finalizare 1.0
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# Copyright (c) 2015, Leland McInnes
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# All rights reserved.
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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# 1. Redistributions of source code must retain the above copyright notice,
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# this list of conditions and the following disclaimer.
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# 2. Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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# 3. Neither the name of the copyright holder nor the names of its contributors
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# may be used to endorse or promote products derived from this software without
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# specific prior written permission.
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
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# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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# POSSIBILITY OF SUCH DAMAGE.
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from ...utils._typedefs cimport intp_t, float64_t, uint8_t
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cimport numpy as cnp
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# This corresponds to the scipy.cluster.hierarchy format
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ctypedef packed struct HIERARCHY_t:
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intp_t left_node
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intp_t right_node
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float64_t value
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intp_t cluster_size
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# Effectively an edgelist encoding a parent/child pair, along with a value and
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# the corresponding cluster_size in each row providing a tree structure.
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ctypedef packed struct CONDENSED_t:
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intp_t parent
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intp_t child
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float64_t value
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intp_t cluster_size
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cdef extern from "numpy/arrayobject.h":
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intp_t * PyArray_SHAPE(cnp.PyArrayObject *)
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import numpy as np
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import pytest
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from sklearn.cluster._hdbscan._reachability import mutual_reachability_graph
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from sklearn.utils._testing import (
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_convert_container,
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assert_allclose,
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)
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def test_mutual_reachability_graph_error_sparse_format():
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"""Check that we raise an error if the sparse format is not CSR."""
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rng = np.random.RandomState(0)
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X = rng.randn(10, 10)
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X = X.T @ X
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np.fill_diagonal(X, 0.0)
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X = _convert_container(X, "sparse_csc")
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err_msg = "Only sparse CSR matrices are supported"
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with pytest.raises(ValueError, match=err_msg):
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mutual_reachability_graph(X)
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@pytest.mark.parametrize("array_type", ["array", "sparse_csr"])
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def test_mutual_reachability_graph_inplace(array_type):
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"""Check that the operation is happening inplace."""
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rng = np.random.RandomState(0)
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X = rng.randn(10, 10)
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X = X.T @ X
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np.fill_diagonal(X, 0.0)
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X = _convert_container(X, array_type)
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mr_graph = mutual_reachability_graph(X)
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assert id(mr_graph) == id(X)
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def test_mutual_reachability_graph_equivalence_dense_sparse():
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"""Check that we get the same results for dense and sparse implementation."""
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rng = np.random.RandomState(0)
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X = rng.randn(5, 5)
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X_dense = X.T @ X
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X_sparse = _convert_container(X_dense, "sparse_csr")
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mr_graph_dense = mutual_reachability_graph(X_dense, min_samples=3)
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mr_graph_sparse = mutual_reachability_graph(X_sparse, min_samples=3)
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assert_allclose(mr_graph_dense, mr_graph_sparse.toarray())
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@pytest.mark.parametrize("array_type", ["array", "sparse_csr"])
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@pytest.mark.parametrize("dtype", [np.float32, np.float64])
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def test_mutual_reachability_graph_preserve_dtype(array_type, dtype):
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"""Check that the computation preserve dtype thanks to fused types."""
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rng = np.random.RandomState(0)
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X = rng.randn(10, 10)
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X = (X.T @ X).astype(dtype)
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np.fill_diagonal(X, 0.0)
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X = _convert_container(X, array_type)
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assert X.dtype == dtype
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mr_graph = mutual_reachability_graph(X)
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assert mr_graph.dtype == dtype
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