93 lines
5.4 KiB
R
93 lines
5.4 KiB
R
library('tidyr')
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library('stringr')
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library('entropy')
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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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call_features_of_type <- function(calls, features_type, call_type, time_segment, requested_features){
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# Output dataframe
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features = data.frame(local_segment = character(), stringsAsFactors = FALSE)
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# The name of the features this function can compute
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base_features_names <- c("count", "distinctcontacts", "meanduration", "sumduration", "minduration", "maxduration", "stdduration", "modeduration", "entropyduration", "timefirstcall", "timelastcall", "countmostfrequentcontact")
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# The subset of requested features this function can compute
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features_to_compute <- intersect(base_features_names, requested_features)
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# If there are not features or data to work with, return an empty df with appropiate columns names
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if(length(features_to_compute) == 0)
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return(features)
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if(nrow(calls) < 1)
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return(cbind(features, read.csv(text = paste(paste(call_type, features_to_compute, sep = "_"), collapse = ","), stringsAsFactors = FALSE)))
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if(features_type == "EPISODES"){
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calls <- calls %>%
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mutate(call_duration = (end_timestamp - start_timestamp) / 1000) %>%
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separate(local_start_date_time, c("local_date","local_time"), "\\s", remove = FALSE) %>%
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separate(local_time, c("local_hour", "local_minute"), ":", remove = FALSE, extra = "drop") %>%
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mutate(local_hour = as.numeric(local_hour),
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local_minute = as.numeric(local_minute))
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}
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for(feature_name in features_to_compute){
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if(feature_name == "countmostfrequentcontact"){
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# Get the number of messages for the most frequent contact throughout the study
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mostfrequentcontact <- calls %>%
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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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head(1) %>% # if there are multiple contacts with the same amount of messages pick the first one only
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pull(trace)
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feature <- calls %>%
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group_by(local_segment) %>%
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summarise(!!paste(call_type, feature_name, sep = "_") := sum(trace == mostfrequentcontact))
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features <- merge(features, feature, by="local_segment", all = TRUE)
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} else {
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feature <- calls %>%
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group_by(local_segment)
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feature <- switch(feature_name,
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"count" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := n()),
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"distinctcontacts" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := n_distinct(trace)),
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"meanduration" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := mean(call_duration)),
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"sumduration" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := sum(call_duration)),
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"minduration" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := min(call_duration)),
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"maxduration" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := max(call_duration)),
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"stdduration" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := sd(call_duration)),
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"modeduration" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := Mode(call_duration)),
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"entropyduration" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := entropy.MillerMadow(call_duration)),
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"timefirstcall" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := first(local_hour) * 60 + first(local_minute)),
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"timelastcall" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := last(local_hour) * 60 + last(local_minute)))
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features <- merge(features, feature, by="local_segment", all = TRUE)
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}
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}
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return(features)
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}
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rapids_features <- function(sensor_data_files, time_segment, provider){
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calls_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE)
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calls_data <- calls_data %>% filter_data_by_segment(time_segment)
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features_type <- provider[["FEATURES_TYPE"]]
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call_types = provider[["CALL_TYPES"]]
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call_features <- setNames(data.frame(matrix(ncol=1, nrow=0)), c("local_segment"))
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for(call_type in call_types){
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# Filter rows that belong to the calls type and time segment of interest
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call_type_label = ifelse(call_type == "incoming", "1", ifelse(call_type == "outgoing", "2", ifelse(call_type == "missed", "3", NA)))
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if(is.na(call_type_label))
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stop(paste("Call type can online be incoming, outgoing or missed but instead you typed: ", call_type, " in config[CALLS][CALL_TYPES]"))
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requested_features <- provider[["FEATURES"]][[call_type]]
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calls_of_type <- calls_data %>% filter(call_type == call_type_label)
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features <- call_features_of_type(calls_of_type, features_type, call_type, time_segment, requested_features)
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call_features <- merge(call_features, features, all=TRUE)
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}
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call_features <- call_features %>% mutate_at(vars(contains("countmostfrequentcontact") | contains("distinctcontacts") | contains("count") | contains("sumduration") | contains("minduration") | contains("maxduration") | contains("meanduration") | contains("modeduration")), list( ~ replace_na(., 0)))
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return(call_features)
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} |