64 lines
3.1 KiB
R
64 lines
3.1 KiB
R
source("packrat/init.R")
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library(dplyr)
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library(entropy)
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library(robustbase)
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filter_by_day_segment <- function(data, day_segment) {
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if(day_segment %in% c("morning", "afternoon", "evening", "night"))
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data <- data %>% filter(local_day_segment == day_segment)
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return(data %>% group_by(local_date))
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}
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Mode <- function(v) {
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uniqv <- unique(v)
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uniqv[which.max(tabulate(match(v, uniqv)))]
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}
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compute_call_feature <- function(calls, metric, day_segment){
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if(metric == "countmostfrequentcontact"){
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# Get the most frequent contact
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calls <- calls %>% group_by(trace) %>%
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mutate(N=n()) %>%
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ungroup() %>%
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filter(N == max(N))
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return(calls %>%
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filter_by_day_segment(day_segment) %>%
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summarise(!!paste("call", type, day_segment, metric, sep = "_") := n()))
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} else {
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calls <- calls %>% filter_by_day_segment(day_segment)
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feature <- switch(metric,
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"count" = calls %>% summarise(!!paste("call", type, day_segment, metric, sep = "_") := n()),
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"distinctcontacts" = calls %>% summarise(!!paste("call", type, day_segment, metric, sep = "_") := n_distinct(trace)),
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"meanduration" = calls %>% summarise(!!paste("call", type, day_segment, metric, sep = "_") := mean(call_duration)),
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"sumduration" = calls %>% summarise(!!paste("call", type, day_segment, metric, sep = "_") := sum(call_duration)),
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"minduration" = calls %>% summarise(!!paste("call", type, day_segment, metric, sep = "_") := min(call_duration)),
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"maxduration" = calls %>% summarise(!!paste("call", type, day_segment, metric, sep = "_") := max(call_duration)),
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"stdduration" = calls %>% summarise(!!paste("call", type, day_segment, metric, sep = "_") := sd(call_duration)),
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"modeduration" = calls %>% summarise(!!paste("call", type, day_segment, metric, sep = "_") := Mode(call_duration)),
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"hubermduration" = calls %>% summarise(!!paste("call", type, day_segment, metric, sep = "_") := huberM(call_duration)$mu),
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"varqnduration" = calls %>% summarise(!!paste("call", type, day_segment, metric, sep = "_") := Qn(call_duration)),
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"entropyduration" = calls %>% summarise(!!paste("call", type, day_segment, metric, sep = "_") := entropy.MillerMadow(call_duration)),
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"timefirstcall" = calls %>% summarise(!!paste("call", type, day_segment, metric, sep = "_") := first(local_hour) + (first(local_minute)/60)),
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"timelastcall" = calls %>% summarise(!!paste("call", type, day_segment, metric, sep = "_") := last(local_hour) + (last(local_minute)/60)))
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}
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return(feature)
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}
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calls <- read.csv(snakemake@input[[1]], stringsAsFactors = FALSE)
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day_segment <- snakemake@params[["day_segment"]]
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metrics <- snakemake@params[["metrics"]]
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type <- snakemake@params[["call_type"]]
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features = data.frame(local_date = character(), stringsAsFactors = FALSE)
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calls <- calls %>% filter(call_type == ifelse(type == "incoming", "1", ifelse(type == "outgoing", "2", "3")))
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for(metric in metrics){
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feature <- compute_call_feature(calls, metric, day_segment)
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features <- merge(features, feature, by="local_date", all = TRUE)
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}
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write.csv(features, snakemake@output[[1]], row.names = FALSE)
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