nalgebra/nalgebra-sparse/tests/unit_tests/proptest.rs

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#[test]
#[ignore]
fn coo_no_duplicates_generates_admissible_matrices() {
//TODO
}
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#[cfg(feature = "slow-tests")]
mod slow {
use nalgebra::DMatrix;
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use nalgebra_sparse::proptest::{
coo_no_duplicates, coo_with_duplicates, csc, csr, sparsity_pattern,
};
use itertools::Itertools;
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use proptest::strategy::ValueTree;
use proptest::test_runner::TestRunner;
use proptest::prelude::*;
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use nalgebra_sparse::csr::CsrMatrix;
use std::collections::HashSet;
use std::iter::repeat;
use std::ops::RangeInclusive;
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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 {
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all_combinations.insert(DMatrix::from_row_slice(
nrows,
ncols,
&matrix_values,
));
}
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}
}
}
all_combinations
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}
#[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.
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// Test that the proptest generation covers all possible outputs for a small space of inputs
// given enough samples.
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// We use a deterministic test runner to make the test "stable".
let mut runner = TestRunner::deterministic();
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// This number needs to be high enough so that we with high probability sample
// all possible cases
let num_generated_matrices = 500000;
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let values = -1..=1;
let rows = 0..=2;
let cols = 0..=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);
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let visited_combinations =
sample_matrix_output_space(strategy, &mut runner, num_generated_matrices);
assert_eq!(visited_combinations.len(), all_combinations.len());
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assert_eq!(
visited_combinations, all_combinations,
"Did not sample all possible values."
);
}
#[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.
// 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 = 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);
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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
// 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 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);
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let visited_combinations =
sample_matrix_output_space(strategy, &mut runner, num_generated_matrices);
assert_eq!(visited_combinations.len(), all_combinations.len());
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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);
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let visited_combinations =
sample_matrix_output_space(strategy, &mut runner, num_generated_matrices);
assert_eq!(visited_combinations.len(), all_combinations.len());
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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);
}
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fn sample_matrix_output_space<S>(
strategy: S,
runner: &mut TestRunner,
num_samples: usize,
) -> HashSet<DMatrix<i32>>
where
S: Strategy,
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DMatrix<i32>: for<'b> From<&'b S::Value>,
{
sample_strategy(strategy, runner)
.take(num_samples)
.map(|matrix| DMatrix::from(&matrix))
.collect()
}
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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
})
}
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}