time series
# ungroup() data_test <- df_motion$data[[1]][c(1:3, 10)]
# data_long <- tidyr::pivot_longer(data_test,
# # Load one time series cols = c("accelerometer_x", "accelerometer_y", "accelerometer_z"),
# data_test <- df_motion$data[[1]][c(1:3, 10)] names_to = "group",
# data_long <- tidyr::pivot_longer(data_test, values_to = "value",
# cols = c("accelerometer_x", "accelerometer_y", "accelerometer_z"), names_prefix = "value")
# names_to = "group",
# values_to = "value", data_long$ts_length <- as.difftime(data_long$ts_length/100, units = 'secs')
# names_prefix = "value")
#
# data_long$ts_length <- as.difftime(data_long$ts_length/100, units = 'secs') figure_1 <- ggplot2::ggplot(data = data_long) +
# ggplot2::geom_line(ggplot2::aes(y = value, x = ts_length, color = group)) +
# ggplot2::theme_bw()+
# figure_1 <- ggplot(data = data_long) + ggplot2::theme(axis.text=ggplot2::element_text(size=20),
# geom_line(aes(y = value, x = ts_length, color = group)) + axis.title=ggplot2::element_text(size=22,face="bold"),
# theme_bw()+ legend.title = ggplot2::element_text(size = 22, face = "bold"),
# theme(axis.text=element_text(size=20), legend.text = ggplot2::element_text(size = 20)) +
# axis.title=element_text(size=22,face="bold"), ggplot2::labs(y= expression(bold(paste("Acceleration (g = 9.8 m/s" ^ "2",")"))), x = " Time (seconds)", color = "Axis")
# legend.title = element_text(size = 22, face = "bold"),
# legend.text = element_text(size = 20)) + figure_1
# labs(y= expression(bold(paste("Acceleration (g = 9.8 m/s" ^ "2",")"))), x = " Time (seconds)", color = "Axis")
#
# figure_1
``` ```
```{r fig2, fig.align='center', out.width="100%", fig.cap = "\\label{fig2}Tsfresh feature Importance by “Gain” bar plot.", echo = FALSE, include = T} ```{r fig2, fig.align='center', out.width="100%", fig.cap = "\\label{fig2}Tsfresh feature Importance by “Gain” bar plot.", echo = FALSE, include = T}
# Plot of the used features in article # Plot of the used features in article
plot_data_importance <- plot_data_importance <-
som_gain_27f_5x5[[27]]$train_data$tsfresh$df_important_feat_xgboost |> som_models[[best_n_features]]$train_data$tsfresh$df_important_feat_xgboost |>
dplyr::arrange(desc(Gain)) |> dplyr::arrange(desc(Gain)) |>
dplyr::mutate(Feature = fct_reorder(Feature, Gain, .desc = T)) |> dplyr::mutate(Feature = forcats::fct_reorder(Feature, Gain, .desc = T)) |>
dplyr::slice_head(n = 35) |> dplyr::slice_head(n = 35) |>
ggplot(aes(x = Feature, y = Gain)) + ggplot2::ggplot(ggplot2::aes(x = Feature, y = Gain)) +
xlab("Feature name") + ggplot2::xlab("Feature name") +
ylab("Value") + ggplot2::ylab("Value") +
geom_bar(stat = "identity") + ggplot2::geom_bar(stat = "identity") +
scale_x_discrete(labels = str_wrap(c(1:35), ggplot2::scale_x_discrete(labels = stringr::str_wrap(c(1:35),
width = 4)) + width = 4)) +
ggplot2::theme_bw() + ggplot2::theme_bw() +
theme( ggplot2::theme(
axis.text.x = element_text( axis.text.x = ggplot2::element_text(
#angle = 30, #angle = 30,
hjust = 0.5, hjust = 0.5,
vjust = 0.5, vjust = 0.5,
size = 10, size = 10,
face = "bold" face = "bold"
), ),
axis.title.x = element_text(size = 25, face = "bold"), axis.title.x = ggplot2::element_text(size = 25, face = "bold"),
axis.title.y = element_text(size = 25, face = "bold")#, axis.title.y = ggplot2::element_text(size = 25, face = "bold")#,
#panel.background = element_blank() #panel.background = element_blank()
) + ) +
geom_vline(xintercept = 27) ggplot2::geom_vline(xintercept = 27)
plot_data_importance plot_data_importance
``` ```
```{r tab4, echo = FALSE, include = T} ```{r tab4, echo = FALSE, include = T}
table_4_som_quality <- table_4_som_quality <-
do.call("rbind", list(som_gain_27f_5x5[[27]]$quality$`648obs`)) do.call("rbind", list(som_models[[best_n_features]]$quality$`648obs`))
DT::datatable( DT::datatable(
table_4_som_quality, table_4_som_quality,
...@@ -275,10 +307,8 @@ DT::datatable( ...@@ -275,10 +307,8 @@ DT::datatable(
``` ```
```{r tab5, echo=FALSE, include = T} ```{r tab5, echo=FALSE, include = T}
# Load pre trained models for 2:35 features
som_gain_2_35f_5x5 <- base::readRDS("D:/it4i/movement-classification/trained_models/som_gain_2_35f_5x5.RDS")
table_2_som_quality <- get_acc_all_features(som_gain_2_35f_5x5) table_2_som_quality <- get_acc_all_features(som_models)
DT::datatable( DT::datatable(
table_2_som_quality$table_format, table_2_som_quality$table_format,
...@@ -286,35 +316,32 @@ DT::datatable( ...@@ -286,35 +316,32 @@ DT::datatable(
filter = list(position = 'top', clear = FALSE) filter = list(position = 'top', clear = FALSE)
) |> ) |>
DT::formatRound(columns = c(2:27), digits = 3) DT::formatRound(columns = c(2:27), digits = 3)
``` ```
# Supplementary Materials # Supplementary Materials
```{r, echo=FALSE, include = T} ```{r compute_clustering, echo=FALSE, include = T}
# Used SOM in article # Used SOM in article
som_27f <- som_gain_27f_5x5[[27]]$som$tsfresh$SOM_results$som_model som_best <- som_models[[best_n_features]]$som$tsfresh$SOM_results$som_model
# Get superclusters number # Get superclusters number
superclust_pam <- cluster::pam(som_27f$codes[[1]], superclust_num) superclust_pam <- cluster::pam(som_best$codes[[1]], superclust_num)
superclasses_pam <- superclust_pam$clustering superclasses_pam <- superclust_pam$clustering
superclust_hclust <- stats::hclust(dist(som_27f$codes[[1]]), "complete") superclust_hclust <- stats::hclust(dist(som_best$codes[[1]]), "complete")
superclasses_hclust <- stats::cutree(superclust_hclust, superclust_num) superclasses_hclust <- stats::cutree(superclust_hclust, superclust_num)
``` ```
```{r fig3, fig.width = 5, fig.height = 6, fig.align='center', out.width="100%", fig.cap = "\\label{fig3}", echo = FALSE, include = T} ```{r fig3, fig.width = 5, fig.height = 6, fig.align='center', out.width="100%", fig.cap = "\\label{fig3}", echo = FALSE, include = T}
som_gain_27f_5x5[[27]]$som$tsfresh$SOM_results$aweSOM_plot som_models[[best_n_features]]$som$tsfresh$SOM_results$aweSOM_plot
``` ```
```{r fig6, fig.width = 5, fig.height = 6, fig.align='center', out.width="100%", fig.cap = "\\label{fig6}Supercluster table", echo = FALSE, include = T} ```{r fig6, fig.width = 5, fig.height = 6, fig.align='center', out.width="100%", fig.cap = "\\label{fig6}Supercluster table", echo = FALSE, include = T}
# Get table superclass # Get table superclass
df_table_6 <- df_table_6 <-
get_table_superclass(som_27f, superclass_number = superclust_num) get_table_superclass(som_best, superclass_number = superclust_num)
table_6 <- DT::datatable( table_6 <- DT::datatable(
df_table_6, df_table_6,
...@@ -327,7 +354,7 @@ table_6 ...@@ -327,7 +354,7 @@ table_6
```{r fig7, fig.width = 5, fig.height = 6, fig.align='center', out.width="100%", fig.cap = "\\label{fig7}Silhouette score", echo = FALSE, include = T} ```{r fig7, fig.width = 5, fig.height = 6, fig.align='center', out.width="100%", fig.cap = "\\label{fig7}Silhouette score", echo = FALSE, include = T}
#Silhouette score #Silhouette score
figure_7 <- aweSOM::aweSOMsilhouette(som_27f, superclasses_pam) figure_7 <- aweSOM::aweSOMsilhouette(som_best, superclasses_pam)
``` ```
```{r fig8, fig.width = 5, fig.height = 6, fig.align='center', out.width="100%", fig.cap = "\\label{fig5}Dendogram", echo = FALSE, include = T} ```{r fig8, fig.width = 5, fig.height = 6, fig.align='center', out.width="100%", fig.cap = "\\label{fig5}Dendogram", echo = FALSE, include = T}
......
renv/requirements.txt
+ 2
− 8
View file @ d70c8f97
numba==0.57.1
numba==0.55.1
tsfresh==0.20.0 tsfresh==0.20.0
numpy==1.21.6
\ No newline at end of file
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