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import numpy as np
import pytest
from sklearn.cluster._hdbscan._reachability import mutual_reachability_graph
from sklearn.utils._testing import (
_convert_container,
assert_allclose,
)
def test_mutual_reachability_graph_error_sparse_format():
"""Check that we raise an error if the sparse format is not CSR."""
rng = np.random.RandomState(0)
X = rng.randn(10, 10)
X = X.T @ X
np.fill_diagonal(X, 0.0)
X = _convert_container(X, "sparse_csc")
err_msg = "Only sparse CSR matrices are supported"
with pytest.raises(ValueError, match=err_msg):
mutual_reachability_graph(X)
@pytest.mark.parametrize("array_type", ["array", "sparse_csr"])
def test_mutual_reachability_graph_inplace(array_type):
"""Check that the operation is happening inplace."""
rng = np.random.RandomState(0)
X = rng.randn(10, 10)
X = X.T @ X
np.fill_diagonal(X, 0.0)
X = _convert_container(X, array_type)
mr_graph = mutual_reachability_graph(X)
assert id(mr_graph) == id(X)
def test_mutual_reachability_graph_equivalence_dense_sparse():
"""Check that we get the same results for dense and sparse implementation."""
rng = np.random.RandomState(0)
X = rng.randn(5, 5)
X_dense = X.T @ X
X_sparse = _convert_container(X_dense, "sparse_csr")
mr_graph_dense = mutual_reachability_graph(X_dense, min_samples=3)
mr_graph_sparse = mutual_reachability_graph(X_sparse, min_samples=3)
assert_allclose(mr_graph_dense, mr_graph_sparse.toarray())
@pytest.mark.parametrize("array_type", ["array", "sparse_csr"])
@pytest.mark.parametrize("dtype", [np.float32, np.float64])
def test_mutual_reachability_graph_preserve_dtype(array_type, dtype):
"""Check that the computation preserve dtype thanks to fused types."""
rng = np.random.RandomState(0)
X = rng.randn(10, 10)
X = (X.T @ X).astype(dtype)
np.fill_diagonal(X, 0.0)
X = _convert_container(X, array_type)
assert X.dtype == dtype
mr_graph = mutual_reachability_graph(X)
assert mr_graph.dtype == dtype
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