rappor/regtest.sh

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#!/bin/bash
usage() {
echo "
Run end-to-end tests in parallel.
Usage:
./regtest.sh <function name>
At the end, it will print an HTML summary.
Three main functions are
run [[<pattern> [<num> [<fast>]] - run tests matching <pattern> in
parallel, each <num> times. The fast
mode (T/F) shortcuts generation of
reports.
run-seq [<pattern> [<num> [<fast>]] - ditto, except that tests are run
sequentially
run-all [<num>] - run all tests, in parallel, each <num> times
Examples:
$ ./regtest.sh run-seq unif-small-typical # Sequential run, matches 1 case
$ ./regtest.sh run-seq unif-small- 3 F # Sequential, each test is run three
times, using slow generation
$ ./regtest.sh run unif- # Parallel run, matches multiple cases
$ ./regtest.sh run unif- 5 # Parallel run, matches multiple cases, each test
is run 5 times
$ ./regtest.sh run-all # Run all tests once
The <pattern> argument is a regex in 'grep -E' format. (Detail: Don't
use $ in the pattern, since it matches the whole spec line and not just the
test case name.) The number of processors used in a parallel run is one less
than the number of CPUs on the machine.
Future speedups:
- Reuse the same input -- come up with naming scheme based on params
- Reuse the same maps -- ditto, rappor library can cache it
"
}
set -o nounset
set -o pipefail
set -o errexit
. util.sh
readonly THIS_DIR=$(dirname $0)
readonly REPO_ROOT=$THIS_DIR
readonly CLIENT_DIR=$REPO_ROOT/client/python
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
# subdirs are in _tmp/$impl, which shouldn't overlap with anything else in _tmp
readonly REGTEST_BASE_DIR=_tmp
# All the Python tools need this
export PYTHONPATH=$CLIENT_DIR
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
print-unique-values() {
local num_unique_values=$1
seq 1 $num_unique_values | awk '{print "v" $1}'
}
# Add some more candidates here. We hope these are estimated at 0.
# e.g. if add_start=51, and num_additional is 20, show v51-v70
more-candidates() {
local last_true=$1
local num_additional=$2
local begin
local end
begin=$(expr $last_true + 1)
end=$(expr $last_true + $num_additional)
seq $begin $end | awk '{print "v" $1}'
}
# Args:
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
# unique_values: File of unique true values
# last_true: last true input, e.g. 50 if we generated "v1" .. "v50".
# num_additional: additional candidates to generate (starting at 'last_true')
# to_remove: Regex of true values to omit from the candidates list, or the
# string 'NONE' if none should be. (Our values look like 'v1', 'v2', etc. so
# there isn't any ambiguity.)
print-candidates() {
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
local unique_values=$1
local last_true=$2
local num_additional=$3
local to_remove=$4
if test $to_remove = NONE; then
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
cat $unique_values # include all true inputs
else
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
egrep -v $to_remove $unique_values # remove some true inputs
fi
more-candidates $last_true $num_additional
}
# Generate a single test case, specified by a line of the test spec.
# This is a helper function for _run_tests().
_setup-one-case() {
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
local impl=$1
shift # impl is not part of the spec; the next 13 params are
local test_case=$1
# input params
local dist=$2
local num_unique_values=$3
local num_clients=$4
local values_per_client=$5
# RAPPOR params
local num_bits=$6
local num_hashes=$7
local num_cohorts=$8
local p=$9
local q=${10} # need curly braces to get the 10th arg
local f=${11}
# map params
local num_additional=${12}
local to_remove=${13}
banner 'Setting up parameters and candidate files for '$test_case
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
local case_dir=$REGTEST_BASE_DIR/$impl/$test_case
mkdir --verbose -p $case_dir
# Save the "spec"
echo "$@" > $case_dir/spec.txt
local params_path=$case_dir/case_params.csv
echo 'k,h,m,p,q,f' > $params_path
echo "$num_bits,$num_hashes,$num_cohorts,$p,$q,$f" >> $params_path
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
print-unique-values $num_unique_values > $case_dir/case_unique_values.txt
local true_map_path=$case_dir/case_true_map.csv
bin/hash_candidates.py \
$params_path \
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
< $case_dir/case_unique_values.txt \
> $true_map_path
# banner "Constructing candidates"
print-candidates \
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
$case_dir/case_unique_values.txt $num_unique_values \
$num_additional "$to_remove" \
> $case_dir/case_candidates.txt
# banner "Hashing candidates to get 'map'"
bin/hash_candidates.py \
$params_path \
< $case_dir/case_candidates.txt \
> $case_dir/case_map.csv
}
# Run a single test instance, specified by <test_name, instance_num>.
# This is a helper function for _run_tests().
_run-one-instance() {
local test_case=$1
local test_instance=$2
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
local impl=$3
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
local case_dir=$REGTEST_BASE_DIR/$impl/$test_case
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
read -r \
case_name distr num_unique_values num_clients values_per_client \
num_bits num_hashes num_cohorts p q f \
num_additional to_remove \
< $case_dir/spec.txt
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
local instance_dir=$case_dir/$test_instance
mkdir --verbose -p $instance_dir
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
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# NOTE: This is a nested case statement on the same variable ($impl). If
# it's fast_counts, we just run gen_counts.R. If it's anything else, then we
# generate raw reports, run them through the appropriate client, and then sum
# the bits.
case $impl in
fast_counts)
local params_file=$case_dir/case_params.csv
local true_map_file=$case_dir/case_true_map.csv
banner "Generating counts directly (gen_counts.R)"
# Writes _counts.csv and _hist.csv
tests/gen_counts.R $distr $num_clients $values_per_client $params_file \
$true_map_file "$instance_dir/case"
;;
*)
banner "Generating reports (gen_reports.R)"
# the TRUE_VALUES_PATH environment variable can be used to avoid
# generating new values every time. NOTE: You are responsible for making
# sure the params match!
local true_values=${TRUE_VALUES_PATH:-}
if test -z "$true_values"; then
true_values=$instance_dir/case_true_values.csv
tests/gen_true_values.R $distr $num_unique_values $num_clients \
$values_per_client $num_cohorts \
$true_values
else
# TEMP hack: Make it visible to plot.
# TODO: Fix compare_dist.R
ln -s -f --verbose \
$PWD/$true_values \
$instance_dir/case_true_values.csv
fi
case $impl in
python)
banner "Running RAPPOR Python client"
# Writes encoded "out" file, true histogram, true inputs to
# $instance_dir.
time tests/rappor_sim.py \
--num-bits $num_bits \
--num-hashes $num_hashes \
--num-cohorts $num_cohorts \
-p $p \
-q $q \
-f $f \
< $true_values \
> "$instance_dir/case_reports.csv"
;;
cpp)
banner "Running RAPPOR C++ client (see rappor_sim.log for errors)"
time client/cpp/_tmp/rappor_sim \
$num_bits \
$num_hashes \
$num_cohorts \
$p \
$q \
$f \
< $true_values \
> "$instance_dir/case_reports.csv" \
2>"$instance_dir/rappor_sim.log"
;;
*)
log "Invalid impl $impl (should be one of fast_counts|python|cpp)"
exit 1
;;
esac
banner "Summing RAPPOR IRR bits to get 'counts'"
bin/sum_bits.py \
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
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$case_dir/case_params.csv \
< $instance_dir/case_reports.csv \
> $instance_dir/case_counts.csv
;;
esac
local out_dir=${instance_dir}_report
mkdir --verbose -p $out_dir
# Currently, the summary file shows and aggregates timing of the inference
# engine, which excludes R's loading time and reading of the (possibly
# substantial) map file. Timing below is more inclusive.
TIMEFORMAT='Running compare_dist.R took %R seconds'
time {
# Input prefix, output dir
tests/compare_dist.R -t "Test case: $test_case (instance $test_instance)" \
"$case_dir/case" "$instance_dir/case" $out_dir
}
}
# Like _run-once-case, but log to a file.
_run-one-instance-logged() {
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
local test_case=$1
local test_instance=$2
local impl=$3
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
local log_dir=$REGTEST_BASE_DIR/$impl/$test_case/${test_instance}_report
mkdir --verbose -p $log_dir
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
log "Started '$test_case' (instance $test_instance) -- logging to $log_dir/log.txt"
_run-one-instance "$@" >$log_dir/log.txt 2>&1 \
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
&& log "Test case $test_case (instance $test_instance) done" \
|| log "Test case $test_case (instance $test_instance) failed"
}
make-summary() {
local dir=$1
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
local impl=$2
local filename=results.html
tests/make_summary.py $dir $dir/rows.html
pushd $dir >/dev/null
cat ../../tests/regtest.html \
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
| sed -e '/__TABLE_ROWS__/ r rows.html' -e "s/_IMPL_/$impl/g" \
2015-04-02 00:03:04 +03:00
> $filename
popd >/dev/null
2015-04-02 00:03:04 +03:00
log "Wrote $dir/$filename"
log "URL: file://$PWD/$dir/$filename"
}
test-error() {
2015-04-01 09:33:29 +03:00
local spec_regex=${1:-}
log "Some test cases failed"
if test -n "$spec_regex"; then
log "(Perhaps none matched pattern '$spec_regex')"
fi
# don't quit just yet
# exit 1
}
# Assuming the spec file, write a list of test case names (first column) with
# the instance ids (second column), where instance ids run from 1 to $1.
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
# Third column is impl.
_setup-test-instances() {
local instances=$1
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
local impl=$2
while read line; do
2015-04-18 00:23:20 +03:00
for i in $(seq 1 $instances); do
read case_name _ <<< $line # extract the first token
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
echo $case_name $i $impl
done
done
}
# Print the default number of parallel processes, which is max(#CPUs - 1, 1)
default-processes() {
processors=$(grep -c ^processor /proc/cpuinfo || echo 4) # Linux-specific
if test $processors -gt 1; then # leave one CPU for the OS
processors=$(expr $processors - 1)
fi
echo $processors
}
# Args:
# spec_gen: A program to execute to generate the spec.
# spec_regex: A pattern selecting the subset of tests to run
# instances: A number of times each test case is run
# parallel: Whether the tests are run in parallel (T/F). Sequential
# runs log to the console; parallel runs log to files.
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
# impl: one of fast_counts, python, cpp
_run-tests() {
local spec_gen=$1
local spec_regex="$2" # grep -E format on the spec, can be empty
local instances=$3
local parallel=$4
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
local impl=$5
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
local regtest_dir=$REGTEST_BASE_DIR/$impl
rm -r -f --verbose $regtest_dir
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
mkdir --verbose -p $regtest_dir
local func
local processors
if test $parallel = F; then
func=_run-one-instance # output to the console
processors=1
else
func=_run-one-instance-logged
# Let the user override with MAX_PROC, in case they don't have enough
# memory.
processors=${MAX_PROC:-$(default-processes)}
log "Running $processors parallel processes"
fi
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
local cases_list=$regtest_dir/test-cases.txt
# Need -- for regexes that start with -
$spec_gen | grep -E -- "$spec_regex" > $cases_list
# Generate parameters for all test cases.
cat $cases_list \
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
| xargs -l -P $processors -- $0 _setup-one-case $impl \
|| test-error
log "Done generating parameters for all test cases"
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
local instances_list=$regtest_dir/test-instances.txt
_setup-test-instances $instances $impl < $cases_list > $instances_list
cat $instances_list \
| xargs -l -P $processors -- $0 $func || test-error
log "Done running all test instances"
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
make-summary $regtest_dir $impl
}
# used for most tests
readonly REGTEST_SPEC=tests/regtest_spec.py
# Run tests sequentially. NOTE: called by demo.sh.
run-seq() {
local spec_regex=${1:-'^r-'} # grep -E format on the spec
local instances=${2:-1}
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
local impl=${3:-fast_counts}
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
time _run-tests $REGTEST_SPEC $spec_regex $instances F $impl
}
# Run tests in parallel
run() {
local spec_regex=${1:-'^r-'} # grep -E format on the spec
local instances=${2:-1}
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
local impl=${3:-fast_counts}
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
time _run-tests $REGTEST_SPEC $spec_regex $instances T $impl
}
# Run tests in parallel (7+ minutes on 8 cores)
run-all() {
local instances=${1:-1}
log "Running all tests. Can take a while."
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
time _run-tests $REGTEST_SPEC '^r-' $instances T fast_counts
}
run-user() {
local spec_regex=${1:-}
local instances=${2:-1}
local parallel=T # too much memory
Make the Python simulation into a Python client library. - It can be used in applications, and follows the same choices as the C++ (and Java) client libraries: MD5 to choose the bloom filter bits, and HMAC-SHA256 with client secret for the PRR. (rappor.py) - hash_candidates.py and rappor_sim.py use the rappor.get_bloom_bits API, which is more efficient since it calls the hash function once instead of 'h' times. - Make the randomness interface a simpler "IrrRand" interface, which has 2 implementations: pure Python and C. (The PRR always uses the same randomness.) - regtest.sh: Instead of a boolean for 'fast_counts', we have 3 modes: Python client, C++ client, and 'fast_counts'. The same tests can be run against each implementation. The results are put in different directories, so they don't overwite each other. - Rename gen_reports.R to gen_true_values.R (since the "reports" are typically the data transformed by RAPPOR). - Change it to output the client and assign the cohort like this: client,cohort,value c1,1,v26 c1,1,v27 c2,2,v8 c2,2,v9 The cohort is stable per client, but the value is different. - Add unit test - tests/rappor_sim.py: New test protocol. Read true_values CSV input. The CSV output contains the bloom filter, PRR, and IRR for easier debugging. - compare_dist.R loads the true values and computes the histograms, rather than rappor_sim.py having to do it. This makes it easier to use the test harness with C++ and other clients. - demo.sh: quickly run equivalent tests for each mode. - regtest.sh: show the implementation tested
2015-07-14 01:56:28 +03:00
time _run-tests tests/user_spec.py "$spec_regex" $instances $parallel \
fast_counts
}
# Use stable true values
compare-python-cpp() {
local num_unique_values=100
local num_clients=10000
local values_per_client=10
local num_cohorts=64
local true_values=$REGTEST_BASE_DIR/stable_true_values.csv
tests/gen_true_values.R \
exp $num_unique_values $num_clients $values_per_client $num_cohorts \
$true_values
wc -l $true_values
# Run Python and C++ simulation on the same input
./build.sh cpp-client
TRUE_VALUES_PATH=$true_values \
./regtest.sh run-seq '^demo3' 1 python
TRUE_VALUES_PATH=$true_values \
./regtest.sh run-seq '^demo3' 1 cpp
head _tmp/{python,cpp}/demo3/1/case_reports.csv
}
if [ $# -eq 0 ]
then
usage
else
"$@"
fi