rapids/src/features/phone_locations/barnett/main.R

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source("renv/activate.R")
library("dplyr", warn.conflicts = F)
library("stringr")
# Load Ian Barnett's code. Taken from https://scholar.harvard.edu/ibarnett/software/gpsmobility
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file.sources = list.files(c("src/features/phone_locations/barnett/library"), pattern="*.R$", full.names=TRUE, ignore.case=TRUE)
sapply(file.sources,source,.GlobalEnv)
create_empty_file <- function(requested_features){
return(data.frame(local_segment= character(),
hometime= numeric(),
disttravelled= numeric(),
rog= numeric(),
maxdiam= numeric(),
maxhomedist= numeric(),
siglocsvisited= numeric(),
avgflightlen= numeric(),
stdflightlen= numeric(),
avgflightdur= numeric(),
stdflightdur= numeric(),
probpause= numeric(),
siglocentropy= numeric(),
minsmissing= numeric(),
circdnrtn= numeric(),
wkenddayrtn= numeric(),
minutes_data_used= numeric()
) %>% select(all_of(requested_features)))
}
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barnett_features <- function(sensor_data_files, time_segment, params){
location_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE)
location_features <- NULL
location <- location_data
accuracy_limit <- params[["ACCURACY_LIMIT"]]
minutes_data_used <- params[["MINUTES_DATA_USED"]]
# Compute what features were requested
available_features <- c("hometime","disttravelled","rog","maxdiam", "maxhomedist","siglocsvisited","avgflightlen", "stdflightlen",
"avgflightdur","stdflightdur", "probpause","siglocentropy","minsmissing", "circdnrtn","wkenddayrtn")
requested_features <- intersect(unlist(params["FEATURES"], use.names = F), available_features)
requested_features <- c("local_segment", requested_features)
if(minutes_data_used)
requested_features <- c(requested_features, "minutes_data_used")
# Excludes datasets with less than 24 hours of data
if(max(location$timestamp) - min(location$timestamp) < 86400000)
location <- head(location, 0)
if (nrow(location) > 1){
# Filter by segment and skipping any non-daily segment
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location <- location %>% filter_data_by_segment(time_segment)
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datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00"
datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59"
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location <- location %>% mutate(is_daily = str_detect(local_segment, paste0(time_segment, "#", datetime_start_regex, ",", datetime_end_regex)))
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if(!all(location$is_daily)){
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message(paste("Barnett's location features cannot be computed for time segmentes that are not daily (cover 00:00:00 to 23:59:59 of every day). Skipping ", time_segment))
location_features <- create_empty_file(requested_features)
} else {
# Count how many minutes of data we use to get location features
# Some minutes have multiple fused rows
location_minutes_used <- location %>%
group_by(local_date, local_hour) %>%
summarise(n_minutes = n_distinct(local_minute), .groups = 'drop_last') %>%
group_by(local_date) %>%
summarise(minutes_data_used = sum(n_minutes), .groups = 'drop_last') %>%
select(local_date, minutes_data_used)
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# Save time segment to attach it later
location_dates_segments <- location %>% select(local_date, local_segment) %>% distinct(local_date, .keep_all = TRUE)
# Select only the columns that the algorithm needs
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all_timezones <- table(location %>% pull(local_timezone))
location <- location %>% select(timestamp, latitude = double_latitude, longitude = double_longitude, altitude = double_altitude, accuracy)
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if(nrow(location %>% filter(accuracy < accuracy_limit)) > 1){
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timezone <- names(all_timezones)[as.vector(all_timezones)==max(all_timezones)]
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outputMobility <- MobilityFeatures(location, ACCURACY_LIM = accuracy_limit, tz = timezone)
} else {
print(paste("Cannot compute Barnett location features because there are no rows with an accuracy value lower than ACCURACY_LIMIT", accuracy_limit))
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outputMobility <- NULL
}
if(is.null(outputMobility)){
location_features <- create_empty_file(requested_features)
} else{
# Copy index (dates) as a column
features <- cbind(rownames(outputMobility$featavg), outputMobility$featavg)
features <- as.data.frame(features)
features[-1] <- lapply(lapply(features[-1], as.character), as.numeric)
colnames(features)=c("local_date",tolower(colnames(outputMobility$featavg)))
# Add the minute count column
features <- left_join(features, location_minutes_used, by = "local_date")
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# Add the time segment column for consistency
features <- left_join(features, location_dates_segments, by = "local_date")
location_features <- features %>% select(all_of(requested_features))
}
}
} else {
location_features <- create_empty_file(requested_features)
}
if(ncol(location_features) != length(requested_features))
stop(paste0("The number of features in the output dataframe (=", ncol(location_features),") does not match the expected value (=", length(requested_features),"). Verify your barnett location features"))
return(location_features)
}