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Update AR episode computation

pull/103/head
JulioV 2020-09-29 17:26:43 -04:00
parent 10b0386c8f
commit 39f6e3841c
1 changed files with 15 additions and 8 deletions
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23
src/features/ar/episodes/activity_recognition_episodes.R
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@ -4,19 +4,26 @@ library("dplyr")
activity_recognition <- read.csv(snakemake@input[[1]])
if(nrow(activity_recognition) > 0){
threshold_between_rows = 5
# TODO expose this in the config file
threshold_between_rows = 5 * 60000
ar_episodes <- activity_recognition %>%
mutate(start_timestamp = timestamp,
end_timestamp = lead(start_timestamp) - 1,
time_diff = (end_timestamp - start_timestamp) / 1000 / 60,
time_diff = if_else(time_diff > threshold_between_rows, threshold_between_rows, time_diff),
episode_id = 1:n()) %>%
select(episode_id, start_timestamp, end_timestamp, activity_type)
mutate(start_timestamp = timestamp, # a battery level starts as soon as is logged
time_diff = (lead(timestamp) - start_timestamp), # lead diff
# we assume the current activity existed until the next row only if that row is logged within [threshold_between_rows] minutes
end_timestamp = if_else(is.na(time_diff) | time_diff > (threshold_between_rows), start_timestamp + (threshold_between_rows), lead(timestamp) - 1),
time_diff = c(1, diff(start_timestamp)), # lag diff
type_diff = c(1, diff(activity_type)),
episode_id = cumsum(type_diff != 0 | time_diff > (threshold_between_rows))) %>%
group_by(episode_id) %>%
summarise(activity_name = first(activity_name), activity_type = first(activity_type), start_timestamp=first(start_timestamp), end_timestamp = last(end_timestamp))
} else {
ar_episodes <- data.frame(start_timestamp = numeric(),
end_timestamp = numeric(),
episode_id = numeric())
episode_id = numeric(),
activity_type = numeric(),
activity_name = character())
}
write.csv(ar_episodes, snakemake@output[[1]], row.names = FALSE)