nalgebra-sparse: Move slow tests and imports into 'slow' child module
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
parent
54329146c9
commit
f20e764edc
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@ -1,134 +1,134 @@
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use nalgebra_sparse::proptest::{coo_with_duplicates, coo_no_duplicates};
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use nalgebra::DMatrix;
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use proptest::prelude::*;
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use itertools::Itertools;
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use std::collections::HashSet;
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use std::iter::repeat;
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#[test]
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#[ignore]
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fn coo_no_duplicates_generates_admissible_matrices() {
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//TODO
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}
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#[cfg(feature = "slow-tests")]
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use {
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proptest::test_runner::TestRunner,
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proptest::strategy::ValueTree
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};
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use std::ops::RangeInclusive;
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mod slow {
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use nalgebra_sparse::proptest::{coo_with_duplicates, coo_no_duplicates};
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use nalgebra::DMatrix;
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#[cfg(feature = "slow-tests")]
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fn generate_all_possible_matrices(value_range: RangeInclusive<i32>,
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rows_range: RangeInclusive<usize>,
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cols_range: RangeInclusive<usize>)
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-> HashSet<DMatrix<i32>>
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{
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// Enumerate all possible combinations
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let mut all_combinations = HashSet::new();
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for nrows in rows_range {
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for ncols in cols_range.clone() {
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// For the given number of rows and columns
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let n_values = nrows * ncols;
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use proptest::test_runner::TestRunner;
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use proptest::strategy::ValueTree;
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use itertools::Itertools;
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if n_values == 0 {
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// If we have zero rows or columns, the set of matrices with the given
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// rows and columns is a single element: an empty matrix
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all_combinations.insert(DMatrix::from_row_slice(nrows, ncols, &[]));
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} else {
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// Otherwise, we need to sample all possible matrices.
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// To do this, we generate the values as the (multi) Cartesian product
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// of the value sets. For example, for a 2x2 matrices, we consider
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// all possible 4-element arrays that the matrices can take by
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// considering all elements in the cartesian product
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// V x V x V x V
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// where V is the set of eligible values, e.g. V := -1 ..= 1
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let values_iter = repeat(value_range.clone())
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.take(n_values)
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.multi_cartesian_product();
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for matrix_values in values_iter {
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all_combinations.insert(DMatrix::from_row_slice(nrows, ncols, &matrix_values));
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use proptest::prelude::*;
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use std::collections::HashSet;
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use std::iter::repeat;
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use std::ops::RangeInclusive;
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fn generate_all_possible_matrices(value_range: RangeInclusive<i32>,
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rows_range: RangeInclusive<usize>,
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cols_range: RangeInclusive<usize>)
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-> HashSet<DMatrix<i32>>
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{
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// Enumerate all possible combinations
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let mut all_combinations = HashSet::new();
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for nrows in rows_range {
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for ncols in cols_range.clone() {
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// For the given number of rows and columns
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let n_values = nrows * ncols;
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if n_values == 0 {
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// If we have zero rows or columns, the set of matrices with the given
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// rows and columns is a single element: an empty matrix
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all_combinations.insert(DMatrix::from_row_slice(nrows, ncols, &[]));
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} else {
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// Otherwise, we need to sample all possible matrices.
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// To do this, we generate the values as the (multi) Cartesian product
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// of the value sets. For example, for a 2x2 matrices, we consider
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// all possible 4-element arrays that the matrices can take by
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// considering all elements in the cartesian product
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// V x V x V x V
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// where V is the set of eligible values, e.g. V := -1 ..= 1
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let values_iter = repeat(value_range.clone())
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.take(n_values)
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.multi_cartesian_product();
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for matrix_values in values_iter {
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all_combinations.insert(DMatrix::from_row_slice(nrows, ncols, &matrix_values));
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}
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}
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}
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}
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}
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all_combinations
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}
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#[cfg(feature = "slow-tests")]
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#[test]
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fn coo_no_duplicates_samples_all_admissible_outputs() {
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// Note: This test basically mirrors a similar test for `matrix` in the `nalgebra` repo.
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// Test that the proptest generation covers all possible outputs for a small space of inputs
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// given enough samples.
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// We use a deterministic test runner to make the test "stable".
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let mut runner = TestRunner::deterministic();
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// This number needs to be high enough so that we with high probability sample
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// all possible cases
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let num_generated_matrices = 500000;
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let values = -1..=1;
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let rows = 0..=2;
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let cols = 0..=3;
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let strategy = coo_no_duplicates(values.clone(), rows.clone(), cols.clone(), 2 * 3);
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// Enumerate all possible combinations
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let all_combinations = generate_all_possible_matrices(values, rows, cols);
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let mut visited_combinations = HashSet::new();
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for _ in 0..num_generated_matrices {
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let tree = strategy
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.new_tree(&mut runner)
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.expect("Tree generation should not fail");
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let matrix = tree.current();
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visited_combinations.insert(DMatrix::from(&matrix));
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all_combinations
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}
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assert_eq!(visited_combinations.len(), all_combinations.len());
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assert_eq!(visited_combinations, all_combinations, "Did not sample all possible values.");
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}
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#[cfg(feature = "slow-tests")]
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#[test]
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fn coo_no_duplicates_samples_all_admissible_outputs() {
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// Note: This test basically mirrors a similar test for `matrix` in the `nalgebra` repo.
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#[cfg(feature = "slow-tests")]
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#[test]
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fn coo_with_duplicates_samples_all_admissible_outputs() {
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// This is almost the same as the test for coo_no_duplicates, except that we need
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// a different "success" criterion, since coo_with_duplicates is able to generate
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// matrices with values outside of the value constraints. See below for details.
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// Test that the proptest generation covers all possible outputs for a small space of inputs
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// given enough samples.
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// We use a deterministic test runner to make the test "stable".
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let mut runner = TestRunner::deterministic();
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// We use a deterministic test runner to make the test "stable".
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let mut runner = TestRunner::deterministic();
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// This number needs to be high enough so that we with high probability sample
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// all possible cases
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let num_generated_matrices = 500000;
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// This number needs to be high enough so that we with high probability sample
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// all possible cases
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let num_generated_matrices = 500000;
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let values = -1..=1;
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let rows = 0..=2;
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let cols = 0..=3;
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let strategy = coo_with_duplicates(values.clone(), rows.clone(), cols.clone(), 2 * 3, 2);
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let values = -1..=1;
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let rows = 0..=2;
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let cols = 0..=3;
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let strategy = coo_no_duplicates(values.clone(), rows.clone(), cols.clone(), 2 * 3);
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// Enumerate all possible combinations that fit the constraints
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// (note: this is only a subset of the matrices that can be generated by
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// `coo_with_duplicates`)
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let all_combinations = generate_all_possible_matrices(values, rows, cols);
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// Enumerate all possible combinations
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let all_combinations = generate_all_possible_matrices(values, rows, cols);
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let mut visited_combinations = HashSet::new();
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for _ in 0..num_generated_matrices {
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let tree = strategy
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.new_tree(&mut runner)
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.expect("Tree generation should not fail");
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let matrix = tree.current();
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visited_combinations.insert(DMatrix::from(&matrix));
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let mut visited_combinations = HashSet::new();
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for _ in 0..num_generated_matrices {
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let tree = strategy
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.new_tree(&mut runner)
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.expect("Tree generation should not fail");
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let matrix = tree.current();
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visited_combinations.insert(DMatrix::from(&matrix));
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}
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assert_eq!(visited_combinations.len(), all_combinations.len());
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assert_eq!(visited_combinations, all_combinations, "Did not sample all possible values.");
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}
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// Here we cannot verify that the set of visited combinations is *equal* to
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// all possible outcomes with the given constraints, however the
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// strategy should be able to generate all matrices that fit the constraints.
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// In other words, we need to determine that set of all admissible matrices
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// is contained in the set of visited matrices
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assert!(all_combinations.is_subset(&visited_combinations));
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}
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#[cfg(feature = "slow-tests")]
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#[test]
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fn coo_with_duplicates_samples_all_admissible_outputs() {
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// This is almost the same as the test for coo_no_duplicates, except that we need
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// a different "success" criterion, since coo_with_duplicates is able to generate
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// matrices with values outside of the value constraints. See below for details.
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#[test]
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fn coo_no_duplicates_generates_admissible_matrices() {
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// We use a deterministic test runner to make the test "stable".
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let mut runner = TestRunner::deterministic();
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// This number needs to be high enough so that we with high probability sample
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// all possible cases
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let num_generated_matrices = 500000;
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let values = -1..=1;
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let rows = 0..=2;
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let cols = 0..=3;
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let strategy = coo_with_duplicates(values.clone(), rows.clone(), cols.clone(), 2 * 3, 2);
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// Enumerate all possible combinations that fit the constraints
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// (note: this is only a subset of the matrices that can be generated by
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// `coo_with_duplicates`)
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let all_combinations = generate_all_possible_matrices(values, rows, cols);
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let mut visited_combinations = HashSet::new();
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for _ in 0..num_generated_matrices {
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let tree = strategy
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.new_tree(&mut runner)
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.expect("Tree generation should not fail");
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let matrix = tree.current();
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visited_combinations.insert(DMatrix::from(&matrix));
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}
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// Here we cannot verify that the set of visited combinations is *equal* to
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// all possible outcomes with the given constraints, however the
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// strategy should be able to generate all matrices that fit the constraints.
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// In other words, we need to determine that set of all admissible matrices
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// is contained in the set of visited matrices
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assert!(all_combinations.is_subset(&visited_combinations));
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}
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}
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