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nalgebra-sparse: Move slow tests and imports into 'slow' child module

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This way it's easier to prevent accidental unused imports when
running tests without the slow-tests feature.
This commit is contained in:
Andreas Longva 2020-11-23 10:16:18 +01:00
parent 54329146c9
commit f20e764edc
1 changed files with 114 additions and 114 deletions
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228
nalgebra-sparse/tests/unit_tests/proptest.rs
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@ -1,134 +1,134 @@
use nalgebra_sparse::proptest::{coo_with_duplicates, coo_no_duplicates};
use nalgebra::DMatrix;
use proptest::prelude::*;
use itertools::Itertools;
use std::collections::HashSet;
use std::iter::repeat;
#[test]
#[ignore]
fn coo_no_duplicates_generates_admissible_matrices() {
//TODO
}
#[cfg(feature = "slow-tests")]
use {
proptest::test_runner::TestRunner,
proptest::strategy::ValueTree
};
use std::ops::RangeInclusive;
mod slow {
use nalgebra_sparse::proptest::{coo_with_duplicates, coo_no_duplicates};
use nalgebra::DMatrix;
#[cfg(feature = "slow-tests")]
fn generate_all_possible_matrices(value_range: RangeInclusive<i32>,
rows_range: RangeInclusive<usize>,
cols_range: RangeInclusive<usize>)
-> HashSet<DMatrix<i32>>
{
// Enumerate all possible combinations
let mut all_combinations = HashSet::new();
for nrows in rows_range {
for ncols in cols_range.clone() {
// For the given number of rows and columns
let n_values = nrows * ncols;
use proptest::test_runner::TestRunner;
use proptest::strategy::ValueTree;
use itertools::Itertools;
if n_values == 0 {
// If we have zero rows or columns, the set of matrices with the given
// rows and columns is a single element: an empty matrix
all_combinations.insert(DMatrix::from_row_slice(nrows, ncols, &[]));
} else {
// Otherwise, we need to sample all possible matrices.
// To do this, we generate the values as the (multi) Cartesian product
// of the value sets. For example, for a 2x2 matrices, we consider
// all possible 4-element arrays that the matrices can take by
// considering all elements in the cartesian product
// V x V x V x V
// where V is the set of eligible values, e.g. V := -1 ..= 1
let values_iter = repeat(value_range.clone())
.take(n_values)
.multi_cartesian_product();
for matrix_values in values_iter {
all_combinations.insert(DMatrix::from_row_slice(nrows, ncols, &matrix_values));
use proptest::prelude::*;
use std::collections::HashSet;
use std::iter::repeat;
use std::ops::RangeInclusive;
fn generate_all_possible_matrices(value_range: RangeInclusive<i32>,
rows_range: RangeInclusive<usize>,
cols_range: RangeInclusive<usize>)
-> HashSet<DMatrix<i32>>
{
// Enumerate all possible combinations
let mut all_combinations = HashSet::new();
for nrows in rows_range {
for ncols in cols_range.clone() {
// For the given number of rows and columns
let n_values = nrows * ncols;
if n_values == 0 {
// If we have zero rows or columns, the set of matrices with the given
// rows and columns is a single element: an empty matrix
all_combinations.insert(DMatrix::from_row_slice(nrows, ncols, &[]));
} else {
// Otherwise, we need to sample all possible matrices.
// To do this, we generate the values as the (multi) Cartesian product
// of the value sets. For example, for a 2x2 matrices, we consider
// all possible 4-element arrays that the matrices can take by
// considering all elements in the cartesian product
// V x V x V x V
// where V is the set of eligible values, e.g. V := -1 ..= 1
let values_iter = repeat(value_range.clone())
.take(n_values)
.multi_cartesian_product();
for matrix_values in values_iter {
all_combinations.insert(DMatrix::from_row_slice(nrows, ncols, &matrix_values));
}
}
}
}
}
all_combinations
}
#[cfg(feature = "slow-tests")]
#[test]
fn coo_no_duplicates_samples_all_admissible_outputs() {
// Note: This test basically mirrors a similar test for `matrix` in the `nalgebra` repo.
// Test that the proptest generation covers all possible outputs for a small space of inputs
// given enough samples.
// 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 strategy = coo_no_duplicates(values.clone(), rows.clone(), cols.clone(), 2 * 3);
// 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));
all_combinations
}
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 coo_no_duplicates_samples_all_admissible_outputs() {
// Note: This test basically mirrors a similar test for `matrix` in the `nalgebra` repo.
#[cfg(feature = "slow-tests")]
#[test]
fn coo_with_duplicates_samples_all_admissible_outputs() {
// This is almost the same as the test for coo_no_duplicates, except that we need
// a different "success" criterion, since coo_with_duplicates is able to generate
// matrices with values outside of the value constraints. See below for details.
// Test that the proptest generation covers all possible outputs for a small space of inputs
// given enough samples.
// We use a deterministic test runner to make the test "stable".
let mut runner = TestRunner::deterministic();
// 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;
// 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 strategy = coo_with_duplicates(values.clone(), rows.clone(), cols.clone(), 2 * 3, 2);
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);
// 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);
// 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 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));
}
assert_eq!(visited_combinations.len(), all_combinations.len());
assert_eq!(visited_combinations, all_combinations, "Did not sample all possible values.");
}
// Here we cannot verify that the set of visited combinations is *equal* to
// all possible outcomes with the given constraints, however the
// strategy should be able to generate all matrices that fit the constraints.
// In other words, we need to determine that set of all admissible matrices
// is contained in the set of visited matrices
assert!(all_combinations.is_subset(&visited_combinations));
}
#[cfg(feature = "slow-tests")]
#[test]
fn coo_with_duplicates_samples_all_admissible_outputs() {
// This is almost the same as the test for coo_no_duplicates, except that we need
// a different "success" criterion, since coo_with_duplicates is able to generate
// matrices with values outside of the value constraints. See below for details.
#[test]
fn coo_no_duplicates_generates_admissible_matrices() {
// 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 strategy = coo_with_duplicates(values.clone(), rows.clone(), cols.clone(), 2 * 3, 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));
}
// Here we cannot verify that the set of visited combinations is *equal* to
// all possible outcomes with the given constraints, however the
// strategy should be able to generate all matrices that fit the constraints.
// In other words, we need to determine that set of all admissible matrices
// is contained in the set of visited matrices
assert!(all_combinations.is_subset(&visited_combinations));
}
}