Move nalgebra proptest slow tests into `slow` submodule
This way it's easier to keep track of what imports are only necessary for the slow tests.
This commit is contained in:
Andreas Longva
parent
f909638bf4
commit
402de4d045
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@ -6,13 +6,6 @@ use proptest::prelude::*;
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use proptest::strategy::ValueTree;
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use proptest::test_runner::TestRunner;
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#[cfg(feature = "slow-tests")]
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use {
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itertools::Itertools,
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std::iter::repeat,
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std::collections::HashSet,
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};
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/// Generate a proptest that tests that all matrices generated with the
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/// provided rows and columns conform to the constraints defined by the
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/// input.
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@ -96,62 +89,6 @@ proptest! {
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fn ensure_arbitrary_test_compiles_dvector(_: DVector<i32>) {}
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}
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#[cfg(feature = "slow-tests")]
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#[test]
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fn matrix_samples_all_possible_outputs() {
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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 = 200000;
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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 = matrix(values.clone(), rows.clone(), cols.clone());
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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 {
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for ncols in cols.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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for matrix_values in repeat(values.clone()).take(n_values).multi_cartesian_product() {
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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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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(matrix.clone());
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}
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assert_eq!(visited_combinations, all_combinations, "Did not sample all possible values.");
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}
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#[test]
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fn matrix_shrinking_satisfies_constraints() {
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// We use a deterministic test runner to make the test "stable".
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@ -206,3 +143,77 @@ fn matrix_shrinking_satisfies_constraints() {
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maybeprintln!("========================== (end of generation process)");
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}
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#[cfg(feature = "slow-tests")]
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mod slow {
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use super::*;
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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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#[cfg(feature = "slow-tests")]
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#[test]
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fn matrix_samples_all_possible_outputs() {
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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 = 200000;
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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 = matrix(values.clone(), rows.clone(), cols.clone());
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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 {
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for ncols in cols.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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for matrix_values in repeat(values.clone())
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.take(n_values)
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.multi_cartesian_product()
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{
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all_combinations.insert(DMatrix::from_row_slice(
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nrows,
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ncols,
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&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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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(matrix.clone());
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}
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assert_eq!(
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visited_combinations, all_combinations,
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"Did not sample all possible values."
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);
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}
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}
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