1238 lines
49 KiB
Python
1238 lines
49 KiB
Python
#
|
|
# Author: Damian Eads
|
|
# Date: April 17, 2008
|
|
#
|
|
# Copyright (C) 2008 Damian Eads
|
|
#
|
|
# Redistribution and use in source and binary forms, with or without
|
|
# modification, are permitted provided that the following conditions
|
|
# are met:
|
|
#
|
|
# 1. Redistributions of source code must retain the above copyright
|
|
# notice, this list of conditions and the following disclaimer.
|
|
#
|
|
# 2. Redistributions in binary form must reproduce the above
|
|
# copyright notice, this list of conditions and the following
|
|
# disclaimer in the documentation and/or other materials provided
|
|
# with the distribution.
|
|
#
|
|
# 3. The name of the author may not be used to endorse or promote
|
|
# products derived from this software without specific prior
|
|
# written permission.
|
|
#
|
|
# THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``AS IS'' AND ANY EXPRESS
|
|
# OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
|
|
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
|
|
# ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY
|
|
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
|
|
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE
|
|
# GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
|
|
# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
|
|
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
|
|
# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
|
|
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
|
import numpy as np
|
|
from numpy.testing import assert_allclose, assert_equal, assert_array_equal, assert_
|
|
import pytest
|
|
from pytest import raises as assert_raises
|
|
|
|
from scipy.cluster.hierarchy import (
|
|
ClusterWarning, linkage, from_mlab_linkage, to_mlab_linkage,
|
|
num_obs_linkage, inconsistent, cophenet, fclusterdata, fcluster,
|
|
is_isomorphic, single, ward, leaders,
|
|
correspond, is_monotonic, maxdists, maxinconsts, maxRstat,
|
|
is_valid_linkage, is_valid_im, to_tree, leaves_list, dendrogram,
|
|
set_link_color_palette, cut_tree, optimal_leaf_ordering,
|
|
_order_cluster_tree, _hierarchy, _EUCLIDEAN_METHODS, _LINKAGE_METHODS)
|
|
from scipy.cluster._hierarchy import Heap
|
|
from scipy.spatial.distance import pdist
|
|
from scipy._lib._array_api import (eager_warns, make_xp_test_case,
|
|
xp_assert_close, xp_assert_equal)
|
|
import scipy._lib.array_api_extra as xpx
|
|
|
|
from threading import Lock
|
|
|
|
from . import hierarchy_test_data
|
|
|
|
class eager:
|
|
# Bypass xpx.testing.lazy_xp_function when calling
|
|
# these functions from this namespace
|
|
is_valid_im = is_valid_im
|
|
is_valid_linkage = is_valid_linkage
|
|
|
|
|
|
# Matplotlib is not a scipy dependency but is optionally used in dendrogram, so
|
|
# check if it's available
|
|
try:
|
|
import matplotlib
|
|
# and set the backend to be Agg (no gui)
|
|
matplotlib.use('Agg')
|
|
# before importing pyplot
|
|
import matplotlib.pyplot as plt
|
|
have_matplotlib = True
|
|
except Exception:
|
|
have_matplotlib = False
|
|
|
|
skip_xp_backends = pytest.mark.skip_xp_backends
|
|
|
|
|
|
@make_xp_test_case(linkage)
|
|
class TestLinkage:
|
|
|
|
@skip_xp_backends("jax.numpy", reason="Can't raise inside jax.pure_callback")
|
|
def test_linkage_non_finite_elements_in_distance_matrix(self, xp):
|
|
# Tests linkage(Y) where Y contains a non-finite element (e.g. NaN or Inf).
|
|
# Exception expected.
|
|
y = xp.asarray([xp.nan] + [0.0]*5)
|
|
assert_raises(ValueError, linkage, y)
|
|
|
|
def test_linkage_empty_distance_matrix(self, xp):
|
|
# Tests linkage(Y) where Y is a 0x4 linkage matrix. Exception expected.
|
|
y = xp.zeros((0,))
|
|
assert_raises(ValueError, linkage, y)
|
|
|
|
def test_linkage_tdist(self, xp):
|
|
for method in ['single', 'complete', 'average', 'weighted']:
|
|
self.check_linkage_tdist(method, xp)
|
|
|
|
def check_linkage_tdist(self, method, xp):
|
|
# Tests linkage(Y, method) on the tdist data set.
|
|
Z = linkage(xp.asarray(hierarchy_test_data.ytdist), method)
|
|
expectedZ = getattr(hierarchy_test_data, 'linkage_ytdist_' + method)
|
|
xp_assert_close(Z, xp.asarray(expectedZ), atol=1e-10)
|
|
|
|
def test_linkage_X(self, xp):
|
|
for method in ['centroid', 'median', 'ward']:
|
|
self.check_linkage_q(method, xp)
|
|
|
|
def check_linkage_q(self, method, xp):
|
|
# Tests linkage(Y, method) on the Q data set.
|
|
Z = linkage(xp.asarray(hierarchy_test_data.X), method)
|
|
expectedZ = getattr(hierarchy_test_data, 'linkage_X_' + method)
|
|
xp_assert_close(Z, xp.asarray(expectedZ), atol=1e-06)
|
|
|
|
X = xp.asarray(hierarchy_test_data.X)
|
|
y = pdist(X, metric="euclidean")
|
|
Z = linkage(y, method)
|
|
xp_assert_close(Z, xp.asarray(expectedZ), atol=1e-06)
|
|
|
|
def test_compare_with_trivial(self, xp):
|
|
rng = np.random.RandomState(0)
|
|
n = 20
|
|
X = rng.rand(n, 2)
|
|
d = pdist(X)
|
|
|
|
for method, code in _LINKAGE_METHODS.items():
|
|
Z_trivial = _hierarchy.linkage(d, n, code)
|
|
Z = linkage(xp.asarray(d), method)
|
|
xp_assert_close(Z, xp.asarray(Z_trivial), rtol=1e-14, atol=1e-15)
|
|
|
|
def test_optimal_leaf_ordering(self, xp):
|
|
Z = linkage(xp.asarray(hierarchy_test_data.ytdist), optimal_ordering=True)
|
|
expectedZ = getattr(hierarchy_test_data, 'linkage_ytdist_single_olo')
|
|
xp_assert_close(Z, xp.asarray(expectedZ), atol=1e-10)
|
|
|
|
@pytest.mark.parametrize("method,expect", [
|
|
('single', [[0, 1, 1.41421356, 2],
|
|
[2, 3, 1.41421356, 3]]),
|
|
('complete', [[0, 1, 1.41421356, 2],
|
|
[2, 3, 2.82842712, 3]]),
|
|
('average', [[0, 1, 1.41421356, 2],
|
|
[2, 3, 2.12132034, 3]]),
|
|
('weighted', [[0, 1, 1.41421356, 2],
|
|
[2, 3, 2.12132034, 3]]),
|
|
('centroid', [[0, 1, 1.41421356, 2],
|
|
[2, 3, 2.12132034, 3]]),
|
|
('median', [[0, 1, 1.41421356, 2],
|
|
[2, 3, 2.12132034, 3]]),
|
|
('ward', [[0, 1, 1.41421356, 2],
|
|
[2, 3, 2.44948974, 3]]),
|
|
])
|
|
def test_linkage_ties(self, method, expect, xp):
|
|
X = xp.asarray([[-1, -1], [0, 0], [1, 1]])
|
|
Z = linkage(X, method=method)
|
|
expect = xp.asarray(expect, dtype=xp.float64)
|
|
xp_assert_close(Z, expect, atol=1e-06)
|
|
|
|
def test_unsupported_uncondensed_distance_matrix_linkage_warning(self, xp):
|
|
X = xp.asarray([[0, 1], [1, 0]])
|
|
with eager_warns(X, ClusterWarning):
|
|
linkage(X)
|
|
|
|
@pytest.mark.parametrize("method", _EUCLIDEAN_METHODS)
|
|
def test_euclidean_linkage_value_error(self, method, xp):
|
|
X = xp.asarray([[1, 1], [1, 1]])
|
|
with pytest.raises(ValueError):
|
|
linkage(X, method=method, metric='cityblock')
|
|
|
|
def test_2x2_linkage(self, xp):
|
|
Z1 = linkage(xp.asarray([1]), method='single', metric='euclidean')
|
|
Z2 = linkage(xp.asarray([[0, 1], [0, 0]]), method='single', metric='euclidean')
|
|
xp_assert_close(Z1, Z2, rtol=1e-15)
|
|
|
|
@skip_xp_backends("jax.numpy", reason="Can't raise inside jax.pure_callback")
|
|
def test_centroid_neg_distance(self, xp):
|
|
# gh-21011
|
|
values = xp.asarray([0, 0, -1])
|
|
with pytest.raises(ValueError):
|
|
# This is just checking that this doesn't crash
|
|
linkage(values, method='centroid')
|
|
|
|
|
|
@make_xp_test_case(inconsistent)
|
|
class TestInconsistent:
|
|
|
|
def test_inconsistent_tdist(self, xp):
|
|
for depth in hierarchy_test_data.inconsistent_ytdist:
|
|
self.check_inconsistent_tdist(depth, xp)
|
|
|
|
def check_inconsistent_tdist(self, depth, xp):
|
|
Z = xp.asarray(hierarchy_test_data.linkage_ytdist_single)
|
|
xp_assert_close(inconsistent(Z, depth),
|
|
xp.asarray(hierarchy_test_data.inconsistent_ytdist[depth]))
|
|
|
|
|
|
@make_xp_test_case(cophenet)
|
|
class TestCopheneticDistance:
|
|
|
|
def test_linkage_cophenet_tdist_Z(self, xp):
|
|
# Tests cophenet(Z) on tdist data set.
|
|
expectedM = xp.asarray([268, 295, 255, 255, 295, 295, 268, 268, 295, 295,
|
|
295, 138, 219, 295, 295])
|
|
Z = xp.asarray(hierarchy_test_data.linkage_ytdist_single)
|
|
M = cophenet(Z)
|
|
xp_assert_close(M, xp.asarray(expectedM, dtype=xp.float64), atol=1e-10)
|
|
|
|
def test_linkage_cophenet_tdist_Z_Y(self, xp):
|
|
# Tests cophenet(Z, Y) on tdist data set.
|
|
Z = xp.asarray(hierarchy_test_data.linkage_ytdist_single)
|
|
(c, M) = cophenet(Z, xp.asarray(hierarchy_test_data.ytdist))
|
|
expectedM = xp.asarray([268, 295, 255, 255, 295, 295, 268, 268, 295, 295,
|
|
295, 138, 219, 295, 295], dtype=xp.float64)
|
|
expectedc = xp.asarray(0.639931296433393415057366837573, dtype=xp.float64)[()]
|
|
xp_assert_close(c, expectedc, atol=1e-10)
|
|
xp_assert_close(M, expectedM, atol=1e-10)
|
|
|
|
@skip_xp_backends("jax.numpy", reason="Can't raise inside jax.pure_callback")
|
|
def test_gh_22183(self, xp):
|
|
# check for lack of segfault
|
|
# (out of bounds memory access)
|
|
# and correct interception of
|
|
# invalid linkage matrix
|
|
arr=[[0.0, 1.0, 1.0, 2.0],
|
|
[2.0, 12.0, 1.0, 3.0],
|
|
[3.0, 4.0, 1.0, 2.0],
|
|
[5.0, 14.0, 1.0, 3.0],
|
|
[6.0, 7.0, 1.0, 2.0],
|
|
[8.0, 16.0, 1.0, 3.0],
|
|
[9.0, 10.0, 1.0, 2.0],
|
|
[11.0, 18.0, 1.0, 3.0],
|
|
[13.0, 15.0, 2.0, 6.0],
|
|
[17.0, 20.0, 2.0, 32.0],
|
|
[19.0, 21.0, 2.0, 12.0]]
|
|
with pytest.raises(ValueError, match="excessive observations"):
|
|
cophenet(xp.asarray(arr))
|
|
|
|
|
|
@make_xp_test_case(from_mlab_linkage, to_mlab_linkage)
|
|
class TestMLabLinkageConversion:
|
|
|
|
def test_mlab_linkage_conversion_empty(self, xp):
|
|
# Tests from/to_mlab_linkage on empty linkage array.
|
|
X = xp.asarray([], dtype=xp.float64)
|
|
xp_assert_equal(from_mlab_linkage(X), X)
|
|
xp_assert_equal(to_mlab_linkage(X), X)
|
|
|
|
def test_mlab_linkage_conversion_single_row(self, xp):
|
|
# Tests from/to_mlab_linkage on linkage array with single row.
|
|
Z = xp.asarray([[0., 1., 3., 2.]])
|
|
Zm = xp.asarray([[1, 2, 3]])
|
|
xp_assert_close(from_mlab_linkage(Zm), xp.asarray(Z, dtype=xp.float64),
|
|
rtol=1e-15)
|
|
xp_assert_close(to_mlab_linkage(Z), xp.asarray(Zm, dtype=xp.float64),
|
|
rtol=1e-15)
|
|
|
|
def test_mlab_linkage_conversion_multiple_rows(self, xp):
|
|
# Tests from/to_mlab_linkage on linkage array with multiple rows.
|
|
Zm = xp.asarray([[3, 6, 138], [4, 5, 219],
|
|
[1, 8, 255], [2, 9, 268], [7, 10, 295]])
|
|
Z = xp.asarray([[2., 5., 138., 2.],
|
|
[3., 4., 219., 2.],
|
|
[0., 7., 255., 3.],
|
|
[1., 8., 268., 4.],
|
|
[6., 9., 295., 6.]],
|
|
dtype=xp.float64)
|
|
xp_assert_close(from_mlab_linkage(Zm), Z, rtol=1e-15)
|
|
xp_assert_close(to_mlab_linkage(Z), xp.asarray(Zm, dtype=xp.float64),
|
|
rtol=1e-15)
|
|
|
|
|
|
@make_xp_test_case(fclusterdata)
|
|
class TestFclusterData:
|
|
|
|
@make_xp_test_case(is_isomorphic)
|
|
@pytest.mark.parametrize("criterion,t",
|
|
[("inconsistent", t) for t in hierarchy_test_data.fcluster_inconsistent]
|
|
+ [("distance", t) for t in hierarchy_test_data.fcluster_distance]
|
|
+ [("maxclust", t) for t in hierarchy_test_data.fcluster_maxclust]
|
|
)
|
|
def test_fclusterdata(self, t, criterion, xp):
|
|
# Tests fclusterdata(X, criterion=criterion, t=t) on a random 3-cluster data set
|
|
expectedT = xp.asarray(getattr(hierarchy_test_data, 'fcluster_' + criterion)[t])
|
|
X = xp.asarray(hierarchy_test_data.Q_X)
|
|
T = fclusterdata(X, criterion=criterion, t=t)
|
|
assert is_isomorphic(T, expectedT)
|
|
|
|
|
|
@make_xp_test_case(fcluster)
|
|
class TestFcluster:
|
|
|
|
@make_xp_test_case(single, is_isomorphic)
|
|
@pytest.mark.parametrize("criterion,t",
|
|
[("inconsistent", t) for t in hierarchy_test_data.fcluster_inconsistent]
|
|
+ [("distance", t) for t in hierarchy_test_data.fcluster_distance]
|
|
+ [("maxclust", t) for t in hierarchy_test_data.fcluster_maxclust]
|
|
)
|
|
def test_fcluster(self, t, criterion, xp):
|
|
# Tests fcluster(Z, criterion=criterion, t=t) on a random 3-cluster data set.
|
|
expectedT = xp.asarray(getattr(hierarchy_test_data, 'fcluster_' + criterion)[t])
|
|
Z = single(xp.asarray(hierarchy_test_data.Q_X))
|
|
T = fcluster(Z, criterion=criterion, t=t)
|
|
assert_(is_isomorphic(T, expectedT))
|
|
|
|
@make_xp_test_case(single, is_isomorphic, maxdists)
|
|
@pytest.mark.parametrize("t", hierarchy_test_data.fcluster_distance)
|
|
def test_fcluster_monocrit(self, t, xp):
|
|
expectedT = xp.asarray(hierarchy_test_data.fcluster_distance[t])
|
|
Z = single(xp.asarray(hierarchy_test_data.Q_X))
|
|
T = fcluster(Z, t, criterion='monocrit', monocrit=maxdists(Z))
|
|
assert_(is_isomorphic(T, expectedT))
|
|
|
|
@make_xp_test_case(single, is_isomorphic, maxdists)
|
|
@pytest.mark.parametrize("t", hierarchy_test_data.fcluster_maxclust)
|
|
def test_fcluster_maxclust_monocrit(self, t, xp):
|
|
expectedT = xp.asarray(hierarchy_test_data.fcluster_maxclust[t])
|
|
Z = single(xp.asarray(hierarchy_test_data.Q_X))
|
|
T = fcluster(Z, t, criterion='maxclust_monocrit', monocrit=maxdists(Z))
|
|
assert_(is_isomorphic(T, expectedT))
|
|
|
|
@make_xp_test_case(single)
|
|
def test_fcluster_maxclust_gh_12651(self, xp):
|
|
y = xp.asarray([[1], [4], [5]])
|
|
Z = single(y)
|
|
assert_array_equal(fcluster(Z, t=1, criterion="maxclust"),
|
|
xp.asarray([1, 1, 1]))
|
|
assert_array_equal(fcluster(Z, t=2, criterion="maxclust"),
|
|
xp.asarray([2, 1, 1]))
|
|
assert_array_equal(fcluster(Z, t=3, criterion="maxclust"),
|
|
xp.asarray([1, 2, 3]))
|
|
assert_array_equal(fcluster(Z, t=5, criterion="maxclust"),
|
|
xp.asarray([1, 2, 3]))
|
|
|
|
|
|
@make_xp_test_case(leaders)
|
|
class TestLeaders:
|
|
|
|
def test_leaders_single(self, xp):
|
|
# Tests leaders using a flat clustering generated by single linkage.
|
|
X = hierarchy_test_data.Q_X
|
|
Y = pdist(X)
|
|
Z = linkage(Y)
|
|
T = fcluster(Z, criterion='maxclust', t=3)
|
|
Z = xp.asarray(Z)
|
|
T = xp.asarray(T, dtype=xp.int32)
|
|
L = leaders(Z, T)
|
|
expect = xp.asarray([53, 55, 56, 2, 3, 1], dtype=xp.int32)
|
|
xp_assert_close(xp.concat(L), expect, rtol=1e-15)
|
|
|
|
|
|
@make_xp_test_case(is_isomorphic)
|
|
class TestIsIsomorphic:
|
|
|
|
def test_array_like(self):
|
|
assert is_isomorphic([1, 1, 1], [2, 2, 2])
|
|
assert is_isomorphic([], [])
|
|
|
|
def test_is_isomorphic_1(self, xp):
|
|
# Tests is_isomorphic on test case #1 (one flat cluster, different labellings)
|
|
a = xp.asarray([1, 1, 1])
|
|
b = xp.asarray([2, 2, 2])
|
|
assert is_isomorphic(a, b)
|
|
assert is_isomorphic(b, a)
|
|
|
|
def test_is_isomorphic_2(self, xp):
|
|
# Tests is_isomorphic on test case #2 (two flat clusters, different labelings)
|
|
a = xp.asarray([1, 7, 1])
|
|
b = xp.asarray([2, 3, 2])
|
|
assert is_isomorphic(a, b)
|
|
assert is_isomorphic(b, a)
|
|
|
|
def test_is_isomorphic_3(self, xp):
|
|
# Tests is_isomorphic on test case #3 (no flat clusters)
|
|
a = xp.asarray([])
|
|
b = xp.asarray([])
|
|
assert is_isomorphic(a, b)
|
|
|
|
def test_is_isomorphic_4A(self, xp):
|
|
# Tests is_isomorphic on test case #4A
|
|
# (3 flat clusters, different labelings, isomorphic)
|
|
a = xp.asarray([1, 2, 3])
|
|
b = xp.asarray([1, 3, 2])
|
|
assert is_isomorphic(a, b)
|
|
assert is_isomorphic(b, a)
|
|
|
|
def test_is_isomorphic_4B(self, xp):
|
|
# Tests is_isomorphic on test case #4B
|
|
# (3 flat clusters, different labelings, nonisomorphic)
|
|
a = xp.asarray([1, 2, 3, 3])
|
|
b = xp.asarray([1, 3, 2, 3])
|
|
assert not is_isomorphic(a, b)
|
|
assert not is_isomorphic(b, a)
|
|
|
|
def test_is_isomorphic_4C(self, xp):
|
|
# Tests is_isomorphic on test case #4C
|
|
# (3 flat clusters, different labelings, isomorphic)
|
|
a = xp.asarray([7, 2, 3])
|
|
b = xp.asarray([6, 3, 2])
|
|
assert is_isomorphic(a, b)
|
|
assert is_isomorphic(b, a)
|
|
|
|
@pytest.mark.parametrize("nclusters", [2, 3, 5])
|
|
def test_is_isomorphic_5(self, nclusters, xp):
|
|
# Tests is_isomorphic on test case #5 (1000 observations, 2/3/5 random
|
|
# clusters, random permutation of the labeling).
|
|
self.is_isomorphic_randperm(1000, nclusters, xp=xp)
|
|
|
|
@pytest.mark.parametrize("nclusters", [2, 3, 5])
|
|
def test_is_isomorphic_6(self, nclusters, xp):
|
|
# Tests is_isomorphic on test case #5A (1000 observations, 2/3/5 random
|
|
# clusters, random permutation of the labeling, slightly
|
|
# nonisomorphic.)
|
|
self.is_isomorphic_randperm(1000, nclusters, True, 5, xp=xp)
|
|
|
|
def test_is_isomorphic_7(self, xp):
|
|
# Regression test for gh-6271
|
|
a = xp.asarray([1, 2, 3])
|
|
b = xp.asarray([1, 1, 1])
|
|
assert not is_isomorphic(a, b)
|
|
|
|
def is_isomorphic_randperm(self, nobs, nclusters, noniso=False, nerrors=0, *, xp):
|
|
for _ in range(3):
|
|
a = (np.random.rand(nobs) * nclusters).astype(int)
|
|
b = np.zeros(a.size, dtype=int)
|
|
P = np.random.permutation(nclusters)
|
|
for i in range(0, a.shape[0]):
|
|
b[i] = P[a[i]]
|
|
if noniso:
|
|
Q = np.random.permutation(nobs)
|
|
b[Q[0:nerrors]] += 1
|
|
b[Q[0:nerrors]] %= nclusters
|
|
a = xp.asarray(a)
|
|
b = xp.asarray(b)
|
|
assert is_isomorphic(a, b) == (not noniso)
|
|
assert is_isomorphic(b, a) == (not noniso)
|
|
|
|
|
|
@make_xp_test_case(is_valid_linkage)
|
|
class TestIsValidLinkage:
|
|
|
|
@pytest.mark.parametrize("nrow, ncol, valid", [(2, 5, False), (2, 3, False),
|
|
(1, 4, True), (2, 4, True)])
|
|
def test_is_valid_linkage_various_size(self, nrow, ncol, valid, xp):
|
|
# Tests is_valid_linkage(Z) with linkage matrices of various sizes
|
|
Z = xp.asarray([[0, 1, 3.0, 2, 5],
|
|
[3, 2, 4.0, 3, 3]], dtype=xp.float64)
|
|
Z = Z[:nrow, :ncol]
|
|
xp_assert_equal(is_valid_linkage(Z), valid, check_namespace=False)
|
|
if not valid:
|
|
assert_raises(ValueError, is_valid_linkage, Z, throw=True)
|
|
|
|
def test_is_valid_linkage_int_type(self, xp):
|
|
# Tests is_valid_linkage(Z) with integer type.
|
|
Z = xp.asarray([[0, 1, 3.0, 2],
|
|
[3, 2, 4.0, 3]], dtype=xp.int64)
|
|
xp_assert_equal(is_valid_linkage(Z), False, check_namespace=False)
|
|
assert_raises(TypeError, is_valid_linkage, Z, throw=True)
|
|
|
|
def test_is_valid_linkage_empty(self, xp):
|
|
# Tests is_valid_linkage(Z) with empty linkage.
|
|
Z = xp.zeros((0, 4), dtype=xp.float64)
|
|
xp_assert_equal(is_valid_linkage(Z), False, check_namespace=False)
|
|
assert_raises(ValueError, is_valid_linkage, Z, throw=True)
|
|
|
|
def test_is_valid_linkage_4_and_up(self, xp):
|
|
# Tests is_valid_linkage(Z) on linkage on observation sets between
|
|
# sizes 4 and 15 (step size 3).
|
|
for i in range(4, 15, 3):
|
|
y = np.random.rand(i*(i-1)//2)
|
|
Z = xp.asarray(linkage(y))
|
|
y = xp.asarray(y)
|
|
xp_assert_equal(is_valid_linkage(Z), True, check_namespace=False)
|
|
|
|
def test_is_valid_linkage_4_and_up_neg_index_left(self, xp):
|
|
# Tests is_valid_linkage(Z) on linkage on observation sets between
|
|
# sizes 4 and 15 (step size 3) with negative indices (left).
|
|
for i in range(4, 15, 3):
|
|
y = np.random.rand(i*(i-1)//2)
|
|
Z = xp.asarray(linkage(y))
|
|
y = xp.asarray(y)
|
|
Z = xpx.at(Z)[i//2, 0].set(-2)
|
|
xp_assert_equal(is_valid_linkage(Z), False, check_namespace=False)
|
|
with pytest.raises(ValueError):
|
|
eager.is_valid_linkage(Z, throw=True)
|
|
|
|
def test_is_valid_linkage_4_and_up_neg_index_right(self, xp):
|
|
# Tests is_valid_linkage(Z) on linkage on observation sets between
|
|
# sizes 4 and 15 (step size 3) with negative indices (right).
|
|
for i in range(4, 15, 3):
|
|
y = np.random.rand(i*(i-1)//2)
|
|
Z = xp.asarray(linkage(y))
|
|
y = xp.asarray(y)
|
|
Z = xpx.at(Z)[i//2, 1].set(-2)
|
|
xp_assert_equal(is_valid_linkage(Z), False, check_namespace=False)
|
|
with pytest.raises(ValueError):
|
|
eager.is_valid_linkage(Z, throw=True)
|
|
|
|
def test_is_valid_linkage_4_and_up_neg_dist(self, xp):
|
|
# Tests is_valid_linkage(Z) on linkage on observation sets between
|
|
# sizes 4 and 15 (step size 3) with negative distances.
|
|
for i in range(4, 15, 3):
|
|
y = np.random.rand(i*(i-1)//2)
|
|
Z = xp.asarray(linkage(y))
|
|
y = xp.asarray(y)
|
|
Z = xpx.at(Z)[i//2, 2].set(-0.5)
|
|
xp_assert_equal(is_valid_linkage(Z), False, check_namespace=False)
|
|
with pytest.raises(ValueError):
|
|
eager.is_valid_linkage(Z, throw=True)
|
|
|
|
def test_is_valid_linkage_4_and_up_neg_counts(self, xp):
|
|
# Tests is_valid_linkage(Z) on linkage on observation sets between
|
|
# sizes 4 and 15 (step size 3) with negative counts.
|
|
for i in range(4, 15, 3):
|
|
y = np.random.rand(i*(i-1)//2)
|
|
Z = xp.asarray(linkage(y))
|
|
y = xp.asarray(y)
|
|
Z = xpx.at(Z)[i//2, 3].set(-2)
|
|
xp_assert_equal(is_valid_linkage(Z), False, check_namespace=False)
|
|
with pytest.raises(ValueError):
|
|
eager.is_valid_linkage(Z, throw=True)
|
|
|
|
|
|
@make_xp_test_case(is_valid_im)
|
|
class TestIsValidInconsistent:
|
|
|
|
def test_is_valid_im_int_type(self, xp):
|
|
# Tests is_valid_im(R) with integer type.
|
|
R = xp.asarray([[0, 1, 3.0, 2],
|
|
[3, 2, 4.0, 3]], dtype=xp.int64)
|
|
xp_assert_equal(is_valid_im(R), False, check_namespace=False)
|
|
assert_raises(TypeError, is_valid_im, R, throw=True)
|
|
|
|
@pytest.mark.parametrize("nrow, ncol, valid", [(2, 5, False), (2, 3, False),
|
|
(1, 4, True), (2, 4, True)])
|
|
def test_is_valid_im_various_size(self, nrow, ncol, valid, xp):
|
|
# Tests is_valid_im(R) with linkage matrices of various sizes
|
|
R = xp.asarray([[0, 1, 3.0, 2, 5],
|
|
[3, 2, 4.0, 3, 3]], dtype=xp.float64)
|
|
R = R[:nrow, :ncol]
|
|
xp_assert_equal(is_valid_im(R), valid, check_namespace=False)
|
|
if not valid:
|
|
assert_raises(ValueError, is_valid_im, R, throw=True)
|
|
|
|
def test_is_valid_im_empty(self, xp):
|
|
# Tests is_valid_im(R) with empty inconsistency matrix.
|
|
R = xp.zeros((0, 4), dtype=xp.float64)
|
|
xp_assert_equal(is_valid_im(R), False, check_namespace=False)
|
|
assert_raises(ValueError, is_valid_im, R, throw=True)
|
|
|
|
def test_is_valid_im_4_and_up(self, xp):
|
|
# Tests is_valid_im(R) on im on observation sets between sizes 4 and 15
|
|
# (step size 3).
|
|
for i in range(4, 15, 3):
|
|
y = np.random.rand(i*(i-1)//2)
|
|
Z = linkage(y)
|
|
R = inconsistent(Z)
|
|
R = xp.asarray(R)
|
|
xp_assert_equal(is_valid_im(R), True, check_namespace=False)
|
|
|
|
def test_is_valid_im_4_and_up_neg_index_left(self, xp):
|
|
# Tests is_valid_im(R) on im on observation sets between sizes 4 and 15
|
|
# (step size 3) with negative link height means.
|
|
for i in range(4, 15, 3):
|
|
y = np.random.rand(i*(i-1)//2)
|
|
Z = linkage(y)
|
|
R = inconsistent(Z)
|
|
R = xpx.at(R)[i//2 , 0].set(-2.0)
|
|
R = xp.asarray(R)
|
|
xp_assert_equal(is_valid_im(R), False, check_namespace=False)
|
|
with pytest.raises(ValueError):
|
|
eager.is_valid_im(R, throw=True)
|
|
|
|
def test_is_valid_im_4_and_up_neg_index_right(self, xp):
|
|
# Tests is_valid_im(R) on im on observation sets between sizes 4 and 15
|
|
# (step size 3) with negative link height standard deviations.
|
|
for i in range(4, 15, 3):
|
|
y = np.random.rand(i*(i-1)//2)
|
|
Z = linkage(y)
|
|
R = inconsistent(Z)
|
|
R = xpx.at(R)[i//2 , 1].set(-2.0)
|
|
R = xp.asarray(R)
|
|
xp_assert_equal(is_valid_im(R), False, check_namespace=False)
|
|
with pytest.raises(ValueError):
|
|
eager.is_valid_im(R, throw=True)
|
|
|
|
def test_is_valid_im_4_and_up_neg_dist(self, xp):
|
|
# Tests is_valid_im(R) on im on observation sets between sizes 4 and 15
|
|
# (step size 3) with negative link counts.
|
|
for i in range(4, 15, 3):
|
|
y = np.random.rand(i*(i-1)//2)
|
|
Z = linkage(y)
|
|
R = inconsistent(Z)
|
|
R = xpx.at(R)[i//2, 2].set(-0.5)
|
|
R = xp.asarray(R)
|
|
xp_assert_equal(is_valid_im(R), False, check_namespace=False)
|
|
with pytest.raises(ValueError):
|
|
eager.is_valid_im(R, throw=True)
|
|
|
|
|
|
class TestNumObsLinkage:
|
|
|
|
def test_num_obs_linkage_empty(self, xp):
|
|
# Tests num_obs_linkage(Z) with empty linkage.
|
|
Z = xp.zeros((0, 4), dtype=xp.float64)
|
|
assert_raises(ValueError, num_obs_linkage, Z)
|
|
|
|
def test_num_obs_linkage_1x4(self, xp):
|
|
# Tests num_obs_linkage(Z) on linkage over 2 observations.
|
|
Z = xp.asarray([[0, 1, 3.0, 2]], dtype=xp.float64)
|
|
assert num_obs_linkage(Z) == 2
|
|
|
|
def test_num_obs_linkage_2x4(self, xp):
|
|
# Tests num_obs_linkage(Z) on linkage over 3 observations.
|
|
Z = xp.asarray([[0, 1, 3.0, 2],
|
|
[3, 2, 4.0, 3]], dtype=xp.float64)
|
|
assert num_obs_linkage(Z) == 3
|
|
|
|
def test_num_obs_linkage_4_and_up(self, xp):
|
|
# Tests num_obs_linkage(Z) on linkage on observation sets between sizes
|
|
# 4 and 15 (step size 3).
|
|
for i in range(4, 15, 3):
|
|
y = np.random.rand(i*(i-1)//2)
|
|
Z = xp.asarray(linkage(y))
|
|
assert num_obs_linkage(Z) == i
|
|
|
|
def test_num_obs_linkage_multi_matrix(self, xp):
|
|
# Tests num_obs_linkage with observation matrices of multiple sizes.
|
|
for n in range(2, 10):
|
|
X = np.random.rand(n, 4)
|
|
Y = pdist(X)
|
|
Z = xp.asarray(linkage(Y))
|
|
assert num_obs_linkage(Z) == n
|
|
|
|
|
|
@make_xp_test_case(leaves_list, to_tree)
|
|
class TestLeavesList:
|
|
|
|
def test_leaves_list_1x4(self, xp):
|
|
# Tests leaves_list(Z) on a 1x4 linkage.
|
|
Z = xp.asarray([[0, 1, 3.0, 2]], dtype=xp.float64)
|
|
to_tree(Z)
|
|
assert_allclose(leaves_list(Z), [0, 1], rtol=1e-15)
|
|
|
|
def test_leaves_list_2x4(self, xp):
|
|
# Tests leaves_list(Z) on a 2x4 linkage.
|
|
Z = xp.asarray([[0, 1, 3.0, 2],
|
|
[3, 2, 4.0, 3]], dtype=xp.float64)
|
|
to_tree(Z)
|
|
assert_allclose(leaves_list(Z), [0, 1, 2], rtol=1e-15)
|
|
|
|
@pytest.mark.parametrize("method",
|
|
['single', 'complete', 'average', 'weighted', 'centroid', 'median', 'ward'])
|
|
def test_leaves_list_Q(self, method, xp):
|
|
# Tests leaves_list(Z) on the Q data set
|
|
X = hierarchy_test_data.Q_X
|
|
Z = xp.asarray(linkage(X, method))
|
|
node = to_tree(Z)
|
|
assert_allclose(node.pre_order(), leaves_list(Z), rtol=1e-15)
|
|
|
|
def test_Q_subtree_pre_order(self, xp):
|
|
# Tests that pre_order() works when called on sub-trees.
|
|
X = hierarchy_test_data.Q_X
|
|
Z = xp.asarray(linkage(X, 'single'))
|
|
node = to_tree(Z)
|
|
assert_allclose(node.pre_order(),
|
|
(node.get_left().pre_order() + node.get_right().pre_order()),
|
|
rtol=1e-15)
|
|
|
|
|
|
@make_xp_test_case(correspond)
|
|
class TestCorrespond:
|
|
|
|
def test_correspond_empty(self, xp):
|
|
# Tests correspond(Z, y) with empty linkage and condensed distance matrix.
|
|
y = xp.zeros((0,), dtype=xp.float64)
|
|
Z = xp.zeros((0,4), dtype=xp.float64)
|
|
assert_raises(ValueError, correspond, Z, y)
|
|
|
|
def test_correspond_2_and_up(self, xp):
|
|
# Tests correspond(Z, y) on linkage and CDMs over observation sets of
|
|
# different sizes.
|
|
for i in range(2, 4):
|
|
y = np.random.rand(i*(i-1)//2)
|
|
Z = xp.asarray(linkage(y))
|
|
y = xp.asarray(y)
|
|
assert_(correspond(Z, y))
|
|
for i in range(4, 15, 3):
|
|
y = np.random.rand(i*(i-1)//2)
|
|
Z = xp.asarray(linkage(y))
|
|
y = xp.asarray(y)
|
|
assert_(correspond(Z, y))
|
|
|
|
def test_correspond_4_and_up(self, xp):
|
|
# Tests correspond(Z, y) on linkage and CDMs over observation sets of
|
|
# different sizes. Correspondence should be false.
|
|
for (i, j) in (list(zip(list(range(2, 4)), list(range(3, 5)))) +
|
|
list(zip(list(range(3, 5)), list(range(2, 4))))):
|
|
y = np.random.rand(i*(i-1)//2)
|
|
y2 = np.random.rand(j*(j-1)//2)
|
|
Z = xp.asarray(linkage(y))
|
|
Z2 = xp.asarray(linkage(y2))
|
|
y = xp.asarray(y)
|
|
y2 = xp.asarray(y2)
|
|
assert not correspond(Z, y2)
|
|
assert not correspond(Z2, y)
|
|
|
|
def test_correspond_4_and_up_2(self, xp):
|
|
# Tests correspond(Z, y) on linkage and CDMs over observation sets of
|
|
# different sizes. Correspondence should be false.
|
|
for (i, j) in (list(zip(list(range(2, 7)), list(range(16, 21)))) +
|
|
list(zip(list(range(2, 7)), list(range(16, 21))))):
|
|
y = np.random.rand(i*(i-1)//2)
|
|
y2 = np.random.rand(j*(j-1)//2)
|
|
Z = xp.asarray(linkage(y))
|
|
Z2 = xp.asarray(linkage(y2))
|
|
y = xp.asarray(y)
|
|
y2 = xp.asarray(y2)
|
|
assert not correspond(Z, y2)
|
|
assert not correspond(Z2, y)
|
|
|
|
|
|
@make_xp_test_case(is_monotonic)
|
|
class TestIsMonotonic:
|
|
|
|
def test_is_monotonic_empty(self, xp):
|
|
# Tests is_monotonic(Z) on an empty linkage.
|
|
Z = xp.zeros((0, 4), dtype=xp.float64)
|
|
assert_raises(ValueError, is_monotonic, Z)
|
|
|
|
def test_is_monotonic_1x4(self, xp):
|
|
# Tests is_monotonic(Z) on 1x4 linkage. Expecting True.
|
|
Z = xp.asarray([[0, 1, 0.3, 2]], dtype=xp.float64)
|
|
assert is_monotonic(Z)
|
|
|
|
def test_is_monotonic_2x4_T(self, xp):
|
|
# Tests is_monotonic(Z) on 2x4 linkage. Expecting True.
|
|
Z = xp.asarray([[0, 1, 0.3, 2],
|
|
[2, 3, 0.4, 3]], dtype=xp.float64)
|
|
assert is_monotonic(Z)
|
|
|
|
def test_is_monotonic_2x4_F(self, xp):
|
|
# Tests is_monotonic(Z) on 2x4 linkage. Expecting False.
|
|
Z = xp.asarray([[0, 1, 0.4, 2],
|
|
[2, 3, 0.3, 3]], dtype=xp.float64)
|
|
assert not is_monotonic(Z)
|
|
|
|
def test_is_monotonic_3x4_T(self, xp):
|
|
# Tests is_monotonic(Z) on 3x4 linkage. Expecting True.
|
|
Z = xp.asarray([[0, 1, 0.3, 2],
|
|
[2, 3, 0.4, 2],
|
|
[4, 5, 0.6, 4]], dtype=xp.float64)
|
|
assert is_monotonic(Z)
|
|
|
|
def test_is_monotonic_3x4_F1(self, xp):
|
|
# Tests is_monotonic(Z) on 3x4 linkage (case 1). Expecting False.
|
|
Z = xp.asarray([[0, 1, 0.3, 2],
|
|
[2, 3, 0.2, 2],
|
|
[4, 5, 0.6, 4]], dtype=xp.float64)
|
|
assert not is_monotonic(Z)
|
|
|
|
def test_is_monotonic_3x4_F2(self, xp):
|
|
# Tests is_monotonic(Z) on 3x4 linkage (case 2). Expecting False.
|
|
Z = xp.asarray([[0, 1, 0.8, 2],
|
|
[2, 3, 0.4, 2],
|
|
[4, 5, 0.6, 4]], dtype=xp.float64)
|
|
assert not is_monotonic(Z)
|
|
|
|
def test_is_monotonic_3x4_F3(self, xp):
|
|
# Tests is_monotonic(Z) on 3x4 linkage (case 3). Expecting False
|
|
Z = xp.asarray([[0, 1, 0.3, 2],
|
|
[2, 3, 0.4, 2],
|
|
[4, 5, 0.2, 4]], dtype=xp.float64)
|
|
assert not is_monotonic(Z)
|
|
|
|
def test_is_monotonic_tdist_linkage1(self, xp):
|
|
# Tests is_monotonic(Z) on clustering generated by single linkage on
|
|
# tdist data set. Expecting True.
|
|
Z = xp.asarray(linkage(hierarchy_test_data.ytdist, 'single'))
|
|
assert is_monotonic(Z)
|
|
|
|
def test_is_monotonic_tdist_linkage2(self, xp):
|
|
# Tests is_monotonic(Z) on clustering generated by single linkage on
|
|
# tdist data set. Perturbing. Expecting False.
|
|
Z = xp.asarray(linkage(hierarchy_test_data.ytdist, 'single'))
|
|
Z = xpx.at(Z)[2, 2].set(0.0)
|
|
assert not is_monotonic(Z)
|
|
|
|
def test_is_monotonic_Q_linkage(self, xp):
|
|
# Tests is_monotonic(Z) on clustering generated by single linkage on
|
|
# Q data set. Expecting True.
|
|
X = hierarchy_test_data.Q_X
|
|
Z = xp.asarray(linkage(X, 'single'))
|
|
assert is_monotonic(Z)
|
|
|
|
|
|
@make_xp_test_case(maxdists)
|
|
class TestMaxDists:
|
|
|
|
def test_maxdists_empty_linkage(self, xp):
|
|
# Tests maxdists(Z) on empty linkage. Expecting exception.
|
|
Z = xp.zeros((0, 4), dtype=xp.float64)
|
|
assert_raises(ValueError, maxdists, Z)
|
|
|
|
def test_maxdists_one_cluster_linkage(self, xp):
|
|
# Tests maxdists(Z) on linkage with one cluster.
|
|
Z = xp.asarray([[0, 1, 0.3, 4]], dtype=xp.float64)
|
|
MD = maxdists(Z)
|
|
expectedMD = calculate_maximum_distances(Z, xp)
|
|
xp_assert_close(MD, expectedMD, atol=1e-15)
|
|
|
|
@pytest.mark.parametrize(
|
|
"method", ['single', 'complete', 'ward', 'centroid', 'median'])
|
|
def test_maxdists_Q_linkage(self, method, xp):
|
|
# Tests maxdists(Z) on the Q data set
|
|
X = hierarchy_test_data.Q_X
|
|
Z = xp.asarray(linkage(X, method))
|
|
MD = maxdists(Z)
|
|
expectedMD = calculate_maximum_distances(Z, xp)
|
|
xp_assert_close(MD, expectedMD, atol=1e-15)
|
|
|
|
|
|
@make_xp_test_case(maxinconsts)
|
|
class TestMaxInconsts:
|
|
|
|
def test_maxinconsts_empty_linkage(self, xp):
|
|
# Tests maxinconsts(Z, R) on empty linkage. Expecting exception.
|
|
Z = xp.zeros((0, 4), dtype=xp.float64)
|
|
R = xp.zeros((0, 4), dtype=xp.float64)
|
|
assert_raises(ValueError, maxinconsts, Z, R)
|
|
|
|
def test_maxinconsts_difrow_linkage(self, xp):
|
|
# Tests maxinconsts(Z, R) on linkage and inconsistency matrices with
|
|
# different numbers of clusters. Expecting exception.
|
|
Z = xp.asarray([[0, 1, 0.3, 4]], dtype=xp.float64)
|
|
R = np.random.rand(2, 4)
|
|
R = xp.asarray(R)
|
|
assert_raises(ValueError, maxinconsts, Z, R)
|
|
|
|
def test_maxinconsts_one_cluster_linkage(self, xp):
|
|
# Tests maxinconsts(Z, R) on linkage with one cluster.
|
|
Z = xp.asarray([[0, 1, 0.3, 4]], dtype=xp.float64)
|
|
R = xp.asarray([[0, 0, 0, 0.3]], dtype=xp.float64)
|
|
MD = maxinconsts(Z, R)
|
|
expectedMD = calculate_maximum_inconsistencies(Z, R, xp=xp)
|
|
xp_assert_close(MD, expectedMD, atol=1e-15)
|
|
|
|
@pytest.mark.parametrize(
|
|
"method", ['single', 'complete', 'ward', 'centroid', 'median'])
|
|
def test_maxinconsts_Q_linkage(self, method, xp):
|
|
# Tests maxinconsts(Z, R) on the Q data set
|
|
X = hierarchy_test_data.Q_X
|
|
Z = linkage(X, method)
|
|
R = xp.asarray(inconsistent(Z))
|
|
Z = xp.asarray(Z)
|
|
MD = maxinconsts(Z, R)
|
|
expectedMD = calculate_maximum_inconsistencies(Z, R, xp=xp)
|
|
xp_assert_close(MD, expectedMD, atol=1e-15)
|
|
|
|
|
|
@make_xp_test_case(maxRstat)
|
|
class TestMaxRStat:
|
|
|
|
def test_maxRstat_invalid_index(self, xp):
|
|
# Tests maxRstat(Z, R, i). Expecting exception.
|
|
Z = xp.asarray([[0, 1, 0.3, 4]], dtype=xp.float64)
|
|
R = xp.asarray([[0, 0, 0, 0.3]], dtype=xp.float64)
|
|
with pytest.raises(TypeError):
|
|
maxRstat(Z, R, 3.3)
|
|
with pytest.raises(ValueError):
|
|
maxRstat(Z, R, -1)
|
|
with pytest.raises(ValueError):
|
|
maxRstat(Z, R, 4)
|
|
|
|
@pytest.mark.parametrize("i", range(4))
|
|
def test_maxRstat_empty_linkage(self, i, xp):
|
|
# Tests maxRstat(Z, R, i) on empty linkage. Expecting exception.
|
|
Z = xp.zeros((0, 4), dtype=xp.float64)
|
|
R = xp.zeros((0, 4), dtype=xp.float64)
|
|
assert_raises(ValueError, maxRstat, Z, R, i)
|
|
|
|
@pytest.mark.parametrize("i", range(4))
|
|
def test_maxRstat_difrow_linkage(self, i, xp):
|
|
# Tests maxRstat(Z, R, i) on linkage and inconsistency matrices with
|
|
# different numbers of clusters. Expecting exception.
|
|
Z = xp.asarray([[0, 1, 0.3, 4]], dtype=xp.float64)
|
|
R = np.random.rand(2, 4)
|
|
R = xp.asarray(R)
|
|
assert_raises(ValueError, maxRstat, Z, R, i)
|
|
|
|
def test_maxRstat_one_cluster_linkage(self, xp):
|
|
# Tests maxRstat(Z, R, i) on linkage with one cluster.
|
|
Z = xp.asarray([[0, 1, 0.3, 4]], dtype=xp.float64)
|
|
R = xp.asarray([[0, 0, 0, 0.3]], dtype=xp.float64)
|
|
MD = maxRstat(Z, R, 1)
|
|
expectedMD = calculate_maximum_inconsistencies(Z, R, 1, xp)
|
|
xp_assert_close(MD, expectedMD, atol=1e-15)
|
|
|
|
@pytest.mark.parametrize(
|
|
"method", ['single', 'complete', 'ward', 'centroid', 'median'])
|
|
def test_maxRstat_Q_linkage(self, method, xp):
|
|
# Tests maxRstat(Z, R, 1) on the Q data set
|
|
X = hierarchy_test_data.Q_X
|
|
Z = linkage(X, method)
|
|
R = xp.asarray(inconsistent(Z))
|
|
Z = xp.asarray(Z)
|
|
MD = maxRstat(Z, R, 1)
|
|
expectedMD = calculate_maximum_inconsistencies(Z, R, 1, xp)
|
|
xp_assert_close(MD, expectedMD, atol=1e-15)
|
|
|
|
|
|
@make_xp_test_case(dendrogram)
|
|
class TestDendrogram:
|
|
|
|
def test_dendrogram_single_linkage_tdist(self, xp):
|
|
# Tests dendrogram calculation on single linkage of the tdist data set.
|
|
Z = xp.asarray(linkage(hierarchy_test_data.ytdist, 'single'))
|
|
R = dendrogram(Z, no_plot=True)
|
|
leaves = R["leaves"]
|
|
assert_equal(leaves, [2, 5, 1, 0, 3, 4])
|
|
|
|
def test_valid_orientation(self, xp):
|
|
Z = xp.asarray(linkage(hierarchy_test_data.ytdist, 'single'))
|
|
assert_raises(ValueError, dendrogram, Z, orientation="foo")
|
|
|
|
def test_labels_as_array_or_list(self, xp):
|
|
# test for gh-12418
|
|
Z = xp.asarray(linkage(hierarchy_test_data.ytdist, 'single'))
|
|
labels = [1, 3, 2, 6, 4, 5]
|
|
result1 = dendrogram(Z, labels=xp.asarray(labels), no_plot=True)
|
|
result2 = dendrogram(Z, labels=labels, no_plot=True)
|
|
assert result1 == result2
|
|
|
|
@pytest.mark.skipif(not have_matplotlib, reason="no matplotlib")
|
|
def test_valid_label_size(self, xp):
|
|
link = xp.asarray([
|
|
[0, 1, 1.0, 4],
|
|
[2, 3, 1.0, 5],
|
|
[4, 5, 2.0, 6],
|
|
])
|
|
plt.figure()
|
|
with pytest.raises(ValueError) as exc_info:
|
|
dendrogram(link, labels=list(range(100)))
|
|
assert "Dimensions of Z and labels must be consistent."\
|
|
in str(exc_info.value)
|
|
|
|
with pytest.raises(
|
|
ValueError,
|
|
match="Dimensions of Z and labels must be consistent."):
|
|
dendrogram(link, labels=[])
|
|
|
|
plt.close()
|
|
|
|
@skip_xp_backends('torch',
|
|
reason='MPL 3.9.2 & torch DeprecationWarning from __array_wrap__'
|
|
' and NumPy 2.0'
|
|
)
|
|
@skip_xp_backends('dask.array',
|
|
reason='dask.array has bad interaction with matplotlib'
|
|
)
|
|
@pytest.mark.skipif(not have_matplotlib, reason="no matplotlib")
|
|
@pytest.mark.parametrize("orientation", ['top', 'bottom', 'left', 'right'])
|
|
def test_dendrogram_plot(self, orientation, xp):
|
|
# Tests dendrogram plotting.
|
|
Z = xp.asarray(linkage(hierarchy_test_data.ytdist, 'single'))
|
|
expected = {'color_list': ['C1', 'C0', 'C0', 'C0', 'C0'],
|
|
'dcoord': [[0.0, 138.0, 138.0, 0.0],
|
|
[0.0, 219.0, 219.0, 0.0],
|
|
[0.0, 255.0, 255.0, 219.0],
|
|
[0.0, 268.0, 268.0, 255.0],
|
|
[138.0, 295.0, 295.0, 268.0]],
|
|
'icoord': [[5.0, 5.0, 15.0, 15.0],
|
|
[45.0, 45.0, 55.0, 55.0],
|
|
[35.0, 35.0, 50.0, 50.0],
|
|
[25.0, 25.0, 42.5, 42.5],
|
|
[10.0, 10.0, 33.75, 33.75]],
|
|
'ivl': ['2', '5', '1', '0', '3', '4'],
|
|
'leaves': [2, 5, 1, 0, 3, 4],
|
|
'leaves_color_list': ['C1', 'C1', 'C0', 'C0', 'C0', 'C0'],
|
|
}
|
|
|
|
fig = plt.figure()
|
|
ax = fig.add_subplot(221)
|
|
|
|
# test that dendrogram accepts ax keyword
|
|
R1 = dendrogram(Z, ax=ax, orientation=orientation)
|
|
R1['dcoord'] = np.asarray(R1['dcoord'])
|
|
assert_equal(R1, expected)
|
|
|
|
# test that dendrogram accepts and handle the leaf_font_size and
|
|
# leaf_rotation keywords
|
|
dendrogram(Z, ax=ax, orientation=orientation,
|
|
leaf_font_size=20, leaf_rotation=90)
|
|
testlabel = (
|
|
ax.get_xticklabels()[0]
|
|
if orientation in ['top', 'bottom']
|
|
else ax.get_yticklabels()[0]
|
|
)
|
|
assert_equal(testlabel.get_rotation(), 90)
|
|
assert_equal(testlabel.get_size(), 20)
|
|
dendrogram(Z, ax=ax, orientation=orientation,
|
|
leaf_rotation=90)
|
|
testlabel = (
|
|
ax.get_xticklabels()[0]
|
|
if orientation in ['top', 'bottom']
|
|
else ax.get_yticklabels()[0]
|
|
)
|
|
assert_equal(testlabel.get_rotation(), 90)
|
|
dendrogram(Z, ax=ax, orientation=orientation,
|
|
leaf_font_size=20)
|
|
testlabel = (
|
|
ax.get_xticklabels()[0]
|
|
if orientation in ['top', 'bottom']
|
|
else ax.get_yticklabels()[0]
|
|
)
|
|
assert_equal(testlabel.get_size(), 20)
|
|
plt.close()
|
|
|
|
# test plotting to gca (will import pylab)
|
|
R2 = dendrogram(Z, orientation=orientation)
|
|
plt.close()
|
|
R2['dcoord'] = np.asarray(R2['dcoord'])
|
|
assert_equal(R2, expected)
|
|
|
|
@skip_xp_backends('torch',
|
|
reason='MPL 3.9.2 & torch DeprecationWarning from __array_wrap__'
|
|
' and NumPy 2.0'
|
|
)
|
|
@skip_xp_backends('dask.array',
|
|
reason='dask.array has bad interaction with matplotlib'
|
|
)
|
|
@pytest.mark.skipif(not have_matplotlib, reason="no matplotlib")
|
|
def test_dendrogram_truncate_mode(self, xp):
|
|
Z = xp.asarray(linkage(hierarchy_test_data.ytdist, 'single'))
|
|
|
|
R = dendrogram(Z, 2, 'lastp', show_contracted=True)
|
|
plt.close()
|
|
R['dcoord'] = np.asarray(R['dcoord'])
|
|
assert_equal(R, {'color_list': ['C0'],
|
|
'dcoord': [[0.0, 295.0, 295.0, 0.0]],
|
|
'icoord': [[5.0, 5.0, 15.0, 15.0]],
|
|
'ivl': ['(2)', '(4)'],
|
|
'leaves': [6, 9],
|
|
'leaves_color_list': ['C0', 'C0'],
|
|
})
|
|
|
|
R = dendrogram(Z, 2, 'mtica', show_contracted=True)
|
|
plt.close()
|
|
R['dcoord'] = np.asarray(R['dcoord'])
|
|
assert_equal(R, {'color_list': ['C1', 'C0', 'C0', 'C0'],
|
|
'dcoord': [[0.0, 138.0, 138.0, 0.0],
|
|
[0.0, 255.0, 255.0, 0.0],
|
|
[0.0, 268.0, 268.0, 255.0],
|
|
[138.0, 295.0, 295.0, 268.0]],
|
|
'icoord': [[5.0, 5.0, 15.0, 15.0],
|
|
[35.0, 35.0, 45.0, 45.0],
|
|
[25.0, 25.0, 40.0, 40.0],
|
|
[10.0, 10.0, 32.5, 32.5]],
|
|
'ivl': ['2', '5', '1', '0', '(2)'],
|
|
'leaves': [2, 5, 1, 0, 7],
|
|
'leaves_color_list': ['C1', 'C1', 'C0', 'C0', 'C0'],
|
|
})
|
|
|
|
@pytest.fixture
|
|
def dendrogram_lock(self):
|
|
return Lock()
|
|
|
|
def test_dendrogram_colors(self, xp, dendrogram_lock):
|
|
# Tests dendrogram plots with alternate colors
|
|
Z = xp.asarray(linkage(hierarchy_test_data.ytdist, 'single'))
|
|
|
|
with dendrogram_lock:
|
|
# Global color palette might be changed concurrently
|
|
set_link_color_palette(['c', 'm', 'y', 'k'])
|
|
R = dendrogram(Z, no_plot=True,
|
|
above_threshold_color='g', color_threshold=250)
|
|
set_link_color_palette(['g', 'r', 'c', 'm', 'y', 'k'])
|
|
|
|
color_list = R['color_list']
|
|
assert_equal(color_list, ['c', 'm', 'g', 'g', 'g'])
|
|
|
|
# reset color palette (global list)
|
|
set_link_color_palette(None)
|
|
|
|
def test_dendrogram_leaf_colors_zero_dist(self, xp):
|
|
# tests that the colors of leafs are correct for tree
|
|
# with two identical points
|
|
X = np.asarray([[1, 0, 0],
|
|
[0, 0, 1],
|
|
[0, 2, 0],
|
|
[0, 0, 1],
|
|
[0, 1, 0],
|
|
[0, 1, 0]])
|
|
Z = xp.asarray(linkage(X, "single"))
|
|
d = dendrogram(Z, no_plot=True)
|
|
exp_colors = ['C0', 'C1', 'C1', 'C0', 'C2', 'C2']
|
|
colors = d["leaves_color_list"]
|
|
assert_equal(colors, exp_colors)
|
|
|
|
def test_dendrogram_leaf_colors(self, xp):
|
|
# tests that the colors are correct for a tree
|
|
# with two near points ((0, 0, 1.1) and (0, 0, 1))
|
|
X = np.asarray([[1, 0, 0],
|
|
[0, 0, 1.1],
|
|
[0, 2, 0],
|
|
[0, 0, 1],
|
|
[0, 1, 0],
|
|
[0, 1, 0]])
|
|
Z = xp.asarray(linkage(X, "single"))
|
|
d = dendrogram(Z, no_plot=True)
|
|
exp_colors = ['C0', 'C1', 'C1', 'C0', 'C2', 'C2']
|
|
colors = d["leaves_color_list"]
|
|
assert_equal(colors, exp_colors)
|
|
|
|
|
|
def calculate_maximum_distances(Z, xp):
|
|
# Used for testing correctness of maxdists.
|
|
n = Z.shape[0] + 1
|
|
B = xp.zeros((n-1,), dtype=Z.dtype)
|
|
for i in range(0, n - 1):
|
|
q = xp.zeros((3,))
|
|
left = Z[i, 0]
|
|
right = Z[i, 1]
|
|
if left >= n:
|
|
b_left = B[xp.asarray(left, dtype=xp.int64) - n]
|
|
q = xpx.at(q, 0).set(b_left)
|
|
if right >= n:
|
|
b_right = B[xp.asarray(right, dtype=xp.int64) - n]
|
|
q = xpx.at(q, 1).set(b_right)
|
|
q = xpx.at(q, 2).set(Z[i, 2])
|
|
B = xpx.at(B, i).set(xp.max(q))
|
|
return B
|
|
|
|
|
|
def calculate_maximum_inconsistencies(Z, R, k=3, xp=np):
|
|
# Used for testing correctness of maxinconsts.
|
|
n = Z.shape[0] + 1
|
|
dtype = xp.result_type(Z, R)
|
|
B = xp.zeros((n-1,), dtype=dtype)
|
|
for i in range(0, n - 1):
|
|
q = xp.zeros((3,))
|
|
left = Z[i, 0]
|
|
right = Z[i, 1]
|
|
if left >= n:
|
|
b_left = B[xp.asarray(left, dtype=xp.int64) - n]
|
|
q = xpx.at(q, 0).set(b_left)
|
|
if right >= n:
|
|
b_right = B[xp.asarray(right, dtype=xp.int64) - n]
|
|
q = xpx.at(q, 1).set(b_right)
|
|
q = xpx.at(q, 2).set(R[i, k])
|
|
B = xpx.at(B, i).set(xp.max(q))
|
|
return B
|
|
|
|
|
|
@make_xp_test_case(to_tree)
|
|
def test_node_compare(xp):
|
|
np.random.seed(23)
|
|
nobs = 50
|
|
X = np.random.randn(nobs, 4)
|
|
Z = xp.asarray(ward(X))
|
|
tree = to_tree(Z)
|
|
assert_(tree > tree.get_left())
|
|
assert_(tree.get_right() > tree.get_left())
|
|
assert_(tree.get_right() == tree.get_right())
|
|
assert_(tree.get_right() != tree.get_left())
|
|
|
|
|
|
@make_xp_test_case(cut_tree)
|
|
def test_cut_tree(xp):
|
|
np.random.seed(23)
|
|
nobs = 50
|
|
X = np.random.randn(nobs, 4)
|
|
Z = xp.asarray(ward(X))
|
|
cutree = cut_tree(Z)
|
|
|
|
# cutree.dtype varies between int32 and int64 over platforms
|
|
xp_assert_close(cutree[:, 0], xp.arange(nobs), rtol=1e-15, check_dtype=False)
|
|
xp_assert_close(cutree[:, -1], xp.zeros(nobs), rtol=1e-15, check_dtype=False)
|
|
assert_equal(np.asarray(cutree).max(0), np.arange(nobs - 1, -1, -1))
|
|
|
|
xp_assert_close(cutree[:, [-5]], cut_tree(Z, n_clusters=5), rtol=1e-15)
|
|
xp_assert_close(cutree[:, [-5, -10]], cut_tree(Z, n_clusters=[5, 10]), rtol=1e-15)
|
|
xp_assert_close(cutree[:, [-10, -5]], cut_tree(Z, n_clusters=[10, 5]), rtol=1e-15)
|
|
|
|
nodes = _order_cluster_tree(Z)
|
|
heights = xp.asarray([node.dist for node in nodes])
|
|
|
|
xp_assert_close(cutree[:, np.searchsorted(heights, [5])],
|
|
cut_tree(Z, height=5), rtol=1e-15)
|
|
xp_assert_close(cutree[:, np.searchsorted(heights, [5, 10])],
|
|
cut_tree(Z, height=[5, 10]), rtol=1e-15)
|
|
xp_assert_close(cutree[:, np.searchsorted(heights, [10, 5])],
|
|
cut_tree(Z, height=[10, 5]), rtol=1e-15)
|
|
|
|
|
|
@make_xp_test_case(optimal_leaf_ordering)
|
|
def test_optimal_leaf_ordering(xp):
|
|
# test with the distance vector y
|
|
Z = optimal_leaf_ordering(xp.asarray(linkage(hierarchy_test_data.ytdist)),
|
|
xp.asarray(hierarchy_test_data.ytdist))
|
|
expectedZ = hierarchy_test_data.linkage_ytdist_single_olo
|
|
xp_assert_close(Z, xp.asarray(expectedZ), atol=1e-10)
|
|
|
|
# test with the observation matrix X
|
|
Z = optimal_leaf_ordering(xp.asarray(linkage(hierarchy_test_data.X, 'ward')),
|
|
xp.asarray(hierarchy_test_data.X))
|
|
expectedZ = hierarchy_test_data.linkage_X_ward_olo
|
|
xp_assert_close(Z, xp.asarray(expectedZ), atol=1e-06)
|
|
|
|
|
|
@skip_xp_backends(np_only=True, reason='`Heap` only supports NumPy backend')
|
|
def test_Heap(xp):
|
|
values = xp.asarray([2, -1, 0, -1.5, 3])
|
|
heap = Heap(values)
|
|
|
|
pair = heap.get_min()
|
|
assert_equal(pair['key'], 3)
|
|
assert_equal(pair['value'], -1.5)
|
|
|
|
heap.remove_min()
|
|
pair = heap.get_min()
|
|
assert_equal(pair['key'], 1)
|
|
assert_equal(pair['value'], -1)
|
|
|
|
heap.change_value(1, 2.5)
|
|
pair = heap.get_min()
|
|
assert_equal(pair['key'], 2)
|
|
assert_equal(pair['value'], 0)
|
|
|
|
heap.remove_min()
|
|
heap.remove_min()
|
|
|
|
heap.change_value(1, 10)
|
|
pair = heap.get_min()
|
|
assert_equal(pair['key'], 4)
|
|
assert_equal(pair['value'], 3)
|
|
|
|
heap.remove_min()
|
|
pair = heap.get_min()
|
|
assert_equal(pair['key'], 1)
|
|
assert_equal(pair['value'], 10)
|