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Diffstat (limited to 'tests/gen_counts_test.R')
| -rwxr-xr-x | tests/gen_counts_test.R | 109 |
1 files changed, 109 insertions, 0 deletions
diff --git a/tests/gen_counts_test.R b/tests/gen_counts_test.R new file mode 100755 index 0000000..87e6e8b --- /dev/null +++ b/tests/gen_counts_test.R @@ -0,0 +1,109 @@ +#!/usr/bin/env Rscript +# +# Copyright 2014 Google Inc. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +library(RUnit) +library(Matrix) # for sparse matrices + +source('tests/gen_counts.R') + +TestGenerateCounts <- function() { + report_params <- list(k = 4, m = 2) # 2 cohorts, 4 bits each + map <- Matrix(0, nrow = 8, ncol = 3, sparse = TRUE) # 3 possible values + map[1,] <- c(1, 0, 0) + map[2,] <- c(0, 1, 0) + map[3,] <- c(0, 0, 1) + map[4,] <- c(1, 1, 1) # 4th bit of the first cohort gets signal from all + map[5,] <- c(0, 0, 1) # 1st bit of the second cohort gets signal from v3 + + colnames(map) <- c('v1', 'v2', 'v3') + + partition <- c(3, 2, 1) * 10000 + v <- 100 # reports per client + + noise0 <- list(p = 0, q = 1, f = 0) # no noise at all + counts0 <- GenerateCounts(c(report_params, noise0), map, partition, v) + + checkEqualsNumeric(sum(counts0[1,2:4]), counts0[1,1]) + checkEqualsNumeric(counts0[1,5], counts0[1,1]) + checkEqualsNumeric(partition[3] * v, counts0[1,4] + counts0[2,2]) + checkEqualsNumeric(sum(partition) * v, counts0[1,1] + counts0[2,1]) + + pvalues <- chisq.test(counts0[,1] / v, p = c(.5, .5))$p.value + for(i in 2:4) + pvalues <- c(pvalues, + chisq.test( + c(counts0[1,i] / v, partition[i - 1] - counts0[1,i] / v), + p = c(.5, .5))$p.value) + + noise1 <- list(p = .5, q = .5, f = 0) # truly random IRRs + counts1 <- GenerateCounts(c(report_params, noise1), map, partition, v) + + for(i in 2:5) + for(j in 1:2) + pvalues <- c(pvalues, + chisq.test(c(counts1[j,1] - counts1[j,i], counts1[j,i]), + p = c(.5, .5))$p.value) + + noise2 <- list(p = 0, q = 1, f = 1.0) # truly random PRRs + counts2 <- GenerateCounts(c(report_params, noise2), map, partition, v) + + checkEqualsNumeric(0, max(counts2 %% v)) # all entries must be divisible by v + + counts2 <- counts2 / v + + for(i in 2:5) + for(j in 1:2) + pvalues <- c(pvalues, + chisq.test(c(counts2[j,1] - counts2[j,i], counts2[j,i]), + p = c(.5, .5))$p.value) + + checkTrue(min(pvalues) > 1E-9, "Chi-squared test failed") +} + +TestRandomPartition <- function() { + + p1 <- RandomPartition(total = 100, dgeom(0:999, prob = .1)) + p2 <- RandomPartition(total = 1000, dnorm(1:1000, mean = 500, sd = 1000 / 6)) + p3 <- RandomPartition(total = 10000, dunif(1:1000)) + + # Totals must check out. + checkEqualsNumeric(100, sum(p1)) + checkEqualsNumeric(1000, sum(p2)) + checkEqualsNumeric(10000, sum(p3)) + + # Initialize the weights vector to 1 0 1 0 1 0 ... + weights <- rep(c(1, 0), 100) + + p4 <- RandomPartition(total = 10000, weights) + + # Check that all mass is allocated to non-zero weights. + checkEqualsNumeric(10000, sum(p4[weights == 1])) + checkTrue(all(p4[weights == 0] == 0)) + + p5 <- RandomPartition(total = 1000000, c(1, 2, 3, 4)) + p.value <- chisq.test(p5, p = c(.1, .2, .3, .4))$p.value + + # Apply the chi squared test and fail if p.value is too high or too low. + # Probability of failure is 2 * 1E-9, which should never happen. + checkTrue(p.value > 1E-9) +} + +TestAll <- function(){ + TestRandomPartition() + TestGenerateCounts() +} + +TestAll()
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