8000 cln deprecations (#24478) · thomasjpfan/scikit-learn@d2c3e65 · GitHub
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cln deprecations (scikit-learn#24478)
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‎sklearn/tests/test_docstrings.py

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"sklearn.utils.fixes.threadpool_limits",
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"sklearn.utils.gen_batches",
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"sklearn.utils.gen_even_slices",
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"sklearn.utils.graph.graph_shortest_path",
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"sklearn.utils.graph.single_source_shortest_path_length",
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"sklearn.utils.is_scalar_nan",
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"sklearn.utils.metaestimators.available_if",

‎sklearn/utils/graph.py

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import numpy as np
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from scipy import sparse
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from .deprecation import deprecated
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from ..metrics.pairwise import pairwise_distances
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return seen # return all path lengths as dictionary
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@deprecated(
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"`graph_shortest_path` is deprecated in 1.0 (renaming of 0.25) and will "
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"be removed in 1.2. Use `scipy.sparse.csgraph.shortest_path` instead."
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)
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def graph_shortest_path(dist_matrix, directed=True, method="auto"):
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"""Shortest-path graph search on a positive directed or undirected graph.
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Parameters
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----------
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dist_matrix : arraylike or sparse matrix, shape = (N,N)
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Array of positive distances.
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If vertex i is connected to vertex j, then dist_matrix[i,j] gives
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the distance between the vertices.
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If vertex i is not connected to vertex j, then dist_matrix[i,j] = 0
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directed : boolean
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if True, then find the shortest path on a directed graph: only
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progress from a point to its neighbors, not the other way around.
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if False, then find the shortest path on an undirected graph: the
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algorithm can progress from a point to its neighbors and vice versa.
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method : {'auto', 'FW', 'D'}, default='auto'
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method to use. Options are
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'auto' : attempt to choose the best method for the current problem
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'FW' : Floyd-Warshall algorithm. O[N^3]
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'D' : Dijkstra's algorithm with Fibonacci stacks. O[(k+log(N))N^2]
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Returns
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-------
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G : np.ndarray, float, shape = [N,N]
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G[i,j] gives the shortest distance from point i to point j
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along the graph.
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Notes
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-----
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As currently implemented, Dijkstra's algorithm does not work for
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graphs with direction-dependent distances when directed == False.
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i.e., if dist_matrix[i,j] and dist_matrix[j,i] are not equal and
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both are nonzero, method='D' will not necessarily yield the correct
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result.
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Also, these routines have not been tested for graphs with negative
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distances. Negative distances can lead to infinite cycles that must
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be handled by specialized algorithms.
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"""
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return sparse.csgraph.shortest_path(dist_matrix, method=method, directed=directed)
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def _fix_connected_components(
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X,
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graph,

‎sklearn/utils/tests/test_shortest_path.py

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from collections import defaultdict
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import numpy as np
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import pytest
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from numpy.testing import assert_array_almost_equal
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from sklearn.utils.graph import graph_shortest_path, single_source_shortest_path_length
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# FIXME: to be removed in 1.2
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def test_graph_shortest_path_deprecation():
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dist_matrix = generate_graph(20)
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with pytest.warns(FutureWarning, match="deprecated"):
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_ = graph_shortest_path(dist_matrix)
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from sklearn.utils.graph import single_source_shortest_path_length
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def floyd_warshall_slow(graph, directed=False):
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return dist_matrix
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@pytest.mark.filterwarnings("ignore:Function graph_shortest_path is deprecated")
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def test_floyd_warshall():
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dist_matrix = generate_graph(20)
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for directed in (True, False):
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graph_FW = graph_shortest_path(dist_matrix, directed, "FW")
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graph_py = floyd_warshall_slow(dist_matrix.copy(), directed)
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assert_array_almost_equal(graph_FW, graph_py)
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@pytest.mark.filterwarnings("ignore:Function graph_shortest_path is deprecated")
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def test_dijkstra():
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dist_matrix = generate_graph(20)
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for directed in (True, False):
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graph_D = graph_shortest_path(dist_matrix, directed, "D")
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graph_py = floyd_warshall_slow(dist_matrix.copy(), directed)
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assert_array_almost_equal(graph_D, graph_py)
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def test_shortest_path():
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dist_matrix = generate_graph(20)
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# We compare path length and not costs (-> set distances to 0 or 1)
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for j in range(graph_py[i].shape[0]):
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assert_array_almost_equal(dist_dict[j], graph_py[i, j])
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@pytest.mark.filterwarnings("ignore:Function graph_shortest_path is deprecated")
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def test_dijkstra_bug_fix():
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X = np.array([[0.0, 0.0, 4.0], [1.0, 0.0, 2.0], [0.0, 5.0, 0.0]])
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dist_FW = graph_shortest_path(X, directed=False, method="FW")
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dist_D = graph_shortest_path(X, directed=False, method="D")
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assert_array_almost_equal(dist_D, dist_FW)

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