173 lines
6.3 KiB
R
173 lines
6.3 KiB
R
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#!/usr/bin/env Rscript
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#
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# Copyright 2015 Google Inc. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Simulates inputs on which association analysis can be run.
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# Currently assoc_sim.R only supports 2 variables but can
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# be easily extended to support more.
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#
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# Usage:
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# $ ./assoc_sim.R -n 1000
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# Inputs: uvals, params, reports, map, num, unif
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# see how options are parsed below for more information
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# Outputs:
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# reports.csv file containing reports
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# map_{1, 2, ...}.csv file(s) containing maps of variables
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library("optparse")
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options(stringsAsFactors = FALSE)
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if(!interactive()) {
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option_list <- list(
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make_option(c("--uvals", "-v"), default = "uvals.csv",
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help = "Filename for list of values over which
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distributions are simulated. The file is a list of
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comma-separated strings each line of which refers
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to a new variable."),
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make_option(c("--params", "-p"), default = "params.csv",
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help = "Filename for RAPPOR parameters"),
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make_option(c("--reports", "-r"), default = "reports.csv",
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help = "Filename for reports"),
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make_option(c("--map", "-m"), default = "map",
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help = "Filename *prefix* for map(s)"),
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make_option(c("--num", "-n"), default = 1e05,
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help = "Number of reports"),
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make_option(c("--unif", "-u"), default = FALSE,
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help = "Run simulation with uniform distribution")
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)
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opts <- parse_args(OptionParser(option_list = option_list))
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}
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source("../analysis/R/encode.R")
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source("../analysis/R/decode.R")
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source("../analysis/R/simulation.R")
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source("../analysis/R/read_input.R")
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source("../analysis/R/association.R")
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# Read unique values of reports from a csv file
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# Inputs: filename. The file is expected to contain two rows of strings
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# (one for each variable):
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# "google.com", "apple.com", ...
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# "ssl", "nossl", ...
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# Returns: a list containing strings
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GetUniqueValsFromFile <- function(filename) {
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contents <- read.csv(filename, header = FALSE)
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# Expect 2 rows of unique vals
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if(nrow(contents) != 2) {
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stop(paste("Unique vals file", filename, "expected to have
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two rows of strings."))
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}
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# Removes superfluous "" entries if the lists of unique values
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# differ in length
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strip_empty <- function(vec) {
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vec[!vec %in% c("")]
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}
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list(var1 = strip_empty(as.vector(t(contents[1,]))),
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var2 = strip_empty(as.vector(t(contents[2,]))))
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}
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# Simulate correlated reports and write into reportsfile
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# Inputs: N = number of reports
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# uvals = list containing a list of unique values
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# params = list with RAPPOR parameters
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# unif = whether to replace poisson with uniform
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# mapfile = file to write maps into (with .csv suffixes)
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# reportsfile = file to write reports into (with .csv suffix)
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SimulateReports <- function(N, uvals, params, unif,
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mapfile, reportsfile) {
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# Compute true distribution
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m <- params$m
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if (unif) {
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# Draw uniformly from 1 to 10
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v1_samples <- as.integer(runif(N, 1, 10))
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} else {
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# Draw from a Poisson random variable
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v1_samples <- rpois(N, 1) + rep(1, N)
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}
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# Pr[var2 = N + 1 | var1 = N] = 0.5
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# Pr[var2 = N | var1 = N] = 0.5
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v2_samples <- v1_samples + sample(c(0, 1), N, replace = TRUE)
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tmp_samples <- list(v1_samples, v2_samples)
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# Function to pad strings to uval_vec if sample_vec has
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# larger support than the number of strings in uval_vec
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# For e.g., if samples have support {1, 2, 3, 4, ...} and uvals
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# only have "value1", "value2", and "value3", samples now
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# over support {"value1", "value2", "value3", "str4", ...}
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PadStrings <- function(sample_vec, uval_vec) {
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if (max(sample_vec) > length(uval_vec)) {
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# Padding uvals to required length
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len <- length(uval_vec)
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max_of_samples <- max(sample_vec)
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uval_vec[(len + 1):max_of_samples] <- apply(
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as.matrix((len + 1):max_of_samples),
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1,
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function(i) sprintf("str%d", i))
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}
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uval_vec
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}
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# Pad and update uvals
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uvals <- lapply(1:2, function(i) PadStrings(tmp_samples[[i]],
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uvals[[i]]))
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# Replace integers in tmp_samples with actual sample strings
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samples <- lapply(1:2, function(i) uvals[[i]][tmp_samples[[i]]])
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# Randomly assign cohorts in each dimension
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cohorts <- sample(1:m, N, replace = TRUE)
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# Create and write map into mapfile_1.csv and mapfile_2.csv
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map <- lapply(uvals, function(u) CreateMap(u, params))
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write.table(map[[1]]$map_pos, file = paste(mapfile, "_1.csv", sep = ""),
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sep = ",", col.names = FALSE, na = "", quote = FALSE)
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write.table(map[[2]]$map_pos, file = paste(mapfile, "_2.csv", sep = ""),
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sep = ",", col.names = FALSE, na = "", quote = FALSE)
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# Write reports into a csv file
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# Format:
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# cohort, bloom filter var1, bloom filter var2
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reports <- lapply(1:2, function(i)
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EncodeAll(samples[[i]], cohorts, map[[i]]$map, params))
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# Organize cohorts and reports into format
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write_matrix <- cbind(as.matrix(cohorts),
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as.matrix(lapply(reports[[1]],
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function(x) paste(x, collapse = ""))),
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as.matrix(lapply(reports[[2]],
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function(x) paste(x, collapse = ""))))
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write.table(write_matrix, file = reportsfile, quote = FALSE,
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row.names = FALSE, col.names = FALSE, sep = ",")
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}
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main <- function(opts) {
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ptm <- proc.time()
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uvals <- GetUniqueValsFromFile(opts$uvals)
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params <- ReadParameterFile(opts$params)
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SimulateReports(opts$num, uvals, params, opts$unif, # inputs
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opts$map, opts$reports) # outputs
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print("PROC.TIME")
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print(proc.time() - ptm)
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}
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if(!interactive()) {
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main(opts)
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}
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