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util-split.R
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util-split.R
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## This file is part of coronet, which is free software: you
## can redistribute it and/or modify it under the terms of the GNU General
## Public License as published by the Free Software Foundation, version 2.
##
## This program is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
## GNU General Public License for more details.
##
## You should have received a copy of the GNU General Public License along
## with this program; if not, write to the Free Software Foundation, Inc.,
## 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
##
## Copyright 2017-2018 by Claus Hunsen <hunsen@fim.uni-passau.de>
## Copyright 2017 by Sofie Kemper <kemperso@fim.uni-passau.de>
## Copyright 2017 by Raphael Nömmer <noemmer@fim.uni-passau.de>
## Copyright 2017-2018 by Christian Hechtl <hechtl@fim.uni-passau.de>
## Copyright 2020 by Christian Hechtl <hechtl@cs.uni-saarland.de>
## Copyright 2017 by Felix Prasse <prassefe@fim.uni-passau.de>
## Copyright 2017-2018 by Thomas Bock <bockthom@fim.uni-passau.de>
## Copyright 2020, 2024 by Thomas Bock <bockthom@cs.uni-saarland.de>
## Copyright 2021 by Niklas Schneider <s8nlschn@stud.uni-saarland.de>
## Copyright 2021 by Johannes Hostert <s8johost@stud.uni-saarland.de>
## Copyright 2022 by Jonathan Baumann <joba00002@stud.uni-saarland.de>
## Copyright 2023-2024 by Maximilian Löffler <s8maloef@stud.uni-saarland.de>
## All Rights Reserved.
## / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /
## Libraries ---------------------------------------------------------------
requireNamespace("igraph") # networks
requireNamespace("logging") # for logging
requireNamespace("parallel") # for parallel computation
requireNamespace("lubridate") # for date conversion
## / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /
## Split data --------------------------------------------------------------
#' Split project data in time-based ranges as specified
#'
#' Important: For given 'time.period' parameters (e.g., 3-month windows), the last bin may be a lot smaller
#' than the specified time period.
#'
#' @param project.data the *Data object from which the data is retrieved
#' @param time.period the time period describing the length of the ranges, a character string,
#' e.g., "3 mins" or "15 days" [default: "3 months"]
#' @param bins the date objects defining the start of ranges (the last date defines the end of the last range, in an
#' *exclusive* manner). If set, the \code{time.period} parameter is ignored; consequently, \code{split.basis} and
#' \code{sliding.window} do not make sense then either. [default: NULL]
#' @param number.windows the number of consecutive data objects to get from this function, implying equally
#' time-sized windows for all ranges. If set, the \code{time.period} and \code{bins} parameters are ignored;
#' consequently, \code{sliding.window} does not make sense then either.
#' [default: NULL]
#' @param split.basis the data name to use as the basis for split bins, either 'commits', 'mails', or 'issues'
#' [default: "commits"]
#' @param sliding.window logical indicating whether the splitting should be performed using a sliding-window approach
#' [default: FALSE]
#' @param project.conf.new the new project config to construct the \code{RangeData} objects.
#' If \code{NULL}, a clone of \code{project.data$get.project.conf()} will be used.
#' [default: NULL]
#'
#' @return the list of RangeData objects, each referring to one time period
split.data.time.based = function(project.data, time.period = "3 months", bins = NULL,
number.windows = NULL, split.basis = c("commits", "mails", "issues"),
sliding.window = FALSE, project.conf.new = NULL) {
# validate existence and type of the 'bins' parameter
if (!is.null(bins) && !lubridate::is.POSIXct(bins)) {
dates = parallel::mclapply(unlist(bins), get.date.from.string)
if (any(is.na(dates))) {
logging::logerror(paste("The bins parameter, if present, needs to be a vector",
"whose elements represent dates"))
stop("Stopped due to incorrect parameter types")
}
}
split = split.data.by.time.or.bins(project.data, splitting.length = time.period, bins, split.by.time = TRUE,
number.windows, split.basis, sliding.window, project.conf.new)
return(split)
}
#' Split project data in activity-bin-based ranges as specified
#'
#' @param project.data the project data object from which the data is retrieved
#' @param activity.amount the amount of data elements with unique ids to be considered in a bin, an integer.
#' @param bins the bins by which data should be split. Comprises of two components:
#' \code{vector}: Assigns elements of the \code{split.basis} column of \code{project.data} to bins.
#' \code{bins}: Dates defining the start of bins (the last date defines the end of the last bin, in an
#' *exclusive* manner).
#' The expected format of \code{bins} is produced by \code{split.get.bins.activity.based}.
#' @param split.basis the data name to use as the basis for split bins, either 'commits', 'mails', or 'issues'
#' [default: "commits"]
#' @param sliding.window logical indicating whether a sliding-window approach was used when obtaining the \code{bins}.
#'
#' @return the list of RangeData objects, each referring to one bin
#'
#' @seealso split.get.bins.activity.based
split.data.by.bins = function(project.data, activity.amount, bins, split.basis = c("commits", "mails", "issues"),
sliding.window) {
# validate type of the 'bins' parameter
if (is.null(bins) || !is.list(bins)) {
logging::logerror("The bins parameter needs to be of type list, (is %s)", class(bins))
stop("Stopped due to incorrect parameter types")
}
# validate existence and type of the 'bins' component of the 'bins' parameter
if (!("bins" %in% names(bins))) {
logging::logerror("The 'bins' parameter needs to include a component 'bins'")
stop("Stopped due to incorrect parameter types")
}
dates = parallel::mclapply(bins[["bins"]], get.date.from.string)
if (any(is.na(dates))) {
logging::logerror(paste("The 'bins' component of the 'bins' parameter, needs to be a vector",
"whose elements represent dates"))
stop("Stopped due to incorrect parameter types")
}
# validate existence and type of the 'vector' component of the 'bins' parameter
if (!inherits(bins[["vector"]], "numeric")) {
logging::logerror("The 'vector' component of the bins parameter needs to be a numeric vector")
stop("Stopped due to incorrect parameter types")
}
split = split.data.by.time.or.bins(project.data, activity.amount, bins, split.by.time = FALSE,
sliding.window = sliding.window, split.basis = split.basis)
return(split)
}
#' Split project data by timestamps
#'
#' Splits project data into ranges, where the first range starts with the first timestamp
#' and the last range ends with the last timestamp.
#'
#' If timestamps are not provided, the custom event timestamps in \code{project.data} are
#' used instead. If no custom event timestamps are available in \code{project.data}, an error is thrown.
#'
#' @param project.data the *Data object from which the data is retrieved
#' @param bins a vector of timestamps [default: NULL]
#' @param project.conf.new the new project config to construct the \code{RangeData} objects.
#' If \code{NULL}, a clone of \code{project.data$get.project.conf()} will be used.
#' [default: NULL]
#'
#' @return the list of RangeData objects, each referring to one time period
split.data.time.based.by.timestamps = function(project.data, bins = NULL, project.conf.new = NULL) {
if (is.null(bins)) { # bins were not provided, use custom timestamps from project
bins = unlist(project.data$get.custom.event.timestamps())
if (is.null(bins)) { # stop if no custom timestamps are available
logging::logerror("There are no custom timestamps available for splitting (configured file: %s).",
project.data$get.project.conf.entry("custom.event.timestamps.file"))
stop("Stopping due to missing data.")
}
}
return(split.data.time.based(project.data, bins = bins, project.conf.new))
}
#' Split project data in activity-based ranges as specified
#'
#' Important: For a given amount of activity, the last set of data may be a lot smaller
#' than the specified amount.
#'
#' @param project.data the *Data object from which the data is retrieved
#' @param activity.type the type of activity used for splitting, either 'commits', 'mails', or 'issues'
#' [default: "commits"]
#' @param activity.amount the amount of activity describing the size of the ranges, a numeric, further
#' specified by 'activity.type' [default: 5000]
#' @param number.windows the number of consecutive data objects to get from this function
#' (implying an equally distributed amount of data in each range and
#' 'sliding.window = FALSE') [default: NULL]
#' @param sliding.window logical indicating whether the splitting should be performed using a sliding-window approach
#' [default: FALSE]
#' @param project.conf.new the new project config to construct the \code{RangeData} objects.
#' If \code{NULL}, a clone of \code{project.data$get.project.conf()} will be used.
#' [default: NULL]
#'
#' @return the list of RangeData objects, each referring to one time period
split.data.activity.based = function(project.data, activity.type = c("commits", "mails", "issues"),
activity.amount = 5000, number.windows = NULL,
sliding.window = FALSE, project.conf.new = NULL) {
## get basis for splitting process
activity.type = match.arg(activity.type)
## get actual raw data
data.sources = project.data$get.cached.data.sources("only.unfiltered")
data = lapply(data.sources, function(ds) {
## build the name of the respective getter and call it
function.name = DATASOURCE.TO.UNFILTERED.ARTIFACT.FUNCTION[[ds]]
return(project.data[[function.name]]())
})
names(data) = data.sources
## if the data used by the split basis is not present, load it automatically
if (!(activity.type %in% project.data$get.cached.data.sources("only.unfiltered"))) {
function.name = DATASOURCE.TO.UNFILTERED.ARTIFACT.FUNCTION[[activity.type]]
project.data[[function.name]]()
}
## define ID columns for mails and commits
id.column = list(
commits = "hash",
mails = "message.id",
issues = "event.id"
)
## get amount of available activity
activity = length(unique(data[[activity.type]][[ id.column[[activity.type]] ]]))
if (is.null(project.conf.new)) {
## Clone the project configuration, so that splitting repeatedly does not interfere
## with the same configuration.
project.conf.new = project.data$get.project.conf()$clone()
}
## activity amount given (number of windows NOT given)
if (is.null(number.windows)) {
if (activity < 1) {
logging::logerror("The given amount of activity has to be strictly positive (given: %s).", activity)
stop("Stopping due to missing data.")
}
## compute the number of time windows according to the activity amount
number.windows = ceiling(activity / activity.amount)
if (activity < activity.amount) {
activity.type.pretty = list(
commits = "commits",
mails = "mails",
issues = "issue events"
)[[activity.type]]
logging::logwarn("Can not form bins of %s %s for splitting data %s, as there are only %s %s.",
activity.amount, activity.type.pretty, project.data$get.class.name(), activity, activity.type.pretty)
}
}
## number of windows given (ignoring amount of activity)
else {
## check the breaking case
if (number.windows < 1 || number.windows > activity) {
logging::logerror("The given number of windows is not suitable for this
data object (given: %s).", number.windows)
stop("Stopping due to illegally specified amount of windows to create.")
}
## compute the amount of activity according to the number of specified windows
activity.amount = ceiling(activity / number.windows)
## remove sliding windows as they do not make sense here
sliding.window = FALSE
}
logging::loginfo("Splitting data '%s' into activity ranges of %s %s (%s windows).",
project.data$get.class.name(), activity.amount, activity.type, number.windows)
## get bins based on 'split.basis'. Here the 'include.duplicate.ids' parameter flag must be set, to
## retrieve bins which map every event to a bin including events with non-unique ids. This is important
## to ensure that every range really has 'activity.amount' many entries after splitting
logging::logdebug("Getting activity-based bins.")
bins.data = split.get.bins.activity.based(data[[activity.type]], id.column[[activity.type]],
activity.amount, remove.duplicate.bins = TRUE, include.duplicate.ids = TRUE)
bins = bins.data[["bins"]]
bins.date = get.date.from.string(bins)
## split the data based on the extracted timestamps
logging::logdebug("Splitting data based on time windows arising from activity bins.")
cf.data = split.data.by.bins(project.data, bins = bins.data, activity.amount = activity.amount,
sliding.window = sliding.window, split.basis = activity.type)
## perform additional steps for sliding-window approach:
## for activity-based sliding-window bins to work, we need to crop the data appropriately and,
## then, compute bins on the cropped data
## (only if there is more than one range until here)
if (sliding.window && length(bins.date) <= 2) {
logging::logwarn("Sliding-window approach does not apply for one range or less.")
} else if (sliding.window) {
## get the list of unique items that are used for the bin computation and, thus, also the
## cropping of data
items.unique = unique(data[[activity.type]][[ id.column[[activity.type]] ]])
items.unique.count = length(items.unique)
## offsets used for cropping (half of the first bin)
offset.start = floor(activity.amount / 2)
items.cut = items.unique[seq_len(offset.start)]
## store the data again
data.to.cut = data[[activity.type]][[ id.column[[activity.type]] ]] %in% items.cut
data[[activity.type]] = data[[activity.type]][ !data.to.cut, ]
## clone the project data and update raw data to split it again
project.data.clone = project.data$clone()
project.data.clone$set.commits(data[["commits"]])
project.data.clone$set.mails(data[["mails"]])
project.data.clone$set.issues(data[["issues"]])
## split data for sliding windows
cf.data.sliding = split.data.activity.based(project.data.clone, activity.type = activity.type,
activity.amount = activity.amount, sliding.window = FALSE,
project.conf.new = project.conf.new)
## extract bins
bins.date.middle = get.date.string(attr(cf.data.sliding, "bins"))
## Both, the last sliding range and the last regular range end at the very last item.
## This is the case because the end of the data is never cropped (like the beginning is).
## 'split.data.activity.based', which is invoked to obtain both set of ranges, creates
## ranges until all elements are in one.
##
## The conditional below inspects whether the very last item is in the first or the second
## half of the last regular range. If it is in the first half, there will be a sliding
## window which covers all items of the last regular range which makes the last regular
## range obsolete.
## Similarely if the last item is in the second half of the last regular range, there
## will be a sliding range (which started at the half of the last regular range) which
## contains only items also included in the last regular range, which makes the sliding
## range obsolete.
length.of.last.range = items.unique.count %% activity.amount
if (length.of.last.range > offset.start || length.of.last.range == 0) {
cf.data.sliding = cf.data.sliding[-length(cf.data.sliding)]
bins.date.middle = bins.date.middle[-length(bins.date.middle)]
} else {
cf.data = cf.data[-length(cf.data)]
bins.date = bins.date[-length(bins.date)]
bins = bins[-length(bins)]
}
## append data to normally-split data
cf.data = append(cf.data, cf.data.sliding)
## sort data object properly by bin starts
bins.ranges.start = c(head(bins.date, -1), head(bins.date.middle, -1))
cf.data = cf.data[ order(bins.ranges.start) ]
## construct proper bin vectors for configuration
bins.date = sort(c(bins.date, bins.date.middle))
bins = get.date.string(bins.date)
## update project configuration
project.conf.new$set.revisions(bins, bins.date, sliding.window = TRUE)
for (cf in cf.data) {
## re-set project configuration due to object duplication
cf.conf = cf$set.project.conf(project.conf.new, reset.environment = FALSE)
}
}
## add splitting information to project configuration
project.conf.new$set.splitting.info(
type = "activity-based",
length = activity.amount,
basis = activity.type,
sliding.window = sliding.window,
revisions = bins,
revisions.dates = bins.date
)
## set bin attribute
attr(cf.data, "bins") = bins.date
return(cf.data)
}
#' Map a list of networks to their corresponding range data, after splitting the
#' given project data (\code{project.data}) to the time ranges given by the networks'
#' names. The splitting can be more specifically configured with the parameter
#' \code{aggregation.level}, see below for more details.
#'
#' For this function to work properly, the list of networks needs to be named with
#' timestamp-ranges, which can be splitted using \code{get.range.bounds}. The easiest
#' way to achieve this is to use one of the \code{split.*} functions in this very file.
#' For example, the time ranges have a format like this:
#' "2017-01-01 23:57:01-2017-02-15 12:19:37", which can be split by the utility
#' function \code{get.range.bounds}, obtaining the range bounds as timestamps.
#'
#' Using different aggregation levels given by the parameter \code{aggregation.level},
#' it is possible to configure the exact treatment of range bounds and, thus, the
#' splitting of the given project data. The various aggregation levels work as follows:
#' - \code{"range"}: The project data will be split exactly to the time ranges specified
#' by the networks' names.
#' - \code{"cumulative"}: The project data will be split exactly to the time ranges
#' specified by the networks' names, but in a cumulative manner.
#' - \code{"all.ranges"}: The project data will be split exactly to the time range
#' specified by the start of the first network and end of the last
#' network. All data instances will contain the same data.
#' - \code{"project.cumulative"}: The same splitting as for \code{"cumulative"}, but all
#' data will start at the beginning of the project data and *not* at
#' the beginning of the first network.
#' - \code{"project.all.ranges"}: The same splitting as for \code{"all.ranges"}, but all
#' data will start at the beginning of the project data and *not* at
#' the beginning of the first network. All data instances will contain
#' the same data.
#' - \code{"complete"}: The same splitting as for \code{"all.ranges"}, but all data will
#' start at the beginning of the project data and end at the end of
#' the project data. All data instances will contain the same data.
#'
#' @param list.of.networks The network list
#' @param project.data The entire project data
#' @param aggregation.level One of \code{"range"}, \code{"cumulative"}, \code{"all.ranges"},
#' \code{"project.cumulative"}, \code{"project.all.ranges"}, and
#' \code{"complete"}. See above for more details. [default: "range"]
#'
#' @return A list containing tuples with the keys "network" and "data", where, under "network", are
#' the respective networks passed via \code{list.of.networks} and, under "data", are the
#' split data instances of type \code{RangeData}.
#'
#' @seealso \code{aggregate.ranges}
split.data.by.networks = function(list.of.networks, project.data,
aggregation.level = c("range", "cumulative", "all.ranges",
"project.cumulative", "project.all.ranges",
"complete")) {
## get the chosen aggregation level
aggregation.level = match.arg.or.default(aggregation.level, default = "range")
## get the timestamp data from the project data (needed for some aggr. levels)
project.timestamps = project.data$get.data.timestamps(outermost = TRUE)
## get the list of ranges
list.of.ranges = names(list.of.networks)
## aggregate ranges
ranges.bounds = aggregate.ranges(
list.of.ranges, project.start = project.timestamps[["start"]], project.end = project.timestamps[["end"]],
aggregation.level = aggregation.level, raw = TRUE
)
## split the data by the computed (and aggregated) ranges
list.of.data = split.data.time.based.by.ranges(project.data, ranges.bounds)
## zip networks and range data
net.to.range.list = mapply(
list.of.networks, list.of.data, SIMPLIFY = FALSE,
FUN = function(net, range.data) {
net.to.range.entry = list(
"network" = net,
"data" = range.data
)
return(net.to.range.entry)
}
)
## properly set names for the result list
names(net.to.range.list) = list.of.ranges
return(net.to.range.list)
}
#' Split the given data to the given ranges and return the resulting list.
#'
#' Note: You may want to use any function \code{construct.*.ranges} to obtain
#' an appropriate sequence of ranges to pass to this function.
#'
#' @param project.data the \code{ProjectData} instance to be split
#' @param ranges the ranges to be used for splitting
#'
#' @return a list of \code{RangeData} instances, each representing one of the
#' given ranges; the ranges are used as names for the list
split.data.time.based.by.ranges = function(project.data, ranges) {
## check whether all ranges are identical (then we only need to split the data once)
if (length(ranges) > 1 && length(unique(ranges)) == 1) {
## aggregate range
range.bounds = get.range.bounds(ranges[[1]])
## split data accordingly
range.data = split.data.time.based(project.data, bins = range.bounds, sliding.window = FALSE)[[1]]
## clone range data objects (as all ranges are identical)
data.split = lapply(ranges, function(x) range.data$clone())
} else {
## aggregate ranges
ranges.bounds = lapply(ranges, get.range.bounds)
## loop over all ranges and split the data accordingly:
data.split = mapply(ranges, ranges.bounds, SIMPLIFY = FALSE, FUN = function(range, start.end) {
## 1) split the data to the current range
range.data = split.data.time.based(project.data, bins = start.end, sliding.window = FALSE)[[1]]
## 2) return the data
return(range.data)
})
}
return(data.split)
}
## / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /
## Split networks ----------------------------------------------------------
#' Discretizes a network (using the edge attribute "date") according to the given 'time.period',
#' to the given hard 'bins', or the given number of windows ('number.windows').
#'
#' Important: For given 'time.period' parameters (e.g., 3-month windows), the last bin may be a lot smaller
#' than the specified time period.
#'
#' Important notice: This function only works for unsimplified networks, where no edges have been
#' contracted, which would combine edge attributes, especially the "date" attribute.
#'
#' @param network the igraph network to split, needs to have an edge attribute named "date"
#' @param time.period the time period describing the length of the ranges, a character string,
#' e.g., "3 mins" or "15 days" [default: "3 months"]
#' @param bins the date objects defining the start of ranges (the last date defines the end of the last range, in an
#' *exclusive* manner). If set, the 'time.period' and 'sliding.window' parameters are ignored.
#' [default: NULL]
#' @param number.windows the number of consecutive networks to get from this function, implying equally
#' time-sized windows for all ranges. If set, the 'time.period' and 'bins' parameters are ignored;
#' consequently, 'sliding.window' does not make sense then either.
#' [default: NULL]
#' @param sliding.window logical indicating whether the splitting should be performed using a sliding-window approach
#' [default: FALSE]
#' @param remove.isolates whether to remove isolates in the resulting split networks [default: TRUE]
#'
#' @return a list of igraph networks, each referring to one time period
split.network.time.based = function(network, time.period = "3 months", bins = NULL,
number.windows = NULL, sliding.window = FALSE,
remove.isolates = TRUE) {
## extract date attributes from edges
dates = get.date.from.unix.timestamp(igraph::get.edge.attribute(network, "date"))
## number of windows given (ignoring time period and bins)
if (!is.null(number.windows)) {
## reset bins for the later algorithm
bins = NULL
## ignore sliding windows
sliding.window = FALSE
}
## get bin information for all edges
if (is.null(bins)) {
## get bins
bins.info = split.get.bins.time.based(dates, time.period, number.windows)
bins.vector = bins.info[["vector"]]
bins.date = get.date.from.string(bins.info[["bins"]])
bins = head(bins.info[["bins"]], -1)
} else {
## specific bins are given, do not use sliding windows
sliding.window = FALSE
## find bins for dates
bins.date = get.date.from.string(bins)
bins.vector = findInterval(dates, bins.date, all.inside = FALSE)
bins = seq_len(length(bins.date) - 1) # the last item just closes the last bin
}
## perform additional steps for sliding-window approach
if (sliding.window) {
ranges = construct.overlapping.ranges(start = min(bins.date), end = max(bins.date),
time.period = time.period, overlap = 0.5, raw = FALSE,
include.end.date = FALSE)
logging::loginfo("Splitting network into overlapping time ranges [%s].",
paste(ranges, collapse = ", "))
nets = split.network.time.based.by.ranges(network, ranges, remove.isolates)
} else {
revs = get.date.string(bins.date)
ranges = construct.ranges(revs, sliding.window = FALSE)
logging::loginfo("Splitting network into non-overlapping time ranges [%s].",
paste(ranges, collapse = ", "))
nets = split.network.by.bins(network, bins, bins.vector, bins.date, remove.isolates)
}
## set ranges as names
names(nets) = ranges
return(nets)
}
#' Discretizes a list of networks (using the edge attribute "date") according to the given 'time.period',
#' using the very same bins for all networks. The procedure is as follows:
#' 1) Use the earliest timestamp of all networks and the latest timestamp of all networks
#' to compute the bins for splitting.
#' 2) All networks are then split using the computed and, thus, very same bins using the
#' function \code{split.network.time.based}.
#' 3) The list of split networks is returned.
#'
#' For further information, see the documentation of \code{split.network.time.based}.
#'
#' Important notice: This function only works for unsimplified networks, where no edges have been
#' contracted, which would combine edge attributes, especially the "date" attribute.
#'
#' @param networks the igraph networks to split, needs to have an edge attribute named "date"
#' @param time.period the time period describing the length of the ranges, a character string,
#' e.g., "3 mins" or "15 days" [default: "3 months"]
#' @param bins the date objects defining the start of ranges (the last date defines the end of the last range, in an
#' *exclusive* manner). If set, the 'time.period' and 'sliding.window' parameters are ignored.
#' [default: NULL]
#' @param number.windows the number of consecutive networks to get for each network, implying equally
#' time-sized windows for all ranges. If set, the 'time.period' and 'bins' parameters are ignored;
#' consequently, 'sliding.window' does not make sense then either.
#' [default: NULL]
#' @param sliding.window logical indicating whether the splitting should be performed using a sliding-window approach
#' [default: FALSE]
#' @param remove.isolates whether to remove isolates in the resulting split networks [default: TRUE]
#'
#' @return a list of network-splitting results (of length \code{length(networks)}), each item referring to a list
#' of networks, each itself referring to one time period
split.networks.time.based = function(networks, time.period = "3 months", bins = NULL,
number.windows = NULL, sliding.window = FALSE,
remove.isolates = TRUE) {
## number of windows given (ignoring time period and bins)
if (!is.null(number.windows)) {
## reset bins for the later algorithm
bins = NULL
## ignore sliding windows
sliding.window = FALSE
}
if (is.null(bins)) {
## get base network and obtain splitting information:
## 1) extract date attributes from edges
networks.dates = lapply(networks, function(net) {
dates = igraph::E(net)$date
return(dates)
})
dates = unlist(networks.dates, recursive = FALSE)
dates = get.date.from.unix.timestamp(dates)
## 2) get bin information
if (sliding.window) {
ranges = construct.overlapping.ranges(start = min(dates), end = max(dates),
time.period = time.period, overlap = 0.5, raw = FALSE,
include.end.date = TRUE)
} else {
bins.info = split.get.bins.time.based(dates, time.period, number.windows)
bins.date = get.date.from.string(bins.info[["bins"]])
}
} else {
## specific bins are given, do not use sliding windows
sliding.window = FALSE
## set the bins to use
bins.date = bins
}
## split all networks to the extracted bins
networks.split = lapply(networks, function(net) {
if (sliding.window) {
nets = split.network.time.based.by.ranges(network = net, ranges = ranges,
remove.isolates = remove.isolates)
} else {
nets = split.network.time.based(network = net, bins = bins.date, sliding.window = sliding.window,
remove.isolates = remove.isolates)
}
return(nets)
})
## return the split networks
return(networks.split)
}
#' Discretizes a network according to the given 'number.edges' or by a predefined 'number.windows'.
#'
#' Important: For a given amount of edges, the last set of data may be a lot smaller
#' than the specified amount.
#'
#' Important notice: This function only works for unsimplified networks, where no edges have been
#' contracted, which would combine edge attributes, especially the "date" attribute.
#'
#' @param network the igraph network to split
#' @param number.edges the amount of edges describing the size of the ranges
#' (implying an open number of resulting ranges) [default: 5000]
#' @param number.windows the number of consecutive networks to get from this function
#' (implying an equally distributed amount of edges in each range and
#' 'sliding.window = FALSE) [default: NULL]
#' @param sliding.window logical indicating whether the splitting should be performed using
#' a sliding-window approach [default: FALSE]
#' @param remove.isolates whether to remove isolates in the resulting split networks [default: TRUE]
#'
#' @return a list of igraph networks, each referring to one period of activity
split.network.activity.based = function(network, number.edges = 5000, number.windows = NULL,
sliding.window = FALSE, remove.isolates = TRUE) {
## get total edge count
edge.count = igraph::ecount(network)
## number of edges given (number of windows NOT given)
if (is.null(number.windows)) {
if (edge.count < 1) {
logging::logerror("The number of edges in the given network has to be
strictly positive (given: %s).", edge.count)
stop("Stopping due to missing edges in given network.")
}
## compute the number of time windows according to the number of edges per network
number.windows = ceiling(edge.count / number.edges)
}
## number of windows given (ignoring number of edges)
else {
## check the breaking case
if (number.windows < 1 || number.windows > edge.count) {
logging::logerror("The given number of windows is not suitable for this
network (given: %s).", number.windows)
stop("Stopping due to illegally specified amount of windows to create.")
}
## compute the amount of activity according to the number of specified windows
number.edges = ceiling(edge.count / number.windows)
## remove sliding windows as they do not make sense here
sliding.window = FALSE
}
logging::loginfo("Splitting network into activity ranges of %s edges, yielding %s windows.",
number.edges, number.windows)
## get dates in a data.frame for splitting purposes
df = data.frame(
date = get.date.from.unix.timestamp(igraph::get.edge.attribute(network, "date")),
my.unique.id = seq_len(edge.count) # as a unique identifier only
)
## sort by date
df = df[ with(df, order(date)), ]
## identify bins
logging::logdebug("Getting bins for activity-based splitting based on amount of edges.")
bins.data = split.get.bins.activity.based(df, "my.unique.id", activity.amount = number.edges,
remove.duplicate.bins = FALSE)
bins.date = bins.data[["bins"]]
bins.vector = bins.data[["vector"]]
bins.vector = bins.vector[ with(df, order(my.unique.id)) ] # re-order to get igraph ordering
bins = sort(unique(bins.vector))
## split network by bins
networks = split.network.by.bins(network, bins, bins.vector, remove.isolates = remove.isolates)
if (number.edges >= edge.count) {
logging::logwarn("Sliding-window approach does not apply: not enough edges (%s) for number of edges %s",
edge.count, number.edges)
sliding.window = FALSE
}
## perform additional steps for sliding-window approach
## for activity-based sliding-window bins to work, we need to crop edges appropriately and,
## then, compute bins on the cropped networks
if (sliding.window) {
## get edge ids ordered by date
edges.by.date = df[["my.unique.id"]]
## offsets used for cropping (half the first/last bin)
offset.start = floor(number.edges / 2)
edges.cut = edges.by.date[seq_len(offset.start)]
## delete edges from the network and create a new network
network.cut = igraph::delete.edges(network, igraph::E(network)[edges.cut])
## split network for sliding windows
networks.sliding = split.network.activity.based(network.cut, number.edges = number.edges,
sliding.window = FALSE)
## compute bins for sliding windows: pairwise middle between dates
bins.date.middle = get.date.string(attr(networks.sliding, "bins"))
## Both, the last sliding network and the last regular network end at the very last edge.
## This is the case because the end of the edges is never cropped (like the beginning is).
## Both 'split.network.activity.based', and 'split.network.by.bins', which are invoked to obtain
## the two set of networks, creates networks until all edges are contained.
##
## The conditional below inspects whether the very last edge is in the first or the second
## half of the last regular network. If it is in the first half, there will be a sliding
## network which covers all edges of the last regular network which makes the last regular
## network obsolete.
## Similarely if the last edge is in the second half of the last regular network, there
## will be a sliding network (which started at the half of the last regular network) which
## contains only edges also included in the last regular network, which makes the sliding
## network obsolete.
length.of.last.range = edge.count %% number.edges
if (length.of.last.range > offset.start || length.of.last.range == 0) {
networks.sliding = networks.sliding[-length(networks.sliding)]
bins.date.middle = bins.date.middle[-length(bins.date.middle)]
} else {
networks = networks[-length(networks)]
bins.date = bins.date[-length(bins.date)]
bins = bins[-length(bins)]
}
## append sliding networks to normally-split networks
networks = append(networks, networks.sliding)
## sort networks properly by bin starts
bins.ranges.start = c(head(bins.date, -1), head(bins.date.middle, -1))
networks = networks[ order(bins.ranges.start) ]
## construct proper bin vectors for configuration
bins.date = sort(c(bins.date, bins.date.middle))
}
## set bin attribute
attr(networks, "bins") = get.date.from.string(bins.date)
## set ranges as names
revs = get.date.string(bins.date)
names(networks) = construct.ranges(revs, sliding.window = sliding.window)
## issue warning if ranges are not unique
if (any(duplicated(names(networks)))) {
logging::logwarn(
paste("Due to the splitting, there are duplicated range names.",
"You can correct these by calling the function 'split.unify.range.names()'",
"and providing the range names.")
)
}
return(networks)
}
#' Split the given network to the given ranges and return the resulting list.
#'
#' Note: You may want to use any function \code{construct.*.ranges} to obtain
#' an appropriate sequence of ranges to pass to this function.
#'
#' @param network the network to be split
#' @param ranges the ranges to be used for splitting
#' @param remove.isolates whether to remove isolates in the resulting split networks [default: TRUE]
#'
#' @return a list of networks, each representing one of the given ranges; the
#' ranges are used as names for the list
split.network.time.based.by.ranges = function(network, ranges, remove.isolates = TRUE) {
## aggregate ranges
ranges.bounds = lapply(ranges, get.range.bounds)
## loop over all ranges and split the network accordingly:
nets.split = lapply(ranges.bounds, function(bounds) {
## 1) split the network to the current range
range.net = split.network.time.based(network, bins = bounds, sliding.window = FALSE,
remove.isolates = remove.isolates)[[1]]
## 2) return the network
return(range.net)
}
)
## convert ranges to bins
bins = get.bin.dates.from.ranges(ranges.bounds)
attr(nets.split, "bins") = bins
return(nets.split)
}
## / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /
## Split raw data ----------------------------------------------------------
#' Split the given datafame by the given bins.
#'
#' @param df a data.frame to be split
#' @param bins a vector with the length of 'nrow(df)' assigning a bin for each row of 'df'
#'
#' @return a list of data.frames, with the length of 'unique(bins)'
split.dataframe.by.bins = function(df, bins) {
logging::logdebug("split.dataframe.by.bins: starting.")
df.split = split(df, bins)
logging::logdebug("split.dataframe.by.bins: finished.")
return(df.split)
}
#' Split the given data by the given bins, in increasing order of the bin identifiers.
#'
#' @param network a network
#' @param bins a vector with the unique bin identifiers, describing the order in which the bins are created
#' @param bins.vector a vector of length 'ecount(network)' assigning a bin for each edge of 'network'
#' @param bins.date a vector of dates representing the start of each bin. If present, then the dates will be set
#' as an attribute on the returned networks [default: NULL]
#' @param remove.isolates whether to remove isolates in the resulting split networks [default: TRUE]
#'
#' @return a list of networks, with the length of 'unique(bins.vector)'
split.network.by.bins = function(network, bins, bins.vector, bins.date = NULL, remove.isolates = TRUE) {
logging::logdebug("split.network.by.bins: starting.")
## create a network for each bin of edges
nets = parallel::mclapply(bins, function(bin) {
logging::logdebug("Splitting network: bin %s", bin)
## identify edges in the current bin
edges = igraph::E(network)[ bins.vector == bin ]
## create network based on the current set of edges
g = igraph::subgraph.edges(network, edges, delete.vertices = remove.isolates)
return(g)
})
## set 'bins' attribute, if specified
if (!is.null(bins.date)) {
attr(nets, "bins") = get.date.from.string(bins.date)
}
logging::logdebug("split.network.by.bins: finished.")
return(nets)
}
## / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / / /
## Internal helper functions for data splitting ----------------------------
#' Split project data in time-based or activity-bin-based ranges as specified
#'
#' @param project.data the *Data object from which the data is retrieved
#' @param splitting.length either \code{time.period} from \code{split.data.time.based}
#' or \code{activity.amount} from \code{split.data.by.bins}
#' @param bins either formatted as the \code{bins} parameter of \code{split.data.time.based}
#' or as the \code{bins} parameter of \code{split.data.by.bins}
#' @param split.by.time logical indicating whether splitting is done time-based or activity-bins-based
#' @param number.windows see \code{number.windows} from \code{split.data.time.based}
#' [default: NULL]
#' @param split.basis the data source to use as the basis for split bins, either 'commits', 'mails', or 'issues'
#' [default: "commits"]
#' @param sliding.window logical indicating whether the splitting should be performed using a sliding-window approach
#' [default: FALSE]
#' @param project.conf.new the new project config to construct the \code{RangeData} objects.
#' If \code{NULL}, a clone of \code{project.data$get.project.conf()} will be used.
#' [default: NULL]
#'
#' @return the list of RangeData objects, each referring to one time period
#'
#' @seealso split.data.time.based
#' @seealso split.data.by.bins
split.data.by.time.or.bins = function(project.data, splitting.length, bins, split.by.time,
number.windows = NULL, split.basis = c("commits", "mails", "issues"),
sliding.window = FALSE, project.conf.new = NULL) {
## get basis for splitting process
split.basis = match.arg(split.basis)
## if the data used by the split basis is not present, load it automatically
if (!(split.basis %in% project.data$get.cached.data.sources("only.unfiltered"))) {
function.name = DATASOURCE.TO.UNFILTERED.ARTIFACT.FUNCTION[[split.basis]]
project.data[[function.name]]()
}
## get actual raw data
data.to.split = project.data$get.cached.data.sources("only.unfiltered")
data = lapply(data.to.split, function(ds) {
## build the name of the respective getter and call it
function.name = DATASOURCE.TO.UNFILTERED.ARTIFACT.FUNCTION[[ds]]
return(project.data[[function.name]]())
})
names(data) = data.to.split
## load available additional data sources
additional.data.sources = project.data$get.cached.data.sources("only.additional")
additional.data = lapply(additional.data.sources, function(ds) {
## build the name of the respective getter and call it
function.name = DATASOURCE.TO.ADDITIONAL.ARTIFACT.FUNCTION[[ds]]
return(project.data[[function.name]]())
})
names(additional.data) = additional.data.sources
## number of windows given (ignoring time period and bins)
if (!is.null(number.windows)) {
## reset bins for the later algorithm
bins = NULL
## remove sliding windows
sliding.window = FALSE
}
## indicates if time-based splitting is performed using bins
split.time.based.with.bins = FALSE
## if bins are NOT given explicitly
if (is.null(bins)) {
## get bins based on split.basis
bins = split.get.bins.time.based(data[[split.basis]][["date"]], splitting.length, number.windows)$bins
bins.labels = head(bins, -1)
## logging
logging::loginfo("Splitting data '%s' into time ranges of %s based on '%s' data.",
project.data$get.class.name(), splitting.length, split.basis)
}
## when bins are given explicitly, get bins based on parameter
else {
if (split.by.time) {
split.time.based.with.bins = TRUE
split.basis = NULL
bins = get.date.from.string(bins)
bins = get.date.string(bins)
## remove sliding windows
sliding.window = FALSE
} else {
## sliding windows do not need to be removed here, as sliding windows and bins
## are not contradicting in activity-based splitting
bins.vector = bins[["vector"]]
bins = bins[["bins"]]
}
bins.labels = head(bins, -1)
## logging
logging::loginfo("Splitting data '%s' into time ranges [%s].",
project.data$get.class.name(), paste(bins, collapse = ", "))
}
bins.date = get.date.from.string(bins)
## construct ranges
bins.ranges = construct.ranges(bins)
names(bins.ranges) = bins.ranges
if ((length(bins.ranges) <= 1) && sliding.window) {
logging::logwarn("Sliding-window approach does not apply for one range or less.")
sliding.window = FALSE
}
if (is.null(project.conf.new)) {
## Clone the project configuration, so that splitting repeatedly does not interfere
## with the same configuration.
project.conf.new = project.data$get.project.conf()$clone()
}
if (!sliding.window || !split.by.time) {
## split data
data.split = parallel::mclapply(data.to.split, function(df.name) {
logging::logdebug("Splitting %s.", df.name)
## identify bins for data
df = data[[df.name]]
df.bins = if (!split.by.time && (df.name == split.basis))
bins.vector
else
findInterval(df[["date"]], bins.date, all.inside = FALSE)