M-Labs/nalgebra
3
0
Fork 1
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Improve and test proptest generators

Browse Source 
Due to a bug in proptest, we were required to pull in and modify
parts of proptest::strategy::Shuffle. Once the below PR has been merged
and released on crates.io, we can remove this code.

https://github.com/AltSysrq/proptest/pull/217
This commit is contained in:
Andreas Longva 2021-01-20 16:07:43 +01:00
parent 3eab45d81b
commit 9cd1540496
3 changed files with 281 additions and 26 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

22
nalgebra-sparse/src/proptest.rs
View File

@ -2,6 +2,10 @@
//!
//! TODO: Clarify that this module needs proptest-support feature
// Contains some patched code from proptest that we can remove in the (hopefully near) future.
// See docs in file for more details.
mod proptest_patched;
use crate::coo::CooMatrix;
use proptest::prelude::*;
use proptest::collection::{vec, hash_map, btree_set};
@ -16,12 +20,20 @@ use crate::csc::CscMatrix;
fn dense_row_major_coord_strategy(nrows: usize, ncols: usize, nnz: usize)
-> impl Strategy<Value=Vec<(usize, usize)>>
{
assert!(nnz <= nrows * ncols);
let mut booleans = vec![true; nnz];
booleans.append(&mut vec![false; (nrows * ncols) - nnz]);
// Make sure that exactly `nnz` of the booleans are true
Just(booleans)
// Need to shuffle to make sure they are randomly distributed
.prop_shuffle()
// TODO: We cannot use the below code because of a bug in proptest, see
// https://github.com/AltSysrq/proptest/pull/217
// so for now we're using a patched version of the Shuffle adapter
// (see also docs in `proptest_patched`
// Just(booleans)
// // Need to shuffle to make sure they are randomly distributed
// .prop_shuffle()
proptest_patched::Shuffle(Just(booleans))
.prop_map(move |booleans| {
booleans
.into_iter()
@ -265,8 +277,8 @@ pub fn sparsity_pattern(
// If the required number of nonzeros is sufficiently dense,
// we instead use a dense sampling
dense_row_major_coord_strategy(nmajor, nminor, nnz)
.prop_map(move |triplets| {
let coords = triplets.into_iter();
.prop_map(move |coords| {
let coords = coords.into_iter();
sparsity_pattern_from_row_major_coords(nmajor, nminor, coords)
}).boxed()
}

146
nalgebra-sparse/src/proptest/proptest_patched.rs Normal file
View File

@ -0,0 +1,146 @@
//! Contains a modified implementation of `proptest::strategy::Shuffle`.
//!
//! The current implementation in `proptest` does not generate all permutations, which is
//! problematic for our proptest generators. The issue has been fixed in
//! https://github.com/AltSysrq/proptest/pull/217
//! but it has yet to be merged and released. As soon as this fix makes it into a new release,
//! the modified code here can be removed.
//!
/*!
This code has been copied and adapted from
https://github.com/AltSysrq/proptest/blob/master/proptest/src/strategy/shuffle.rs
The original licensing text is:
//-
// Copyright 2017 Jason Lingle
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.
*/
use proptest::strategy::{Strategy, Shuffleable, NewTree, ValueTree};
use proptest::test_runner::{TestRunner, TestRng};
use std::cell::Cell;
use proptest::num;
use proptest::prelude::Rng;
#[derive(Clone, Debug)]
#[must_use = "strategies do nothing unless used"]
pub struct Shuffle<S>(pub(super) S);
impl<S: Strategy> Strategy for Shuffle<S>
where
S::Value: Shuffleable,
{
type Tree = ShuffleValueTree<S::Tree>;
type Value = S::Value;
fn new_tree(&self, runner: &mut TestRunner) -> NewTree<Self> {
let rng = runner.new_rng();
self.0.new_tree(runner).map(|inner| ShuffleValueTree {
inner,
rng,
dist: Cell::new(None),
simplifying_inner: false,
})
}
}
#[derive(Clone, Debug)]
pub struct ShuffleValueTree<V> {
inner: V,
rng: TestRng,
dist: Cell<Option<num::usize::BinarySearch>>,
simplifying_inner: bool,
}
impl<V: ValueTree> ShuffleValueTree<V>
where
V::Value: Shuffleable,
{
fn init_dist(&self, dflt: usize) -> usize {
if self.dist.get().is_none() {
self.dist.set(Some(num::usize::BinarySearch::new(dflt)));
}
self.dist.get().unwrap().current()
}
fn force_init_dist(&self) {
if self.dist.get().is_none() {
let _ = self.init_dist(self.current().shuffle_len());
}
}
}
impl<V: ValueTree> ValueTree for ShuffleValueTree<V>
where
V::Value: Shuffleable,
{
type Value = V::Value;
fn current(&self) -> V::Value {
let mut value = self.inner.current();
let len = value.shuffle_len();
// The maximum distance to swap elements. This could be larger than
// `value` if `value` has reduced size during shrinking; that's OK,
// since we only use this to filter swaps.
let max_swap = self.init_dist(len);
// If empty collection or all swaps will be filtered out, there's
// nothing to shuffle.
if 0 == len || 0 == max_swap {
return value;
}
let mut rng = self.rng.clone();
for start_index in 0..len - 1 {
// Determine the other index to be swapped, then skip the swap if
// it is too far. This ordering is critical, as it ensures that we
// generate the same sequence of random numbers every time.
// NOTE: The below line is the whole reason for the existence of this adapted code
// We need to be able to swap with the same element, so that some elements remain in
// place rather being swapped
// let end_index = rng.gen_range(start_index + 1, len);
let end_index = rng.gen_range(start_index, len);
if end_index - start_index <= max_swap {
value.shuffle_swap(start_index, end_index);
}
}
value
}
fn simplify(&mut self) -> bool {
if self.simplifying_inner {
self.inner.simplify()
} else {
// Ensure that we've initialised `dist` to *something* to give
// consistent non-panicking behaviour even if called in an
// unexpected sequence.
self.force_init_dist();
if self.dist.get_mut().as_mut().unwrap().simplify() {
true
} else {
self.simplifying_inner = true;
self.inner.simplify()
}
}
}
fn complicate(&mut self) -> bool {
if self.simplifying_inner {
self.inner.complicate()
} else {
self.force_init_dist();
self.dist.get_mut().as_mut().unwrap().complicate()
}
}
}

139
nalgebra-sparse/tests/unit_tests/proptest.rs
View File

@ -6,7 +6,7 @@ fn coo_no_duplicates_generates_admissible_matrices() {
#[cfg(feature = "slow-tests")]
mod slow {
use nalgebra_sparse::proptest::{coo_with_duplicates, coo_no_duplicates};
use nalgebra_sparse::proptest::{coo_with_duplicates, coo_no_duplicates, csr, csc, sparsity_pattern};
use nalgebra::DMatrix;
use proptest::test_runner::TestRunner;
@ -18,6 +18,7 @@ mod slow {
use std::collections::HashSet;
use std::iter::repeat;
use std::ops::RangeInclusive;
use nalgebra_sparse::csr::CsrMatrix;
fn generate_all_possible_matrices(value_range: RangeInclusive<i32>,
rows_range: RangeInclusive<usize>,
@ -73,19 +74,15 @@ mod slow {
let values = -1..=1;
let rows = 0..=2;
let cols = 0..=3;
let strategy = coo_no_duplicates(values.clone(), rows.clone(), cols.clone(), 2 * 3);
let max_nnz = rows.end() * cols.end();
let strategy = coo_no_duplicates(values.clone(), rows.clone(), cols.clone(), max_nnz);
// Enumerate all possible combinations
let all_combinations = generate_all_possible_matrices(values, rows, cols);
let mut visited_combinations = HashSet::new();
for _ in 0..num_generated_matrices {
let tree = strategy
.new_tree(&mut runner)
.expect("Tree generation should not fail");
let matrix = tree.current();
visited_combinations.insert(DMatrix::from(&matrix));
}
let visited_combinations = sample_matrix_output_space(strategy,
&mut runner,
num_generated_matrices);
assert_eq!(visited_combinations.len(), all_combinations.len());
assert_eq!(visited_combinations, all_combinations, "Did not sample all possible values.");
@ -108,21 +105,17 @@ mod slow {
let values = -1..=1;
let rows = 0..=2;
let cols = 0..=3;
let strategy = coo_with_duplicates(values.clone(), rows.clone(), cols.clone(), 2 * 3, 2);
let max_nnz = rows.end() * cols.end();
let strategy = coo_with_duplicates(values.clone(), rows.clone(), cols.clone(), max_nnz, 2);
// Enumerate all possible combinations that fit the constraints
// (note: this is only a subset of the matrices that can be generated by
// `coo_with_duplicates`)
let all_combinations = generate_all_possible_matrices(values, rows, cols);
let mut visited_combinations = HashSet::new();
for _ in 0..num_generated_matrices {
let tree = strategy
.new_tree(&mut runner)
.expect("Tree generation should not fail");
let matrix = tree.current();
visited_combinations.insert(DMatrix::from(&matrix));
}
let visited_combinations = sample_matrix_output_space(strategy,
&mut runner,
num_generated_matrices);
// Here we cannot verify that the set of visited combinations is *equal* to
// all possible outcomes with the given constraints, however the
@ -131,6 +124,110 @@ mod slow {
// is contained in the set of visited matrices
assert!(all_combinations.is_subset(&visited_combinations));
}
}
// TODO: Tests for csr, csc and sparsity_pattern strategies
#[cfg(feature = "slow-tests")]
#[test]
fn csr_samples_all_admissible_outputs() {
// We use a deterministic test runner to make the test "stable".
let mut runner = TestRunner::deterministic();
// This number needs to be high enough so that we with high probability sample
// all possible cases
let num_generated_matrices = 500000;
let values = -1..=1;
let rows = 0..=2;
let cols = 0..=3;
let max_nnz = rows.end() * cols.end();
let strategy = csr(values.clone(), rows.clone(), cols.clone(), max_nnz);
let all_combinations = generate_all_possible_matrices(values, rows, cols);
let visited_combinations = sample_matrix_output_space(strategy,
&mut runner,
num_generated_matrices);
assert_eq!(visited_combinations.len(), all_combinations.len());
assert_eq!(visited_combinations, all_combinations, "Did not sample all possible values.");
}
#[cfg(feature = "slow-tests")]
#[test]
fn csc_samples_all_admissible_outputs() {
// We use a deterministic test runner to make the test "stable".
let mut runner = TestRunner::deterministic();
// This number needs to be high enough so that we with high probability sample
// all possible cases
let num_generated_matrices = 500000;
let values = -1..=1;
let rows = 0..=2;
let cols = 0..=3;
let max_nnz = rows.end() * cols.end();
let strategy = csc(values.clone(), rows.clone(), cols.clone(), max_nnz);
let all_combinations = generate_all_possible_matrices(values, rows, cols);
let visited_combinations = sample_matrix_output_space(strategy,
&mut runner,
num_generated_matrices);
assert_eq!(visited_combinations.len(), all_combinations.len());
assert_eq!(visited_combinations, all_combinations, "Did not sample all possible values.");
}
#[cfg(feature = "slow-tests")]
#[test]
fn sparsity_pattern_samples_all_admissible_outputs() {
let mut runner = TestRunner::deterministic();
let num_generated_patterns = 50000;
let major_dims = 0..=2;
let minor_dims = 0..=3;
let max_nnz = major_dims.end() * minor_dims.end();
let strategy = sparsity_pattern(major_dims.clone(), minor_dims.clone(), max_nnz);
let visited_patterns: HashSet<_> = sample_strategy(strategy, &mut runner)
.take(num_generated_patterns)
.map(|pattern| {
// We represent patterns as dense matrices with 1 if an entry is occupied,
// 0 otherwise
let values = vec![1; pattern.nnz()];
CsrMatrix::try_from_pattern_and_values(pattern, values).unwrap()
})
.map(|csr| DMatrix::from(&csr))
.collect();
let all_possible_patterns = generate_all_possible_matrices(0..=1, major_dims, minor_dims);
assert_eq!(visited_patterns.len(), all_possible_patterns.len());
assert_eq!(visited_patterns, all_possible_patterns);
}
fn sample_matrix_output_space<S>(strategy: S,
runner: &mut TestRunner,
num_samples: usize)
-> HashSet<DMatrix<i32>>
where
S: Strategy,
DMatrix<i32>: for<'b> From<&'b S::Value>
{
sample_strategy(strategy, runner)
.take(num_samples)
.map(|matrix| DMatrix::from(&matrix))
.collect()
}
fn sample_strategy<'a, S: 'a + Strategy>(strategy: S, runner: &'a mut TestRunner)
-> impl 'a + Iterator<Item=S::Value> {
repeat(()).map(move |_| {
let tree = strategy
.new_tree(runner)
.expect("Tree generation should not fail");
let value = tree.current();
value
})
}
}